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    <title>DefensePredictor: A machine learning model to discover prokaryotic immune systems | Science</title>
    <dc:date>2026-04-26T12:32:02+00:00</dc:date>
    <link>https://www.science.org/doi/10.1126/science.adv7924</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Bacteria have diverse immune systems that protect them from viral infection, yet the full extent of this diversity remains unknown. Two groups of researchers have now independently developed machine learning and deep learning models that leverage protein sequences and genomic context to predict antiphage defense systems at scale. DeWeirdt et al. developed a model called DefensePredictor and applied it to Escherichia coli, experimentally validating dozens of previously uncharacterized defense systems. Mordret et al. developed several different models and applied them to over 120 million proteins from bacterial genomes, identifying hundreds of thousands of candidate antiphage families, many lacking any prior annotation. Together, these studies reveal that bacterial immunity is far more extensive than previously thought and highlight how such discoveries can inspire powerful biotechnologies. —Di Jiang
]]></description>
<dc:subject>structural-biology machine-learning bioinformatics indistinguishable-from-magic learning-from-data to-understand</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d94701a9a214/</dc:identifier>
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<item rdf:about="https://pubmed.ncbi.nlm.nih.gov/40644298/">
    <title>A bacterial host factor confines phage localization for excluding the infected compartment through cell division - PubMed</title>
    <dc:date>2025-08-09T21:01:11+00:00</dc:date>
    <link>https://pubmed.ncbi.nlm.nih.gov/40644298/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Viruses frequently induce the formation of specialized subcellular compartments to facilitate their replication and assembly. Here, we describe a "host-derived" confinement mechanism, compartmentalizing bacteriophage (phage) production to enable phage caging through cell division. By employing the bacterium Bacillus subtilis and its lytic phages, we identified YjbH, highly conserved among gram-positive bacteria, as a host factor that limits plaque expansion. YjbH directly binds the penetrating phage genome via its helix-turn-helix DNA-binding domain and accumulates into a focus at the site of DNA injection. YjbH further constricts the synthesis of phage components, including DNA and capsid proteins, to a specific subcellular locale. Consequently, the division machinery is recruited to produce adjacent septations, often asymmetric, effectively trapping and excluding the infected compartment. This "exclude and survive" defense mechanism may represent a prevalent strategy employed by the host to contain viral spread.

]]></description>
<dc:subject>an-extra-billion-years-can't-hurt structural-biology biology evolutionary-biology physiology life-finds-a-way it's-a-prokaryotic-world-we-just-here-for-the-ride to-write-about bacteria-are-alien-but-from-here</dc:subject>
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<item rdf:about="https://www.nature.com/articles/s41586-024-08370-4">
    <title>Site-saturation mutagenesis of 500 human protein domains | Nature</title>
    <dc:date>2025-04-12T13:22:20+00:00</dc:date>
    <link>https://www.nature.com/articles/s41586-024-08370-4</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Missense variants that change the amino acid sequences of proteins cause one-third of human genetic diseases1. Tens of millions of missense variants exist in the current human population, and the vast majority of these have unknown functional consequences. Here we present a large-scale experimental analysis of human missense variants across many different proteins. Using DNA synthesis and cellular selection experiments we quantify the effect of more than 500,000 variants on the abundance of more than 500 human protein domains. This dataset reveals that 60% of pathogenic missense variants reduce protein stability. The contribution of stability to protein fitness varies across proteins and diseases and is particularly important in recessive disorders. We combine stability measurements with protein language models to annotate functional sites across proteins. Mutational effects on stability are largely conserved in homologous domains, enabling accurate stability prediction across entire protein families using energy models. Our data demonstrate the feasibility of assaying human protein variants at scale and provides a large consistent reference dataset for clinical variant interpretation and training and benchmarking of computational methods.

]]></description>
<dc:subject>structural-biology biochemistry molecular-biology impressive looking-to-see neural-networks protein-folding to-write-about evolutionary-biology variation-as-a-source-of-innovation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:604c7c235a9a/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
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<item rdf:about="https://arxiv.org/abs/2301.01929">
    <title>[2301.01929] Two-dimensional tile displacement can simulate cellular automata</title>
    <dc:date>2024-07-05T19:02:54+00:00</dc:date>
    <link>https://arxiv.org/abs/2301.01929</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Tile displacement is a newly-recognized mechanism in DNA nanotechnology that exploits principles analogous to toehold-mediated strand displacement but within the context of self-assembled DNA origami tile arrays. Here, we formulate an abstract model of tile displacement for the simplest case: individual assemblies interacting with monomer tiles in solution. We give several constructions for programmable computation by tile displacement, from circuits to cellular automata, that vary in how they use energy (or not) to drive the system forward (or not), how much space and how many tile types they require, and whether their computational power is limited to PTIME or PSPACE with respect to the size of the system. In particular, we show that tile displacement systems are Turing universal and can simulate arbitrary two-dimensional synchronous block cellular automata, where each transition rule for updating the state of a 2 by 2 neighborhood is implemented by just a single tile.
]]></description>
<dc:subject>nanotechnology DNA-computing indistinguishable-from-magic cellular-automata everything-looks-like-a-nail-made-of-DNA to-write-about nonlinear-dynamics structural-biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:48db3c7121ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:DNA-computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:indistinguishable-from-magic"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nonlinear-dynamics"/>
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<item rdf:about="https://arxiv.org/abs/1907.13371">
    <title>[1907.13371] Proteins: the physics of amorphous evolving matter</title>
    <dc:date>2022-03-10T17:11:41+00:00</dc:date>
    <link>https://arxiv.org/abs/1907.13371</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Proteins are a matter of dual nature. As a physical object, a protein molecule is a folded chain of amino acids with multifarious biochemistry. But it is also an instantiation along an evolutionary trajectory determined by the function performed by the protein within a hierarchy of interwoven interaction networks of the cell, the organism and the population. A physical theory of proteins therefore needs to unify both aspects, the biophysical and the evolutionary. Specifically, it should provide a model of how the DNA gene is mapped into the functional phenotype of the protein. 
We review several physical approaches to the protein problem, focusing on a mechanical framework which treats proteins as evolvable condensed matter: Mutations introduce localized perturbations in the gene, which are translated to localized perturbations in the protein matter. A natural tool to examine how mutations shape the phenotype are Green's functions. They map the evolutionary linkage among mutations in the gene (termed epistasis) to cooperative physical interactions among the amino acids in the protein. We discuss how the mechanistic view can be applied to examine basic questions of protein evolution and design.
]]></description>
<dc:subject>structural-biology review everything-is-physics-again complexology oversimplifications-as-a-route-to-papers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:df77874dd62d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
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<item rdf:about="https://arxiv.org/abs/1804.05003">
    <title>[1804.05003] Higher order molecular organisation as a source of biological function</title>
    <dc:date>2022-02-05T13:33:25+00:00</dc:date>
    <link>https://arxiv.org/abs/1804.05003</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Molecular interactions have widely been modelled as networks. The local wiring patterns around molecules in molecular networks are linked with their biological functions. However, networks model only pairwise interactions between molecules and cannot explicitly and directly capture the higher order molecular organisation, such as protein complexes and pathways. Hence, we ask if hypergraphs (hypernetworks), that directly capture entire complexes and pathways along with protein-protein interactions (PPIs), carry additional functional information beyond what can be uncovered from networks of pairwise molecular interactions. The mathematical formalism of a hypergraph has long been known, but not often used in studying molecular networks due to the lack of sophisticated algorithms for mining the underlying biological information hidden in the wiring patterns of molecular systems modelled as hypernetworks. 
We propose a new, multi-scale, protein interaction hypernetwork model that utilizes hypergraphs to capture different scales of protein organization, including PPIs, protein complexes and pathways. In analogy to graphlets, we introduce hypergraphlets, small, connected, non-isomorphic, induced sub-hypergraphs of a hypergraph, to quantify the local wiring patterns of these multi-scale molecular hypergraphs and to mine them for new biological information. We apply them to model the multi-scale protein networks of baker yeast and human and show that the higher order molecular organisation captured by these hypergraphs is strongly related to the underlying biology. Importantly, we demonstrate that our new models and data mining tools reveal different, but complementary biological information compared to classical PPI networks. We apply our hypergraphlets to successfully predict biological functions of uncharacterised proteins.
]]></description>
<dc:subject>structural-biology systems-biology rather-interesting hypergraphs interaction-graphs bioinformatics structure-function-relations consider:components-in-Push to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:06c5ffeda0b0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hypergraphs"/>
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</item>
<item rdf:about="https://www.quantamagazine.org/metamorphic-proteins-change-their-folds-for-different-jobs-20210203/">
    <title>Some Proteins Change Their Folds to Perform Different Jobs | Quanta Magazine</title>
    <dc:date>2022-01-20T16:29:55+00:00</dc:date>
    <link>https://www.quantamagazine.org/metamorphic-proteins-change-their-folds-for-different-jobs-20210203/</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[But why would metamorphosis be better than having two specialized proteins? The scientists theorize in their paper about a couple of linked possibilities. If a single protein can do double duty, it spares the cell from transcribing, translating and maintaining more than one gene. But the more compelling advantage may be that the protein’s ability to transform may give the body a more dynamic way to control its defenses against bacteria.

Because XCL1 can adopt its two folded forms with equal probability, it can switch between them faster than once per second. But changes in the temperature or salt concentration or the introduction of binding partners can change that equilibrium. For example, around microbial pathogens, more of the XCL1 proteins get stuck in the conformation that interacts with the microbial membranes, shifting the balance toward that fold. Elsewhere in the body, the protein can adopt the other fold more often and bind to the receptors on white blood cells to mobilize them.

]]></description>
<dc:subject>structural-biology protein-folding biological-engineering multitask-learning rather-interesting examples</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b97926810368/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
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</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/275008v1?rss=1">
    <title>Predicting improved protein conformations with a temporal deep recurrent neural network | bioRxiv</title>
    <dc:date>2021-06-04T11:19:21+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/275008v1?rss=1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from the MD trajectory data in order to classify whether each recorded simulation snapshot is an improved conformational state, decreased conformational state or a none perceivable change in state with respect to the starting conformation. The model is trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel.

