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  </channel><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>
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<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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<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>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:protein-folding"/>
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<item rdf:about="https://arxiv.org/abs/0706.1754">
    <title>[0706.1754] Protein structure prediction by an iterative search method</title>
    <dc:date>2020-01-14T21:41:45+00:00</dc:date>
    <link>https://arxiv.org/abs/0706.1754</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We demonstrate a new algorithm for finding protein conformations that minimize a non-bonded energy function. The new algorithm, called the difference map, seeks to find an atomic configuration that is simultaneously in two constraint spaces. The first constraint space is the space of atomic configurations that have a valid peptide geometry, while the second is the space of configurations that have a non-bonded energy below a given target. These two constraint spaces are used to define a deterministic dynamical system, whose fixed points produce atomic configurations in the intersection of the two constraint spaces. The rate at which the difference map produces low energy protein conformations is compared with that of a contemporary search algorithm, parallel tempering. The results indicate the difference map finds low energy protein conformations at a significantly higher rate then parallel tempering.
]]></description>
<dc:subject>protein-folding heuristics hill-climbing rather-interesting metaheuristics old to-write-about to-simulate</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a9a0f4f2c4d9/</dc:identifier>
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<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>
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<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>
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<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>
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<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"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:distance-measure"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1601.03420">
    <title>[1601.03420] Critical fluctuations in proteins native states</title>
    <dc:date>2016-02-24T23:37:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1601.03420</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We study a large data set of protein structure ensembles of very diverse sizes determined by nuclear magnetic resonance. By examining the distance-dependent correlations in the displacement of residues pairs and conducting finite size scaling analysis it was found that the correlations and susceptibility behave as in systems near a critical point implying that, at the native state, the motion of each amino acid residue is felt by every other residue up to the size of the protein molecule. Furthermore certain protein's shapes corresponding to maximum susceptibility were found to be more probable than others. Overall the results suggest that the protein's native state is critical, implying that despite being posed near the minimum of the energy landscape, they still preserve their dynamic flexibility.
]]></description>
<dc:subject>biophysics protein-folding physics phase-transitions self-organization looking-to-see nonlinear-dynamics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:71906633e624/</dc:identifier>
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<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>
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	<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/1505.05709">
    <title>[1505.05709] A Conformational Search Method for Protein Systems Using Genetic Crossover and Metropolis Criterion</title>
    <dc:date>2015-05-26T10:59:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1505.05709</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Many proteins carry out their biological functions by forming the characteristic tertiary structures. Therefore, the search of the stable states of proteins by molecular simulations is important to understand their functions and stabilities. However, getting the stable state by conformational search is difficult, because the energy landscape of the system is characterized by many local minima separated by high energy barriers. In order to overcome this difficulty, various sampling and optimization methods for conformations of proteins have been proposed. In this study, we propose a new conformational search method for proteins by using genetic crossover and Metropolis criterion. We applied this method to an α-helical protein. The conformations obtained from the simulations are in good agreement with the experimental results.
]]></description>
<dc:subject>metaheuristics protein-folding energy-landscapes nudge-targets simulation meh?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:1be80c6869cf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:metaheuristics"/>
	<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:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meh?"/>
</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/1310.5091">
    <title>[1310.5091] Synchronization as a unifying mechanism for protein folding</title>
    <dc:date>2014-11-09T12:21:24+00:00</dc:date>
    <link>http://arxiv.org/abs/1310.5091</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Different models such as diffusion-collision and nucleation-condensation have been used to unravel how secondary and tertiary structures form during protein folding. However, a simple mechanism based on physical principles that provide an accurate description of kinetics and thermodynamics for such phenomena has not yet been identified. This study introduces the hypothesis that the synchronization of the peptide plane oscillatory movements throughout the backbone must also play a key role in the folding mechanism. Based on that, we draw a parallel between the folding process and the dynamics for a network of coupled oscillators described by the Kuramoto model. The amino acid coupling may explain the mean-field character of the force that propels an amino acid sequence into a structure through self-organization. Thus, the pattern of synchronized cluster formation and growing helps to solve the Levinthal's paradox.Synchronization may also help us to understand the success of homology structural modeling, allosteric effect, and the mechanism responsible for the recognition of odorants by olfactory receptors.
]]></description>
<dc:subject>protein-folding molecular-design coupled-oscillators simulation coordination rather-interesting nudge-targets consider:detectors</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:32a42af2d6c6/</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:molecular-design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coupled-oscillators"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:coordination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:rather-interesting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:detectors"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1409.3397">
    <title>[1409.3397] Are there Unfoldable Proteins in Dimension Three?</title>
    <dc:date>2014-09-24T11:03:41+00:00</dc:date>
    <link>http://arxiv.org/abs/1409.3397</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper we show the existence of three dimensional rigid, and thus unfoldable, lattice conformations. The structure we found has 450+ bonds, and we provide a computer assisted proof of the existence of such structures. The existence of such rigid structures illustrates why protein folding problems are hard also in dimension three. The existence of two rigid two dimensional structures was shown earlier. This work answers question 8 in \cite{2D} in the affirmative: rigid self avoiding walks exist in three dimensional lattice configurations.
]]></description>
<dc:subject>protein-folding Dill-models biophysics simulation abstraction proof-of-concept nudge-targets foldability biological-engineering consider:performance-criteria</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:73326b64d2ed/</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:Dill-models"/>
	<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:abstraction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:proof-of-concept"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:foldability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:biological-engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:performance-criteria"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.1301">
    <title>[1311.1301] Prediction of residue-residue contacts from protein families using similarity kernels and least squares regularization</title>
    <dc:date>2014-04-07T12:06:37+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.1301</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[One of the most challenging and long-standing problems in computational biology is the prediction of three-dimensional protein structure from amino acid sequence