]]></description>
<dc:subject>protein-folding structural-biology machine-learning recurrent-neural-network deep-learning rather-interesting dynamical-systems to-simulate to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:82ac88eb634f/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:deep-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.biorxiv.org/content/10.1101/320481v1?rss=1">
    <title>RNA polymerase organizes into distinct spatial clusters independent of ribosomal RNA transcription in E. coli | bioRxiv</title>
    <dc:date>2020-07-11T18:58:21+00:00</dc:date>
    <link>https://www.biorxiv.org/content/10.1101/320481v1?rss=1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Recent studies have shown that RNA polymerase (RNAP) is spatially organized into distinct clusters in E. coli and B. subtilis cells. Spatially organized molecular components in prokaryotic systems imply compartmentalization without the use of membranes, which may offer new insights into pertinent functions and regulations. However, the function of RNAP clusters and whether its formation is driven by active ribosomal RNA (rRNA) transcription remain elusive. In this work, we investigated the spatial organization of RNAP in E. coli cells using quantitative superresolution imaging. We observed that RNAP formed large, distinct clusters under a rich medium growth condition and preferentially located in the center of the nucleoid. Two-color superresolution colocalization imaging showed that under the rich medium growth condition, nearly all RNAP clusters were active in synthesizing rRNA, suggesting that rRNA synthesis may be spatially separated from mRNA synthesis that most likely occurs at the nucleoid periphery. Surprisingly, a large fraction of RNAP clusters persisted under conditions in which rRNA synthesis was reduced or abolished, or when only one out of the seven rRNA operons (rrn) remained. Furthermore, when gyrase activity was inhibited, we observed a similar rRNA synthesis level, but multiple dispersed, smaller rRNA and RNAP clusters occupying not only the center but also the periphery of the nucleoid, comparable to an expanded nucleoid. These results suggested that RNAP was organized into active transcription centers for rRNA synthesis under the rich medium growth condition; their presence and spatial organization, however, were independent of rRNA synthesis activity under the conditions used but were instead influenced by the structure and characteristics of the underlying nucleoid. Our work opens the door for further investigations of the function and molecular nature of RNAP clusters and points to a potentially new mechanism of transcription regulation by the spatial organization of individual molecular components.

]]></description>
<dc:subject>cell-biology structural-biology ultrastructure supramolecular-complexes cytoskeleton but-in-bacteria??? it's-more-complicated-than-you-think</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b2578693849a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:ultrastructure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:supramolecular-complexes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cytoskeleton"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:but-in-bacteria???"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-more-complicated-than-you-think"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://htsang.wikidot.com/research">
    <title>DR. HERBERT H. TSANG - http://www.herberttsang.org</title>
    <dc:date>2020-02-09T01:14:49+00:00</dc:date>
    <link>http://htsang.wikidot.com/research</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[RNA design algorithm takes an RNA secondary structure description as input and then try to identify an RNA strand that folds into this function-specific target structure. With new advances in biotechnology and synthetic biology, a reliable RNA design algorithm can be crucial steps to create new biochemical components. Our lab is interested in employing various computational intelligence techniques to propose the new paradigm to help with the RNA design problem. Recently, we have designed an algorithm SIMARD, which is based on the simulated annealing paradigm.
]]></description>
<dc:subject>structural-biology polymer-folding biochemistry biophysics simulation metaheuristics energy-landscapes rather-interesting to-write-about to-simulate to-visualize</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d531cf9636cd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:polymer-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:energy-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-visualize"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://manual.gromacs.org/documentation/2019/how-to/index.html">
    <title>Short How-To guides — GROMACS 2019 documentation</title>
    <dc:date>2020-02-09T01:06:09+00:00</dc:date>
    <link>http://manual.gromacs.org/documentation/2019/how-to/index.html</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A number of short guides are presented here to help users getting started with simulations. More detailed tutorials are available for example at the http://www.mdtutorials.com/.

]]></description>
<dc:subject>structural-biology simulation software open-source rather-interesting to-simulate to-write-about consider:looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9395b17ef4db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:software"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:open-source"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1803.07114">
    <title>[1803.07114] Slipknotting in Random Diagrams</title>
    <dc:date>2020-02-09T00:45:31+00:00</dc:date>
    <link>https://arxiv.org/abs/1803.07114</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The presence of slipknots in configurations of proteins and DNA has been shown to affect their functionality, or alter it entirely. Historically, polymers are modeled as polygonal chains in space. As an alternative to space curves, we provide a framework for working with subknots inside of knot diagrams via knotoid diagrams. We prove using a pattern theorem for knot diagrams that not only are almost all knot diagrams slipknotted, almost all unknot diagrams are slipknotted. This proves in the random diagram model a conjecture yet unproven in random space curve models. We also discuss conjectures on the enumeration of knotoid diagrams.
]]></description>
<dc:subject>knot-theory combinatorics sampling random-walks structural-biology rather-interesting theoretical-biology to-simulate to-write-about consider:representation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:6e72fdda6ed0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:knot-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:combinatorics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sampling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:random-walks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2001.11709">
    <title>[2001.11709] Gaussian Random Embeddings of Multigraphs</title>
    <dc:date>2020-02-05T12:22:13+00:00</dc:date>
    <link>https://arxiv.org/abs/2001.11709</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[This paper generalizes the Gaussian random walk and Gaussian random polygon models for linear and ring polymers to polymer topologies specified by an arbitrary multigraph $G$. Probability distributions of monomer positions and edge displacements are given explicitly and the spectrum of the graph Laplacian of $G$ is shown to predict the geometry of the configurations. This provides a new perspective on the James-Guth-Flory theory of phantom elastic networks. The model is based on linear algebra motivated by ideas from homology and cohomology theory. It provides a robust theoretical foundation for more detailed models of topological polymers.
]]></description>
<dc:subject>structural-biology probability-theory random-walks rather-interesting to-simulate constraint-satisfaction polymers to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8473fce4e2d9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:probability-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:random-walks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:polymers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1806.03333">
    <title>[1806.03333] The rainbow-spectrum of RNA secondary structures</title>
    <dc:date>2020-01-10T21:01:53+00:00</dc:date>
    <link>https://arxiv.org/abs/1806.03333</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper we analyze the length-spectrum of rainbows in RNA secondary structures. A rainbow in a secondary structure is a maximal arc with respect to the partial order induced by nesting. We show that there is a significant gap in this length-spectrum. We shall prove that there asymptotically almost surely exists a unique longest rainbow of length at least n−O(n1/2) and that with high probability any other rainbow has finite length. We show that the distribution of the length of the longest rainbow converges to a discrete limit law and that, for finite k, the distribution of rainbows of length k, becomes for large n a negative binomial distribution. We then put the results of this paper into context, comparing the analytical results with those observed in RNA minimum free energy structures, biological RNA structures and relate our findings to the sparsification of folding algorithms.
]]></description>
<dc:subject>structural-biology RNA-folding hey-I-know-this-guy molecular-design simulation rather-interesting feature-construction to-write-about to-simulate consider:extreme-cases consider:feature-discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a438e8d14c51/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:RNA-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-I-know-this-guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-construction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:extreme-cases"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.00721">
    <title>[1705.00721] Single microtubules and small networks become significantly stiffer on short time-scales upon mechanical stimulation</title>
    <dc:date>2019-06-12T13:50:33+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.00721</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The transfer of mechanical signals through cells is a complex phenomenon. To uncover a new mechanotransduction pathway, we study the frequency-dependent transport of mechanical stimuli by single microtubules and small networks in a bottom-up approach using optically trapped beads as anchor points. We interconnected microtubules to linear and triangular geometries to perform micro-rheology by defined oscillations of the beads relative to each other. We found a substantial stiffening of single filaments above a characteristic transition frequency of 1-30 Hz depending on the filament's molecular composition. Below this frequency, filament elasticity only depends on its contour and persistence length. Interestingly, this elastic behavior is transferable to small networks, where we found the surprising effect that linear two filament connections act as transistor-like, angle dependent momentum filters, whereas triangular networks act as stabilizing elements. These observations implicate that cells can tune mechanical signals by temporal and spatial filtering stronger and more flexibly than expected.
]]></description>
<dc:subject>phrasing! biophysics cytoskeleton microtubules structural-biology nanotechnology rather-interesting experiment looking-to-see</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c7766fc0c267/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:phrasing!"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cytoskeleton"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:microtubules"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:looking-to-see"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1608.03145">
    <title>[1608.03145] Physical model of the genotype-to-phenotype map of proteins</title>
    <dc:date>2019-04-24T15:24:51+00:00</dc:date>
    <link>https://arxiv.org/abs/1608.03145</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[How DNA is mapped to functional proteins is a basic question of living matter. We introduce and study a physical model of protein evolution which suggests a mechanical basis for this map. Many proteins rely on large-scale motion to function. We therefore treat protein as learning amorphous matter that evolves towards such a mechanical function: Genes are binary sequences that encode the connectivity of the amino acid network that makes a protein. The gene is evolved until the network forms a shear band across the protein, which allows for long-range, soft modes required for protein function. The evolution reduces the high-dimensional sequence space to a low-dimensional space of mechanical modes, in accord with the observed dimensional reduction between genotype and phenotype of proteins. Spectral analysis of the space of 106 solutions shows a strong correspondence between localization around the shear band of both mechanical modes and the sequence structure. Specifically, our model shows how mutations of the gene and their correlations occur at amino acids whose interactions determine the functional mode.
]]></description>
<dc:subject>complexology rather-interesting theoretical-biology abstract-models combinatorics bioinformatics structural-biology structure-function-cartoons to-write-about to-simulate lattice-polymers</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:63c4d1b88c21/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:abstract-models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:combinatorics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structure-function-cartoons"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lattice-polymers"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1904.04610">
    <title>[1904.04610] Buckling Soft Tensegrities: Fickle Elasticity and Configurational Switching in Living Cells</title>
    <dc:date>2019-04-24T15:14:58+00:00</dc:date>
    <link>https://arxiv.org/abs/1904.04610</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Tensegrity structures are special architectures made by floating compressed struts kept together by a continuous system of tensed cables. The multiplicity of shapes that tensegrity structures can assume and their intrinsic capability to be deployable and assembled, so storing (and releasing) elastic energy, have motivated their success as paradigm -pioneeringly proposed by Donald E. Ingber- to explain some underlying mechanisms regulating dynamics of living cells. The interlaced structure of the cell cytoskeleton, constituted by actin and intermediate filaments and microtubules which continuously change their spatial organization and pre-stresses through polymerization/depolymerization, seems to steer migration, adhesion and cell division by obeying the tensegrity construct. Even though rough calculations lead to estimate discrepancies when comparing axial stiffness of actin filaments and microtubules and recent works have shown bent microtubules, no one has yet tried to remove the hypothesis of rigid struts in tensegrities when used to idealize the cytoskeleton mechanics. With reference to the 30-element tensegrity cell paradigm, we introduce both compressibility and bendability of the struts and rewrite the theory to take into account nonlinear elasticity of both tendons and bars, so abandoning the classical linear stress-strain assumptions. By relaxing the hypothesis of rigidity of the struts, we demonstrate that some quantitative confirmations and many extreme and somehow counterintuitive mechanical behaviors actually exploited by cells for storing/releasing energy, resisting to applied loads and deforming by modulating their overall elasticity and shape through pre-stress changes and instability-guided configurational switching, can be all theoretically found.
]]></description>
<dc:subject>theoretical-biology cell-biology structural-biology tensegrity statics dynamics mechanical-engineering mechanical-design rather-interesting to-write-about to-do to-simulate consider:genetic-programming cytoskeleton</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:0275e9f42a66/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tensegrity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanical-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanical-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-do"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-simulate"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cytoskeleton"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1610.07277">
    <title>[1610.07277] Rapid calculation of side chain packing and free energy with applications to protein molecular dynamics</title>
    <dc:date>2017-10-12T10:56:57+00:00</dc:date>
    <link>https://arxiv.org/abs/1610.07277</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[To address the large gap between time scales that can be easily reached by molecular simulations and those required to understand protein dynamics, we propose a rapid self-consistent approximation of the side chain free energy at every integration step. In analogy with the adiabatic Born-Oppenheimer approximation for electronic structure, the protein backbone dynamics are simulated as preceding according to the dictates of the free energy of an instantaneously-equilibrated side chain potential. The side chain free energy is computed on the fly, allowing the protein backbone dynamics to traverse a greatly smoothed energetic landscape. This results in extremely rapid equilibration and sampling of the Boltzmann distribution. Because our method employs a reduced model involving single-bead side chains, we also provide a novel, maximum-likelihood method to parameterize the side chain model using input data from high resolution protein crystal structures. We demonstrate state-of-the-art accuracy for predicting χ1 rotamer states while consuming only milliseconds of CPU time. We also show that the resulting free energies of side chains is sufficiently accurate for de novo folding of some proteins.]]></description>
<dc:subject>structural-biology approximation heuristics algorithms protein-folding to-write-about nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:afdc2b1f0a08/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1109.6459">
    <title>[1109.6459] Folding Kinetics of a Polymer</title>
    <dc:date>2017-09-25T11:40:12+00:00</dc:date>
    <link>https://arxiv.org/abs/1109.6459</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[By simulating the first order globule-crystal transition of a flexible homopolymer chain, both by collision dynamics and Monte Carlo with non-kinetic moves, we show that the effective and the thermodynamic transition temperatures are different and we propose a way of quantifying the kinetic hindering. We then also observe that the top eigenvalue in the spectrum of the dynamical (contact or adjacency) matrix provides insight into the ensembles of folding and unfolding trajectories, and may be a suitable additional reaction coordinate for the folding transition of chain molecules.
]]></description>
<dc:subject>lattice-proteins biophysics structural-biology simulation energy-landscapes to-write-about rather-interesting feature-construction</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:87cd953887c9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:lattice-proteins"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:energy-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-construction"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1702.01994">
    <title>[1702.01994] Polyomino Models of Surface Supramolecular Assembly: Design Constraints and Structural Selectivity</title>
    <dc:date>2017-09-23T12:01:03+00:00</dc:date>
    <link>https://arxiv.org/abs/1702.01994</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We examine emergent properties of 2D supramolecular networks, using enumeration of configurations formed by interacting dominoes on square lattices as a simple model system. Possible ground states are identified using a convex hull construction in the interaction parameters for nearest-neighbour bonds. We demonstrate how this construction can be used to design interaction parameters which lead to networks with specific properties, including chirality and highly degenerate ground states. We then introduce kinetics as simple local rearrangements. By partitioning the configuration space into smaller sets which satisfy different topological constraints, we can design configurations which are kinetically trapped. By considering heat capacity curves along directions through the convex hull, we also demonstrate design of interacting domino configurations to create tilings robust against temperature induced phase transitions. We discuss extension of this design construction to more complex molecular shapes.
]]></description>
<dc:subject>tiling supramolecular-complexes biological-engineering rather-interesting to-write-about energy-landscapes self-assembly molecular-design structural-biology nanotechnology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d7c71fe82a17/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:supramolecular-complexes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:energy-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1506.08747">
    <title>[1506.08747] Faceted particles formed by the frustrated packing of anisotropic colloids on curved surfaces</title>
    <dc:date>2017-01-04T13:07:47+00:00</dc:date>
    <link>https://arxiv.org/abs/1506.08747</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We use computer simulations and simple theoretical models to analyze the morphologies that result when rod-like particles end-attach onto a curved surface, creating a finite-thickness monolayer aligned with the surface normal. This geometry leads to two forms of frustration, one associated with the incompatibility of hexagonal order on surfaces with Gaussian curvature, and the second reflecting the deformation of a layer with finite thickness on a surface with non-zero mean curvature. We show that the latter effect leads to a faceting mechanism. Above threshold values of the inter-particle attraction strength and surface mean curvature, the adsorbed layer undergoes a transition from orientational disorder to an ordered state that is demarcated by reproducible patterns of line defects. The number of facets is controlled by the competition between line defect energy and intra-facet strain. Tuning control parameters thus leads to a rich variety of morphologies, including icosahedral particles and irregular polyhedra. In addition to suggesting a new strategy for the synthesis of aspherical particles with tunable symmetries, our results may shed light on recent experiments in which rod-like HIV GAG proteins assemble around nanoscale particles.
]]></description>
<dc:subject>self-assembly rather-interesting nanotechnology packing structural-biology engineering-design simulation to-write-about</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:875cbef2530e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:packing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1608.06971">
    <title>[1608.06971] Protein Collapse is Encoded in the Folded State Architecture</title>
    <dc:date>2016-12-31T11:50:08+00:00</dc:date>
    <link>https://arxiv.org/abs/1608.06971</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Natural protein sequences that self-assemble to form globular structures are compact with high packing densities in the folded states. It is known that proteins unfold upon addition of denaturants, adopting random coil structures. The dependence of the radii of gyration on protein size in the folded and unfolded states obeys the same scaling laws as synthetic polymers. Thus, one might surmise that the mechanism of collapse in proteins and polymers ought to be similar. However, because the number of amino acids in single domain proteins is not significantly greater than about two hundred, it has not been resolved if the unfolded states of proteins are compact under conditions that favor the folded states - a problem at the heart of how proteins fold. By adopting a theory used to derive polymer-scaling laws, we find that the propensity for the unfolded state of a protein to be compact is universal and is encoded in the contact map of the folded state. Remarkably, analysis of over 2000 proteins shows that proteins rich in β-sheets have greater tendency to be compact than α-helical proteins. The theory provides insights into the reasons for the small size of single domain proteins and the physical basis for the origin of multi-domain proteins. Application to non-coding RNA molecules show that they have evolved to collapse sharing similarities to β-sheet proteins. An implication of our theory is that the evolution of natural foldable sequences is guided by the requirement that for efficient folding they should populate minimum energy compact states under folding conditions. This concept also supports the compaction selection hypothesis used to rationalize the unusually condensed states of viral RNA molecules.
]]></description>
<dc:subject>structural-biology protein-folding rather-interesting bioinformatics theoretical-biology self-organization engineering-design dynamical-systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:81ce4c9f7934/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1611.00842">
    <title>[1611.00842] Symmetry and size of membrane protein polyhedral nanoparticles</title>
    <dc:date>2016-12-28T12:02:14+00:00</dc:date>
    <link>https://arxiv.org/abs/1611.00842</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In recent experiments [T. Basta et al., Proc. Natl. Acad. Sci. U.S.A. 111, 670 (2014)] lipids and membrane proteins were observed to self-assemble into membrane protein polyhedral nanoparticles (MPPNs) with a well-defined polyhedral protein arrangement and characteristic size. We develop a model of MPPN self-assembly in which the preferred symmetry and size of MPPNs emerge from the interplay of protein-induced lipid bilayer deformations, topological defects in protein packing, and thermal effects. With all model parameters determined directly from experiments, our model correctly predicts the observed symmetry and size of MPPNs. Our model suggests how key lipid and protein properties can be modified to produce a range of MPPN symmetries and sizes in experiments.
]]></description>
<dc:subject>packing engineering-design rather-interesting structural-biology nanotechnology nudge-targets consider:feature-discovery consider:classification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:9fd5c30c80a3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:packing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:feature-discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:classification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://biorxiv.org/content/early/2016/12/03/091389?rss=1%2522">
    <title>Exploring the mutational robustness of nucleic acids by searching genotype neighbourhoods in sequence space | bioRxiv</title>
    <dc:date>2016-12-17T13:53:57+00:00</dc:date>
    <link>http://biorxiv.org/content/early/2016/12/03/091389?rss=1%2522</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[To assess the mutational robustness of nucleic acids, many genome- and protein-level studies have been performed; in these investigations, nucleic acids are treated as genetic information carriers and transferrers. However, the molecular mechanism through which mutations alter the structural, dynamic and functional properties of nucleic acids is poorly understood. Here, we performed SELEX in silico study to investigate the fitness distribution of the nucleic acid genotype neighborhood in a sequence space for L-Arm binding aptamer. Although most mutants of the L-Arm-binding aptamer failed to retain their ligand-binding ability, two novel functional genotype neighborhoods were isolated by SELEX in silico and experimentally verified to have similar binding affinity (Kd = 69.3 μM and 110.7 μM) as the wild-type aptamer (Kd = 114.4 μM). Based on data from the current study and previous research, mutational robustness is strongly influenced by the local base environment and ligand-binding mode, whereas bases distant from the binding pocket provide potential evolutionary pathways to approach global fitness maximum. Our work provides an example of successful application of SELEX in silico to optimize an aptamer and demonstrates the strong sensitivity of mutational robustness to the site of genetic variation.

]]></description>
<dc:subject>biophysics structural-biology fitness-landscapes combinatorial-libraries aptamers rather-interesting to-write-about experiment</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f84e913f3f8f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:fitness-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:combinatorial-libraries"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aptamers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:to-write-about"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://biorxiv.org/content/early/2016/04/25/050146?rss=1%2522">
    <title>Chromatin structure shapes the search process of transcription factors | bioRxiv</title>
    <dc:date>2016-05-01T11:57:47+00:00</dc:date>
    <link>http://biorxiv.org/content/early/2016/04/25/050146?rss=1%2522</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The diffusion of regulatory proteins within the nucleus plays a crucial role in the dynamics of transcriptional regulation. The standard model assumes a 3D plus 1D diffusion process: regulatory proteins either move freely in solution or slide on DNA. This model however does not considered the 3D structure of chromatin. Here we proposed a multi-scale stochastic model that integrates, for the first time, high-resolution information on chromatin structure as well as DNA-protein interactions. The dynamics of transcription factors was modeled as a slide plus jump diffusion process on a chromatin network based on pair-wise contact maps obtained from high-resolution Hi-C experiments. Our model allowed us to uncover the effects of chromatin structure on transcription factor occupancy profiles and target search times. Finally, we showed that binding sites clustered on few topological associated domains leading to a higher local concentration of transcription factors which could reflect an optimal strategy to efficiently use limited transcriptional resources.

]]></description>
<dc:subject>structural-biology molecular-design molecular-biology systems-biology bioinformatics it's-more-complicated-than-you-think</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f151d98280fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-more-complicated-than-you-think"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1511.01848">
    <title>[1511.01848] Binding of bivalent transcription factors to active and inactive regions folds human chromosomes into loops, rosettes and domains</title>
    <dc:date>2016-03-26T22:15:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1511.01848</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Biophysicists are modeling conformations of interphase chromosomes, often basing the strengths of interactions between segments distant on the genetic map on contact frequencies determined experimentally. Here, instead, we develop a fitting-free, minimal model: bivalent red and green "transcription factors" bind to cognate sites in runs of beads ("chromatin") to form molecular bridges stabilizing loops. In the absence of additional explicit forces, molecular dynamic simulations reveal that bound "factors' spontaneously cluster -- red with red, green with green, but rarely red with green -- to give structures reminiscent of transcription factories. Binding of just two transcription factors (or proteins) to active and inactive regions of human chromosomes yields rosettes, topological domains, and contact maps much like those seen experimentally. This emergent "bridging-induced attraction" proves to be a robust, simple, and generic force able to organize interphase chromosomes at all scales.
]]></description>
<dc:subject>structural-biology simulation macromolecular-complexes cell-biology rather-interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cf9efa46ddc2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:macromolecular-complexes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1511.00139">
    <title>[1511.00139] Chaos of Protein Folding</title>
    <dc:date>2016-03-26T20:51:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1511.00139</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[As protein folding is a NP-complete problem, artificial intelligence tools like neural networks and genetic algorithms are used to attempt to predict the 3D shape of an amino acids sequence. Underlying these attempts, it is supposed that this folding process is predictable. However, to the best of our knowledge, this important assumption has been neither proven, nor studied. In this paper the topological dynamic of protein folding is evaluated. It is mathematically established that protein folding in 2D hydrophobic-hydrophilic (HP) square lattice model is chaotic as defined by Devaney. Consequences for both structure prediction and biology are then outlined.
]]></description>
<dc:subject>protein-folding Devaney-Model theoretical-biology structural-biology nudge-targets consider:representation consider:looking-to-see metaheuristics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:01bc08b528b6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Devaney-Model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:looking-to-see"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1501.03971">
    <title>[1501.03971] Bayesian protein structure alignment</title>
    <dc:date>2016-02-27T21:43:28+00:00</dc:date>
    <link>http://arxiv.org/abs/1501.03971</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The analysis of the three-dimensional structure of proteins is an important topic in molecular biochemistry. Structure plays a critical role in defining the function of proteins and is more strongly conserved than amino acid sequence over evolutionary timescales. A key challenge is the identification and evaluation of structural similarity between proteins; such analysis can aid in understanding the role of newly discovered proteins and help elucidate evolutionary relationships between organisms. Computational biologists have developed many clever algorithmic techniques for comparing protein structures, however, all are based on heuristic optimization criteria, making statistical interpretation somewhat difficult. Here we present a fully probabilistic framework for pairwise structural alignment of proteins. Our approach has several advantages, including the ability to capture alignment uncertainty and to estimate key "gap" parameters which critically affect the quality of the alignment. We show that several existing alignment methods arise as maximum a posteriori estimates under specific choices of prior distributions and error models. Our probabilistic framework is also easily extended to incorporate additional information, which we demonstrate by including primary sequence information to generate simultaneous sequence-structure alignments that can resolve ambiguities obtained using structure alone. This combined model also provides a natural approach for the difficult task of estimating evolutionary distance based on structural alignments. The model is illustrated by comparison with well-established methods on several challenging protein alignment examples.
]]></description>
<dc:subject>biochemistry structural-biology protein-folding bioinformatics algorithms machine-learning statistics metrics nudge-targets consider:distance-measure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:eefeff02cf1a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:distance-measure"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1512.00268">
    <title>[1512.00268] Chromatin assortativity: integrating epigenomic data and 3D genomic structure</title>
    <dc:date>2016-02-25T11:23:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1512.00268</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Background: The field of 3D chromatin interaction mapping is changing our point of view on the genome, paving the way for new insights into its organization. Network analysis is a natural and powerful way of modelling chromatin interactions. Assortativity is a network property that has been widely used in the social sciences to measure the probability of nodes with similar values of a specific feature to interact preferentially. We propose a new approach, using Chromatin feature Assortativity (ChAs), to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network. Results: We use high-resolution Promoter Capture Hi-C and Hi-Cap data as well as ChIA-PET data from embryonic stem cells to generate promoter-centered interaction networks. We calculate the presence of a collection of 78 chromatin features in the chromatin fragments constituting the nodes of the network. Based on the ChAs of these epigenomic features calculated in 4 different interaction networks, we find Polycomb Group proteins and associated histone marks to play a prominent role. Remarkably, in promoter-centered networks, we observe higher ChAs of the actively elongating form of RNA Polymerase 2 compared to inactive forms in interactions between promoters and other elements. Conclusions: Contacts amongst promoters and between promoters and other elements have different characteristic epigenomic features. Using ChAs we identify a possible role of the elongating form of RNAPII in enhancer activity. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing for the comparison of any number of chromatin interaction networks.
]]></description>
<dc:subject>bioinformatics structural-biology it's-more-complicated-than-you-think supramolecular-complex cell-biology molecular-machinery experiment</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d7eadedfcdd2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-more-complicated-than-you-think"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:supramolecular-complex"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1509.03434">
    <title>[1509.03434] Improving protein threading accuracy via combining local and global potential using TreeCRF model</title>
    <dc:date>2015-12-25T17:11:31+00:00</dc:date>
    <link>http://arxiv.org/abs/1509.03434</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Protein structure prediction remains to be an open problem in bioinformatics. There are two main categories of methods for protein structure prediction: Free Modeling (FM) and Template Based Modeling (TBM). Protein threading, belonging to the category of template based modeling, identifies the most likely fold with the target by making a sequence-structure alignment between target protein and template protein. Though protein threading has been shown to more be successful for protein structure prediction, it performs poorly for remote homology detection.
]]></description>
<dc:subject>structural-biology biophysics protein-folding models optimization algorithms representation nudge-targets bioinformatics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ad99a83404f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:representation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1507.08335">
    <title>[1507.08335] Dissecting the roles of local packing density and longer-range effects in protein sequence evolution</title>
    <dc:date>2015-08-07T11:19:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1507.08335</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[What are the structural determinants of protein sequence evolution? A number of site-specific structural characteristics have been proposed, most of which are broadly related to either the density of contacts or the solvent accessibility of individual residues. Most importantly, there has been disagreement in the literature over the relative importance of solvent accessibility and local packing density for explaining site-specific sequence variability in proteins. We show here that this discussion has been confounded by the definition of local packing density. The most commonly used measures of local packing, such as the contact number and the weighted contact number, represent by definition the combined effects of local packing density and longer-range effects. As an alternative, we here propose a truly local measure of packing density around a single residue, based on the Voronoi cell volume. We show that the Voronoi cell volume, when calculated relative to the geometric center of amino-acid side chains, behaves nearly identically to the relative solvent accessibility, and both can explain, on average, approximately 34\% of the site-specific variation in evolutionary rate in a data set of 209 enzymes. An additional 10\% of variation can be explained by non-local effects that are captured in the weighted contact number. Consequently, evolutionary variation at a site is determined by the combined action of the immediate amino-acid neighbors of that site and of effects mediated by more distant amino acids. We conclude that instead of contrasting solvent accessibility and local packing density, future research should emphasize the relative importance of immediate contacts and longer-range effects on evolutionary variation.
]]></description>
<dc:subject>biophysics structural-biology theoretical-biology elegant-demonstrations it's-crowded-in-there complexology models-and-modes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:700a45381acb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:theoretical-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:elegant-demonstrations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-crowded-in-there"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models-and-modes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1412.3138">
    <title>[1412.3138] Computational Protein Design Using AND/OR Branch-and-Bound Search</title>
    <dc:date>2015-07-25T11:29:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1412.3138</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design. In this paper, we propose a new protein design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process. Tests on real protein data show that our new protein design algorithm is able to solve many prob- lems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.
]]></description>
<dc:subject>structural-biology biochemistry constraint-satisfaction biological-engineering engineering-design nudge-targets consider:rediscovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fcb821ffbd3e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:constraint-satisfaction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:rediscovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.4465">
    <title>[1410.4465] On the abundance of intrinsically disordered proteins in the human proteome and its relation to diseases: there is no enrichment</title>
    <dc:date>2015-03-15T20:39:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.4465</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Intrinsically disordered proteins are fascinating the community of protein science since the last decade, at least. There is a well-established line of research that intends to reveal the crucial role played by intrinsically disordered proteins (IDPs) in the development of human diseases. The main argument is that IDPs are differentially more present in groups of disease-related proteins. In this note we compare the frequency of disorder in human proteins, both disease-related and not. The frequency of disorder is comparable in the two sub-groups of proteins. Disorder is widespread in human proteins, but it is not a specific pre-requisite of proteins involved in the development of cancer, cardiovascular diseases, diabetes and neurodegenerative diseases. A tendency of cancer-related proteins to be statistically more disordered than the rest of human proteins is confirmed.
]]></description>
<dc:subject>bioinformatics biochemistry structural-biology protein-folding Thom-LaBean's-thesis it's-more-complicated-than-you-think Oh Science.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:e941d1fa5934/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Thom-LaBean's-thesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-more-complicated-than-you-think"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Oh"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:Science."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1501.04071">
    <title>[1501.04071] Structures of Spherical Viral Capsids as Quasicrystalline Tilings</title>
    <dc:date>2015-02-02T18:22:31+00:00</dc:date>
    <link>http://arxiv.org/abs/1501.04071</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Spherical viral shells with icosahedral symmetry are considered as quasicrystalline tilings. Similarly to known Caspar-Klug quasi-equivalence theory, the presented approach also minimizes the number of conformations necessary for the protein molecule bonding with its neighbors in the shell, but is based on different geometrical principles. It is assumed that protein molecule centers are located at vertices of tiles with identical edges, and the number of different tile types is minimal. Idealized coordinates of nonequivalent by symmetry protein positions in six various capsid types are obtained. The approach describes in a uniform way both the structures satisfying the well-known Caspar and Klug geometrical model and the structures contradicting this model.
]]></description>
<dc:subject>geometry structural-biology quasicrystals tiling stamp-collecting nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:50e41de9cec1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:quasicrystals"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:stamp-collecting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.4530">
    <title>[1410.4530] Fatigueless response of spider draglines in cyclic torsion facilitated by reversible molecular deformation</title>
    <dc:date>2014-12-06T17:13:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.4530</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We demonstrate that spider draglines exhibit a fatigueless response in extreme cyclic torsion up to its breaking limit. The well defined Raman bands at 1095 and 1245cm−1 shifted linearly towards lower wavenumbers versus increasing twist in both clockwise and counter-clockwise directions. Under thousands of continuous loading cycles of twist strain approaching its breaking limit, all the Raman bands were preserved and the characteristic Raman peak shifts were found to be reversible. Besides, nanoscale surface profile of the worked silk appeared as good as the pristine silk. This unique fatigueless twist response of draglines, facilitated by reversible deformation of protein molecules, could find applications in durable miniatured devices.
]]></description>
<dc:subject>materials-science biological-engineering spiders-rock biochemistry structural-biology mechanical-engineering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:87ddee18247e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:materials-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:spiders-rock"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:mechanical-engineering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1403.2269">
    <title>[1403.2269] Virus Assembly on a Membrane is Facilitated by Membrane Microdomains</title>
    <dc:date>2014-04-20T10:28:46+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.2269</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[For many viruses assembly and budding occur simultaneously during virion formation. Understanding the mechanisms underlying this process could promote biomedical efforts to block viral propagation and enable use of capsids in nanomaterials applications. To this end, we have performed molecular dynamics simulations on a coarse-grained model that describes virus assembly on a fluctuating lipid membrane. Our simulations show that the membrane can promote association of adsorbed subunits through dimensional reduction, but also can introduce barriers that inhibit complete assembly. We find several mechanisms, including one not anticipated by equilibrium theories, by which membrane microdomains, such as lipid rafts, can enhance assembly by reducing these barriers. We show how this predicted mechanism can be experimentally tested. Furthermore, the simulations demonstrate that assembly and budding depend crucially on the system dynamics via multiple timescales related to membrane deformation, protein diffusion, association, and adsorption onto the membrane.
]]></description>
<dc:subject>structural-biology simulation membrane-biochemistry self-assembly molecular-design molecular-machinery well-duh it's-crowded-inside-a-cell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f4c5e15ddc07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:membrane-biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-assembly"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:well-duh"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:it's-crowded-inside-a-cell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.4146">
    <title>[1312.4146] Unfolding kinetics of periodic DNA hairpins</title>
    <dc:date>2014-01-17T14:57:29+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.4146</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[DNA hairpin molecules with periodic base sequences can be expected to exhibit a regular coarse-grained free energy landscape (FEL) as function of the number of open base pairs and applied mechanical force. Using a commonly employed model, we first analyse for which types of sequences a particularly simple landscape structure is predicted, where forward and backward energy barriers between partly unfolded states are decreasing linearly with force. Stochastic unfolding trajectories for such molecules with simple FEL are subsequently generated by kinetic Monte Carlo simulations. Introducing probabilities that can be sampled from these trajectories, it is shown how the parameters characterising the FEL can be estimated. Already 300 trajectories, as typically generated in experiments, provide faithful results for the FEL parameters.
]]></description>
<dc:subject>structural-biology molecular-machinery experiment modeling DNA biochemistry</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cd0bd3a00371/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:DNA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.3573">
    <title>[1311.3573] Improved design and screening of high bioactivity peptides for drug discovery</title>
    <dc:date>2013-12-16T23:16:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.3573</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The discovery of peptides having high biological activity is very challenging mainly because there is an enormous diversity of compounds and only a minority have the desired properties. To lower cost and reduce the time to obtain promising compounds, machine learning approaches can greatly assist in the process and even replace expensive laboratory experiments by learning a predictor with existing data. Unfortunately, selecting ligands having the greatest predicted bioactivity requires a prohibitive amount of computational time. For this combinatorial problem, heuristics and stochastic optimization methods are not guaranteed to find adequate compounds. 
We propose an efficient algorithm based on De Bruijn graphs, guaranteed to find the peptides of maximal predicted bioactivity. We demonstrate how this algorithm can be part of an iterative combinatorial chemistry procedure to speed up the discovery and the validation of peptide leads. Moreover, the proposed approach does not require the use of known ligands for the target protein since it can leverage recent multi-target machine learning predictors where ligands for similar targets can serve as initial training data. Finally, we validated the proposed approach in vitro with the discovery of new cationic anti-microbial peptides. 
]]></description>
<dc:subject>biological-engineering structural-biology systems-biology combinatorial-chemistry bioinformatics at-least-somebody-did-it-finally hey-that's-my-Penn-thesis (fifteen-years-later)</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ba1e928fd825/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:combinatorial-chemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:at-least-somebody-did-it-finally"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:hey-that's-my-Penn-thesis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:(fifteen-years-later)"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.7249">
    <title>[1310.7249] A Spatial Simulation Approach to Account for Protein Structure When Identifying Non-Random Somatic Mutations</title>
    <dc:date>2013-12-05T12:50:47+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.7249</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Background: Current research suggests that a small set of "driver" mutations are responsible for tumorigenesis while a larger body of "passenger" mutations occurs in the tumor but does not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of of methodologies that attempt to identify such mutations have been developed. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development of cluster identification algorithms has become critical. 
Results: We have developed a novel methodology, SpacePAC (Spatial Protein Amino acid Clustering), that identifies mutational clustering by considering the protein tertiary structure directly in 3D space. By combining the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and the spatial information in the Protein Data Bank (PDB), SpacePAC is able to identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In addition, SpacePAC is better able to localize the most significant mutational hotspots as demonstrated in the cases of BRAF and ALK. The R package is available on Bioconductor at: this http URL 
Conclusion: SpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structure
]]></description>
<dc:subject>structural-biology biophysics bioinformatics I-thought-Alan-Perelson-did-this-years-ago</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:18f8659471df/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:I-thought-Alan-Perelson-did-this-years-ago"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.4309">
    <title>[1310.4309] Optimizing water permeability through the hourglass shape of aquaporins</title>
    <dc:date>2013-12-04T23:11:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.4309</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The ubiquitous aquaporin channels are able to conduct water across cell membranes, combining the seemingly antagonist functions of a very high selectivity with a remarkable permeability. Whereas molecular details are obvious keys to perform these tasks, the overall efficiency of transport in such nanopores is also strongly limited by viscous dissipation arising at the connection between the nanoconstriction and the nearby bulk reservoirs. In this contribution, we focus on these so-called entrance effects and specifically examine whether the characteristic hourglass shape of aquaporins may arise from a geometrical optimum for such hydrodynamic dissipation. Using a combination of finite-element calculations and analytical modeling, we show that conical entrances with suitable opening angle can indeed provide a large increase of the overall channel permeability. Moreover, the optimal opening angles that maximize the permeability are found to compare well with the angles measured in a large variety of aquaporins. This suggests that the hourglass shape of aquaporins could be the result of a natural selection process toward optimal hydrodynamic transport. Finally, in a biomimetic perspective, these results provide guidelines to design artificial nanopores with optimal performances.
]]></description>
<dc:subject>molecular-design molecular-machinery biological-engineering structural-biology engineering-design</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:b530f83e2d6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.3185">
    <title>[1310.3185] Optimization of collective enzyme activity via spatial localization</title>
    <dc:date>2013-12-04T21:33:15+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.3185</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The spatial organization of enzymes often plays a crucial role in the functionality and efficiency of enzymatic pathways. To fully understand the design and operation of enzymatic pathways, it is therefore crucial to understand how the relative arrangement of enzymes affects pathway function. Here we investigate the effect of enzyme localization on the flux of a minimal two-enzyme pathway within a reaction-diffusion model. We consider different reaction kinetics, spatial dimensions, and loss mechanisms for intermediate substrate molecules. Our systematic analysis of the different regimes of this model reveals both universal features and distinct characteristics in the phenomenology of these different systems. In particular, the distribution of the second pathway enzyme that maximizes the reaction flux undergoes a generic transition from co-localization with the first enzyme when the catalytic efficiency of the second enzyme is low, to an extended profile when the catalytic efficiency is high. However, the critical transition point and the shape of the extended optimal profile is significantly affected by specific features of the model. We explain the behavior of these different systems in terms of the underlying stochastic reaction and diffusion processes of single substrate molecules.
]]></description>
<dc:subject>structural-biology systems-biology space-matters departures-from-the-ideal molecular-biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d3a2dfd26eeb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:space-matters"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:departures-from-the-ideal"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.4514">
    <title>[1311.4514] The importance of crowding in signaling, genetic, and metabolic networks</title>
    <dc:date>2013-12-04T21:31:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.4514</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[It is now well established that the cell is a highly crowded environment. Yet, the effects of crowding on the dynamics of signaling pathways, gene regulation networks and metabolic networks are still largely unknown. Crowding can alter both molecular diffusion and the equilibria of biomolecular reactions. In this review, we first discuss how diffusion can affect biochemical networks. Diffusion of transcription factors can increase noise in gene expression, while diffusion of proteins between intracellular compartments or between cells can reduce concentration fluctuations. In push-pull networks diffusion can impede information transmission, while in multi-site protein modification networks diffusion can qualitatively change the macroscopic response of the system, such as the loss or emergence of bistability. Moreover, diffusion can directly change the metabolic flux. We describe how crowding affects diffusion, and thus how all these phenomena are influenced by crowding. Yet, a potentially more important effect of crowding on biochemical networks is mediated via the shift in the equilibria of bimolecular reactions, and we provide computational evidence that supports this idea. Finally, we discuss how the effects of crowding can be incorporated in models of biochemical networks.
]]></description>
<dc:subject>systems-biology structural-biology molecular-machinery dynamical-systems space-matters simulation departures-from-the-ideal interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f5cfd1d5738f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:dynamical-systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:space-matters"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:departures-from-the-ideal"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.6741">
    <title>[1310.6741] Design principles governing the motility of myosin V</title>
    <dc:date>2013-11-21T13:09:19+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.6741</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The molecular motor myosin V exhibits a wide repertoire of pathways during the stepping process, which is intimately connected to its biological function. The best understood of these is hand-over-hand stepping by a swinging lever arm movement toward the plus-end of actin filaments, essential to its role as a cellular transporter. However, single-molecule experiments have also shown that the motor "foot stomps", with one hand detaching and rebinding to the same site, and backsteps under sufficient load. Explaining the complete taxonomy of myosin V's load-dependent stepping pathways, and the extent to which these are constrained by motor structure and mechanochemistry, are still open questions. Starting from a polymer model, we develop an analytical theory to understand the minimal physical properties that govern motor dynamics. In particular, we solve the first-passage problem of the head reaching the target binding site, investigating the competing effects of load pulling back at the motor, strain in the leading head that biases the diffusion in the direction of the target, and the possibility of preferential binding to the forward site due to the recovery stroke. The theory reproduces a variety of experimental data, including the power stroke and slow diffusive search regimes in the mean trajectory of the detached head, and the force dependence of the forward-to-backward step ratio, run length, and velocity. The analytical approach yields a formula for the stall force, identifying the relative contributions of the chemical cycle rates and mechanical features like the bending rigidities of the lever arms. Most importantly, by fully exploring the design space of the motor, we predict that myosin V is a robust motor whose dynamical behavior is not compromised by reasonable perturbations to the reaction cycle, and changes in the architecture of the lever arm.
]]></description>
<dc:subject>biophysics structural-biology cytoskeleton biological-engineering experiment</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:32a9a1ded380/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cytoskeleton"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.1403">
    <title>[1310.1403] Sequence-Structure Relationship in Proteins: a Computational Analysis of Proteins that Differ in Sequence but Share the Same Fold</title>
    <dc:date>2013-11-16T19:56:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.1403</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Mapping between sequence and structure is currently an open problem in structural biology. Despite many experimental and computational efforts it is not clear yet how the structure is encoded in the sequence. Answering this question may pave the way for predicting a protein fold given its sequence. 
My doctoral studies have focused on a particular phenomenon relevant to the protein sequence-structure relationship. It has been observed that many proteins having apparently dissimilar sequences share the same native fold. The phenomenon of mapping many divergent sequences into a single fold raises the question of which positions along the sequence are important for the conservation of fold and function in dissimilar sequences. Detecting those positions, and classifying them according to role can help understand which elements in a sequence are important for maintenance of structure and/or function. In the course of my doctoral research I have attempted to discover and characterize those positions.
]]></description>
<dc:subject>structural-biology protein-folding biological-engineering biophysics interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:f6a4059c8d99/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.4223">
    <title>[1310.4223] Exact Learning of RNA Energy Parameters From Structure</title>
    <dc:date>2013-11-03T13:36:47+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.4223</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We consider the problem of exact learning of parameters of a linear RNA energy model from secondary structure data. A necessary and sufficient condition for learnability of parameters is derived, which is based on computing the convex hull of union of translated Newton polytopes of input sequences. The set of learned energy parameters is characterized as the convex cone generated by the normal vectors to those facets of the resulting polytope that are incident to the origin. In practice, the sufficient condition may not be satisfied by the entire training data set; hence, computing a maximal subset of training data for which the sufficient condition is satisfied is often desired. We show that problem is NP-hard in general for an arbitrary dimensional feature space. Using a randomized greedy algorithm, we select a subset of RNA STRAND v2.0 database that satisfies the sufficient condition for separate A-U, C-G, G-U base pair counting model. The set of learned energy parameters includes experimentally measured energies of A-U, C-G, and G-U pairs; hence, our parameter set is in agreement with the Turner parameters.
]]></description>
<dc:subject>learning-by-watching structural-biology nudge-targets algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:fc85db7f46c4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:learning-by-watching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.5778">
    <title>[1310.5778] Interplay between single-stranded binding proteins on RNA secondary structure</title>
    <dc:date>2013-11-03T13:23:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.5778</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[RNA protein interactions control the fate of cellular RNAs and play an important role in gene regulation. An interdependency between such interactions allows for the implementation of logic functions in gene regulation. We investigate the interplay between RNA binding partners in the context of the statistical physics of RNA secondary structure, and define a linear correlation function between the two partners as a measurement of the interdependency of their binding events. We demonstrate the emergence of a long-range power-law behavior of this linear correlation function. This suggests RNA secondary structure driven interdependency between binding sites as a general mechanism for combinatorial post-transcriptional gene regulation.
]]></description>
<dc:subject>RNA structural-biology molecular-biology molecular-machinery bioinformatics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:4857b22685c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:RNA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1310.1579">
    <title>[1310.1579] Distribution of lifetimes of kinetochore-microtubule attachments:interplay of energy landscape, molecular motors and microtubule (de-)polymerization</title>
    <dc:date>2013-10-18T12:27:35+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.1579</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Before a cell divides into two daughter cells, the chromosomes are replicated resulting in two sister chromosomes embracing each other. Each sister chromosome is bound to a separate proteinous structure, called kinetochore (kt), that captures the tip of a filamentous protein, called microtubule (MT). Two oppositely oriented MTs pull the two kts attached to two sister chromosomes thereby pulling the two sisters away from each other. Here we theoretically study an even simpler system, namely an isolated kt coupled to a single MT; this system mimics an {\it in-vitro} experiment where a single kt-MT attachment is reconstituted using purified extracts from budding yeast. Our models not only account for the experimentally observed "catch-bond-like" behavior of the kt-MT coupling, but also make new predictions on the probability distribution of the lifetimes of the attachments. In principle, our new predictions can be tested by analyzing the data collected in the {\it in-vitro} experiments provided the experiment is repeated sufficiently large number of times. Our theory provides a deep insight into the effects of (a) size, (b) energetics, and (c) stochastic kinetics of the kt-MT coupling on the distribution of the lifetimes of these attachments.
]]></description>
<dc:subject>structural-biology cellular-biology simulation molecular-machinery cell-physiology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d7548cf5d108/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cellular-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cell-physiology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.7694">
    <title>[1309.7694] Self Organizing Maps to efficiently cluster and functionally interpret protein conformational ensembles</title>
    <dc:date>2013-10-06T13:20:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.7694</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[An approach that combines Self-Organizing maps, hierarchical clustering and network components is presented, aimed at comparing protein conformational ensembles obtained from multiple Molecular Dynamic simulations. As a first result the original ensembles can be summarized by using only the representative conformations of the clusters obtained. In addition the network components analysis allows to discover and interpret the dynamic behavior of the conformations won by each neuron. The results showed the ability of this approach to efficiently derive a functional interpretation of the protein dynamics described by the original conformational ensemble, highlighting its potential as a support for protein engineering.
]]></description>
<dc:subject>SOMs protein-folding structural-biology biophysics metaheuristics feature-extraction clustering</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:cdf3f860bb4c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:SOMs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:feature-extraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:clustering"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1308.6510">
    <title>[1308.6510] A perturbation density functional theory for the competition between inter and intramolecular association</title>
    <dc:date>2013-08-31T20:44:12+00:00</dc:date>
    <link>http://arxiv.org/abs/1308.6510</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Using the framework of Wertheim's thermodynamic perturbation theory we develop the first density functional theory which accounts for intramolecular association in chain molecules. To test the theory new Monte Carlo simulations are performed at a fluid solid interface for a 4 segment chain which can both intra and intermolecularly associate. The theory and simulation results are found to be in excellent agreement. It is shown that the inclusion of intramolecular association can have profound effects on interfacial properties such as interfacial tension and the partition coefficient.
]]></description>
<dc:subject>biophysics simulation polymer-chemistry about-damned-time structural-biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:38359628df84/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:polymer-chemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:about-damned-time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1307.1009">
    <title>[1307.1009] Systems Biophysics of Gene Expression</title>
    <dc:date>2013-07-21T14:30:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1307.1009</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Gene expression is a central process to any form of life. It involves multiple temporal and functional scales that extend from specific protein-DNA interactions to the coordinated regulation of multiple genes in response to intracellular and extracellular changes. This diversity in scales poses fundamental challenges among traditional approaches to fully understand even the simplest gene expression systems. Recent advances in computational systems biophysics have provided promising avenues to reliably integrate the molecular detail of biophysical process into the system behavior. Here, we review recent advances in the description of gene regulation as a system of biophysical processes that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. There is now basic mechanistic understanding on how promoters controlled by multiple, local and distal, DNA binding sites for transcription factors can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including precision and flexibility of the transcriptional responses.
]]></description>
<dc:subject>systems-biology structural-biology sittin-in-a-tree</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c65733ca7906/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:sittin-in-a-tree"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1306.2852">
    <title>[1306.2852] Detecting Repetitions and Periodicities in Proteins by Tiling the Structural Space</title>
    <dc:date>2013-06-17T12:28:07+00:00</dc:date>
    <link>http://arxiv.org/abs/1306.2852</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The notion of energy landscapes provides conceptual tools for understanding the complexities of protein folding and function. Energy Landscape Theory indicates that it is much easier to find sequences that satisfy the "Principle of Minimal Frustration" when the folded structure is symmetric (Wolynes, P. G. Symmetry and the Energy Landscapes of Biomolecules. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 14249-14255). Similarly, repeats and structural mosaics may be fundamentally related to landscapes with multiple embedded funnels. Here we present analytical tools to detect and compare structural repetitions in protein molecules. By an exhaustive analysis of the distribution of structural repeats using a robust metric we define those portions of a protein molecule that best describe the overall structure as a tessellation of basic units. The patterns produced by such tessellations provide intuitive representations of the repeating regions and their association towards higher order arrangements. We find that some protein architectures can be described as nearly periodic, while in others clear separations between repetitions exist. Since the method is independent of amino acid sequence information we can identify structural units that can be encoded by a variety of distinct amino acid sequences.
]]></description>
<dc:subject>structural-biology protein-folding energy-landscapes algorithms heuristics tiling interesting nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:118950cd383b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:energy-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:tiling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1305.6096">
    <title>[1305.6096] A Multiscale Factorization Method for Simulating Mesoscopic Systems with Atomic Precision</title>
    <dc:date>2013-06-17T12:20:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.6096</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Mesoscopic N-atom systems derive their structural and dynamical properties from processes coupled across multiple scales in space and time. An efficient method for understanding and simulating such systems from the underlying N-atom formulation is presented. The method integrates notions of multiscale analysis, Trotter factorization, and a hypothesis that the momenta conjugate to coarse-grained variables can be treated as a stationary random process. The method is demonstrated for Lactoferrin protein, Nudaurelia Capensis Omega Virus, and Cowpea Chlorotic Mottle Virus to assess its accuracy and scaling with system size.
]]></description>
<dc:subject>biophysics simulation structural-biology nudge-targets physics monte-carlo-modeling algorithms</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d0936f6da268/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:monte-carlo-modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.7241">
    <title>[1304.7241] pH-dependent Response of Coiled Coils: A Coarse-Grained Molecular Simulation Study</title>
    <dc:date>2013-04-30T21:56:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.7241</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In a recent work we proposed a coarse-grained methodology for studying the response of peptides when simulated at different values of pH; in this work we extend the methodology to analyze the pH-dependent behavior of coiled coils. This protein structure presents a remarkable chain stiffness andis formed by two or more long helical peptides that are interacting like the strands of a rope. Chain length and rigidity are the key aspects needed to extend previous peptide interaction potentials to this particular case; however the original model is naturally recovered when the length or the ridigity of the simulated chain are reduced. We apply the model and discuss results for two cases: (a) the folding/unfolding transition of a generic coiled coil as a function of pH; (b) behavior of a specific sequence as a function of the acidity conditions. In this latter case results are compared with experimental data from the literature in order to comment about the consistency of our approach.
]]></description>
<dc:subject>biochemistry protein-folding structural-biology engineering-design biological-engineering nudge-targets simulation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8e7ba4a2acc9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.4312">
    <title>[1212.4312] pH-dependent coarse-grained model of peptides</title>
    <dc:date>2013-04-30T21:55:33+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.4312</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We propose the first, to our knowledge, coarse-grained modeling strategy for peptides where the effect of changes of the pH can be efficiently described. The idea is based on modeling the effects of the pH value on the main driving interactions. We use reference data from atomistic simulations and experimental databases and transfer its main physical features to the coarse-grained resolution according the principle of "consistency across the scales". The coarse-grained model is refined by finding a set of parameters that, when applied to peptides with different sequences and experimental properties, reproduces the experimental and atomistic data of reference. We use the such parameterized model for performing several numerical tests to check its transferability to other systems and to prove the universality of the related modeling strategy. We have tried systems with rather different response to pH variations, showing a highly satisfactory performance of the model.
]]></description>
<dc:subject>structural-biology protein-folding biochemistry models simulation interesting</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:8d8919003693/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:interesting"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.2543">
    <title>[1303.2543] Estimation of Persistence Lengths of Semiflexible Polymers: Insight from Simulations</title>
    <dc:date>2013-04-25T11:18:13+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.2543</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The persistence length of macromolecules is one of their basic characteristics, describing their intrinsic local stiffness. However, it is difficult to extract this length from physical properties of the polymers, different recipes may give answers that disagree with each other. Monte Carlo simulations are used to elucidate this problem, giving a comparative discussion of two lattice models, the self-avoiding walk model extended by a bond bending energy, and bottle-brush polymers described by the bond fluctuation model. The conditions are discussed under which a description of such macromolecules by Kratky-Porod worm-like chains holds, and the question to what extent the persistence length depends on external conditions (such as solvent quality) is considered. The scattering function of semiflexible polymers is discussed in detail, a comparison to various analytic treatments is given, and an outlook to experimental work is presented.
]]></description>
<dc:subject>structural-biology simulation experiment nudge-targets higher-order-predictions aggregation-heuristic</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:64e7a48e1f62/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:higher-order-predictions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:aggregation-heuristic"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.4092">
    <title>[1301.4092] Encounter dynamics of a small target by a polymer diffusing in a confined domain</title>
    <dc:date>2013-04-25T11:15:42+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.4092</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study the first passage time for a polymer, that we call the narrow encounter time (NETP), to reach a small target located on the surface of a microdomain. The polymer is modeled as a Freely Joint Chain (beads connected by springs with a resting non zero length) and we use Brownian simulations to study two cases: when (i) any of the monomer or (ii) only one can be absorbed at the target window. Interestingly, we find that {in the first case} the NETP is an increasing function of the polymer length until a critical length, after which it decreases. Moreover, in the long polymer regime, we identified an exponential scaling law for the NETP as a function of the polymer length. {In the second case, the position of the absorbed monomer along the polymer chain strongly influences the NETP}. Our analysis can be applied to estimate the mean first time of a DNA fragment to a small target in the chromatin structure or for mRNA to find a small target.
]]></description>
<dc:subject>structural-biology simulation what-happens-if-project molecular-crowding nudge-targets parameter-tuning benchmarking</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:03502be8630d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:what-happens-if-project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-crowding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:parameter-tuning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:benchmarking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.6231">
    <title>[1303.6231] From mechanical folding trajectories to intrinsic energy landscapes of biopolymers</title>
    <dc:date>2013-04-21T15:09:06+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.6231</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In single molecule laser optical tweezer (LOT) pulling experiments a protein or RNA is juxtaposed between DNA handles that are attached to beads in optical traps. The LOT generates folding trajectories under force in terms of time-dependent changes in the distance between the beads. How to construct the full intrinsic folding landscape (without the handles and the beads) from the measured time series is a major unsolved problem. By using rigorous theoretical methods---which account for fluctuations of the DNA handles, rotation of the optical beads, variations in applied tension due to finite trap stiffness, as well as environmental noise and the limited bandwidth of the apparatus---we provide a tractable method to derive intrinsic free energy profiles. We validate the method by showing that the exactly calculable intrinsic free energy profile for a Generalized Rouse Model, which mimics the two-state behavior in nucleic acid hairpins, can be accurately extracted from simulated time series in a LOT setup regardless of the stiffness of the handles. We next apply the approach to trajectories from coarse grained LOT molecular simulations of a coiled-coil protein based on the GCN4 leucine zipper, and obtain a free energy landscape that is in quantitative agreement with simulations performed without the beads and handles. Finally, we extract the intrinsic free energy landscape from experimental LOT measurements for the leucine zipper, which is independent of the trap parameters.]]></description>
<dc:subject>structural-biology biological-engineering experiment energy-landscapes protein-folding</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:651b230099ee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:energy-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1304.3704">
    <title>[1304.3704] Pearls Are Self-Organized Natural Ratchets</title>
    <dc:date>2013-04-16T12:09:50+00:00</dc:date>
    <link>http://arxiv.org/abs/1304.3704</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Pearls, the most flawless and highly prized of them, are perhaps the most perfectly spherical macroscopic bodies in the biological world. How are they so round? Why are other pearls solids of revolution (off-round, drop, ringed), and yet others have no symmetry (baroque)? We find that with a spherical pearl the growth fronts of nacre are spirals and target patterns distributed across its surface, and this is true for a baroque pearl, too, but that in pearls with rotational symmetry spirals and target patterns are found only in the vicinity of the poles; elsewhere the growth fronts are arrayed in ratchet fashion around the equator. We demonstrate that pearl rotation is a self-organized phenomenon caused and sustained by physical forces from the growth fronts, and that rotating pearls are a - perhaps unique - example of a natural ratchet.]]></description>
<dc:subject>via:cshalizi self-organization biological-engineering biomaterials structural-biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:3febfc1bf268/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:via:cshalizi"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biomaterials"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1209.6379">
    <title>[1209.6379] Crowding induced entropy-enthalpy compensation in protein association equilibria</title>
    <dc:date>2013-04-14T12:00:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1209.6379</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A statistical mechanical theory is presented to predict the effects of macromolecular crowding on protein association equilibria, accounting for both excluded volume and attractive interactions between proteins and crowding molecules. Predicted binding free energies are in excellent agreement with simulation data over a wide range of crowder sizes and packing fraction. It is shown that attractive interactions between proteins and crowding agents counteract the stabilizing effects of excluded volume interactions. A critical attraction strength, for which there is no net effect of crowding, is almost independent of the crowder packing fraction.]]></description>
<dc:subject>simulation molecular-machinery structural-biology biophysics macromolecules nudge-targets experiment</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d095d98ce5d0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biophysics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:macromolecules"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.5889">
    <title>[1303.5889] A Graph Theoretic Approach to Utilizing Protein Structure to Identify Non-Random Somatic Mutations</title>
    <dc:date>2013-04-08T20:24:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.5889</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Motivation: It is well known that the development of cancer is caused by the accumulation of somatic mutations in oncogenes and tumor suppressors within the genome. For oncogenes specifically, current research suggests that there is a small set of "driver" mutations that are primarily responsible for tumorigenesis. Further, due to some recent pharmacological successes in treating these driver mutations and their resulting tumors, a variety of methods have been developed to identify potential driver mutations using methods such as machine learning and mutational clustering. We propose a novel methodology that increases our power to identify mutational clusters by taking into account protein tertiary structure via a graph theoretical approach. 
Results: We have designed and implemented a novel algorithm, GraphPAC (Graph Protein Amino acid Clustering), to identify mutational clustering while considering protein spatial structure. Using GraphPAC, we are able to detect novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of prior clustering based on current methods. Specifically, by utilizing the spatial information available in the Protein Data Bank (PDB) along with the mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC), GraphPAC identifies new mutational clusters in well known oncogenes such as EGFR and KRAS. Further, by utilizing graph theory to account for the tertiary structure, GraphPAC identifies clusters in DPP4, NRP1 and other proteins not identified by existing methods.]]></description>
<dc:subject>structural-biology bioinformatics genetics graph-theory algorithms heuristics nudge-targets cancer</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:bc1b5c4b2e0d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:graph-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:heuristics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:cancer"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1303.3898">
    <title>[1303.3898] Crumpled globule possesses the properties of molecular machines</title>
    <dc:date>2013-03-30T14:09:53+00:00</dc:date>
    <link>http://arxiv.org/abs/1303.3898</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Folding and unfolding of a crumpled polymer globule is regarded as a cascade of equilibrium phase transitions in a hierarchical system, similar to the Dyson hierarchical spin model. Studying the relaxation properties of the elastic network of contacts in a crumpled globule, we show that the dynamic properties of hierarchically folded polymer chains in globular phase are similar to those of molecular machines.]]></description>
<dc:subject>molecular-design molecular-machinery structural-biology energy-landscapes protein-folding nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:d43fda22c6ea/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:energy-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1211.6493">
    <title>[1211.6493] RNA under Tension: Folding Landscapes, Kinetic Partitioning Mechanism, and Molecular Tensegrity</title>
    <dc:date>2013-03-20T21:09:18+00:00</dc:date>
    <link>http://arxiv.org/abs/1211.6493</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Non-coding RNA sequences play a great role in controlling a number of cellular functions, thus raising the need to understand their complex conformational dynamics in quantitative detail. In this perspective, we first show that single molecule pulling experiments when combined with with theory and simulations can be used to quantitatively explore the folding landscape of nucleic acid hairpins, and riboswitches with tertiary interactions. Applications to riboswitches, which are non-coding RNA elements that control gene expression by undergoing dynamical conformational changes in response to binding of metabolites, lead to an organization principle that assembly of RNA is determined by the stability of isolated helices. We also point out the limitations of single molecule pulling experiments, with molecular extension as the only accessible parameter, in extracting key parameters of the folding landscapes of RNA molecules.]]></description>
<dc:subject>structural-biology bioinformatics macromolecules simulation control-structures emergent-design nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5ed1a5d25fe2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:macromolecules"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:control-structures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:emergent-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1003.2811">
    <title>[1003.2811] Refining the Protein-Protein Interactome using Gene Expression Data</title>
    <dc:date>2013-03-10T22:59:58+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.2811</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Proteins interact with other proteins within biological pathways, forming connected subgraphs in the protein-protein interactome (PPI). Proteins are often involved in multiple biological pathways which complicates interpretation of interactions between proteins. Gene expression data can assist our inference since genes within a particular pathway tend to have more correlated expression patterns than genes from distinct pathways. We provide an algorithm that uses gene expression information to remove inter-pathway protein-protein interactions, thereby simplifying the structure of the protein-protein interactome. This refined topology permits easier interpretation and greater biological coherence of multiple biological pathways simultaneously.]]></description>
<dc:subject>protein–protein-iteraction contact-maps GWAS structural-biology systems-biology nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:773f7caa9177/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein–protein-iteraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:contact-maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:GWAS"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1211.0662">
    <title>[1211.0662] Hidden Complexity in the Isomerization Dynamics of Holliday Junctions</title>
    <dc:date>2013-03-06T19:06:36+00:00</dc:date>
    <link>http://arxiv.org/abs/1211.0662</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[A plausible consequence of rugged energy landscapes of biomolecules is that functionally competent folded states may not be unique, as is generally assumed. Indeed, molecule-to-molecule variations in the dynamics of enzymes and ribozymes under folding conditions have recently been identified in single molecule experiments. However, systematic quantification and the structural origin of the observed complex behavior remain elusive. Even for a relatively simple case of isomerization dynamics in Holliday Junctions (HJs), molecular heterogeneities persist over a long observation time (Tobs ~ 40 sec). Here, using concepts in glass physics and complementary clustering analysis, we provide a quantitative method to analyze the smFRET data probing the isomerization in HJ dynamics. We show that ergodicity of HJ dynamics is effectively broken; as a result, the conformational space of HJs is partitioned into a folding network of kinetically disconnected clusters. While isomerization dynamics in each cluster occurs rapidly as if the associated conformational space is fully sampled, distinct patterns of time series belonging to different clusters do not interconvert on Tobs. Theory suggests that persistent heterogeneity of HJ dynamics is a consequence of internal multiloops with varying sizes and flexibilities frozen by Mg2+ ions. An annealing experiment using Mg2+ pulse that changes the Mg2+ cocentration from high to low to high values lends support to this idea by explicitly showing that interconversions can be driven among trajectories with different patterns.]]></description>
<dc:subject>molecular-design molecular-machinery biochemistry experiment protein-folding structural-biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:95b009c64a0f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:molecular-machinery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1211.1281">
    <title>[1211.1281] Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models</title>
    <dc:date>2013-02-03T14:15:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1211.1281</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional (3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D structures from such correlations is an open problem in structural biology, pursued with increasing vigor as more and more protein sequences continue to fill the data banks. Within this task lies a statistical inference problem, rooted in the following: correlation between two sites in a protein sequence can arise from firsthand interaction but can also be network-propagated via intermediate sites; observed correlation is not enough to guarantee proximity. To separate direct from indirect interactions is an instance of the general problem of inverse statistical mechanics, where the task is to learn model parameters (fields, couplings) from observables (magnetizations, correlations, samples) in large systems. In the context of protein sequences, the approach has been referred to as direct-coupling analysis. Here we show that the pseudolikelihood method, applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins, significantly outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. This improved performance also relies on a modified score for the coupling strength. The results are verified using known crystal structures of specific sequence instances of various protein families. Code implementing the new method can be found at this http URL]]></description>
<dc:subject>structural-biology multimodal-modeling inference domain-specific-knowledge nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:c7fbd7ea9e00/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:multimodal-modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:domain-specific-knowledge"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1207.3289">
    <title>[1207.3289] The Origin, Evolution and Development of Bilateral Symmetry in Multicellular Organisms</title>
    <dc:date>2012-08-04T12:29:43+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.3289</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["A computational theory and model of the ontogeny and development of bilateral symmetry in multicellular organisms is presented. Understanding the origin and evolution of bilateral organisms requires an understanding of how bilateral symmetry develops, starting from a single cell. Bilateral symmetric growth of a multicellular organism from a single starter cell is explained as resulting from the opposite handedness and orientation along one axis in two daughter founder cells that are in equivalent developmental control network states. Several methods of establishing the initial orientation of the daughter cells (including oriented cell division and cell signaling) are discussed. The orientation states of the daughter cells are epigenetically inherited by their progeny. This results in mirror development with the two founding daughter cells generating complementary mirror image multicellular morphologies. The end product is a bilateral symmetric organism. The theory gives a unified explanation of diverse phenomena including symmetry breaking, situs inversus, gynandromorphs, inside-out growth, bilaterally symmetric cancers, and the rapid, punctuated evolution of bilaterally symmetric organisms in the Cambrian Explosion. The theory is supported by experimental results on early embryonic development. The theory makes precise testable predications."]]></description>
<dc:subject>evo-devo artificial-life simulation structural-biology complexology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:40cc1da713b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:evo-devo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:artificial-life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.3789">
    <title>[1206.3789] Tree decomposition and parameterized algorithms for RNA structure-sequence alignment including tertiary interactions and pseudoknots</title>
    <dc:date>2012-07-02T22:27:51+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.3789</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We present a general setting for structure-sequence comparison in a large class of RNA structures that unifies and generalizes a number of recent works on specific families on structures. Our approach is based on tree decomposition of structures and gives rises to a general parameterized algorithm, where the exponential part of the complexity depends on the family of structures. For each of the previously studied families, our algorithm has the same complexity as the specific algorithm that had been given before."]]></description>
<dc:subject>RNA structural-biology folding bioinformatics algorithms nudge-targets modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5a557133a984/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:RNA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:folding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.1098">
    <title>[1206.1098] The interplay of intrinsic and extrinsic bounded noises in genetic networks</title>
    <dc:date>2012-06-08T15:09:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.1098</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a genetic network. The influence of intrinsic and extrinsic noises on genetic networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. 

We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: $(i)$ the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, $(ii)$ a model of enzymatic futile cycle and $(iii)$ a genetic toggle switch. In $(ii)$ and $(iii)$ we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possibile functional role of bounded noises."]]></description>
<dc:subject>biochemistry structural-biology reaction-networks biological-engineering noise its-complicated-inside-a-cell simulation nudge-targets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:5b5907dd51b0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biochemistry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:reaction-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:noise"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:its-complicated-inside-a-cell"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.0766">
    <title>[1206.0766] Why Optimal States Recruit Fewer Reactions in Metabolic Networks</title>
    <dc:date>2012-06-07T11:36:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.0766</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["The metabolic network of a living cell involves several hundreds or thousands of interconnected biochemical reactions. Previous research has shown that under realistic conditions only a fraction of these reactions is concurrently active in any given cell. This is partially determined by nutrient availability, but is also strongly dependent on the metabolic function and network structure. Here, we establish rigorous bounds showing that the fraction of active reactions is smaller (rather than larger) in metabolic networks evolved or engineered to optimize a specific metabolic task, and we show that this is largely determined by the presence of thermodynamically irreversible reactions in the network. We also show that the inactivation of a certain number of reactions determined by irreversibility can generate a cascade of secondary reaction inactivations that propagates through the network. The mathematical results are complemented with numerical simulations of the metabolic networks of the bacterium Escherichia coli and of human cells, which show, counterintuitively, that even the maximization of the total reaction flux in the network leads to a reduced number of active reactions."]]></description>
<dc:subject>network-theory biological-engineering metabolic-networks systems-biology engineering-design structural-biology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:39a7aeaeefae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:network-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metabolic-networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:systems-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:engineering-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1203.3353">
    <title>[1203.3353] Solving Structure with Sparse, Randomly-Oriented X-ray Data</title>
    <dc:date>2012-03-18T10:21:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1203.3353</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Single-particle imaging experiments of biomolecules at x-ray free-electron lasers (XFELs) require processing of hundreds of thousands (or more) of images that contain very few x-rays. Each low-flux image of the diffraction pattern is produced by a single, randomly oriented particle, such as a protein. We demonstrate the feasibility of collecting data at these extremes, averaging only 2.5 photons per frame, where it seems doubtful there could be information about the state of rotation, let alone the image contrast. This is accomplished with an expectation maximization algorithm that processes the low-flux data in aggregate, and without any prior knowledge of the object or its orientation. The versatility of the method promises, more generally, to redefine what measurement scenarios can provide useful signal in the high-noise regime."]]></description>
<dc:subject>structural-biology image-analysis crystallography algorithms inverse-problems nudge-targets statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:66baf5a1cb02/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:image-analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:inverse-problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>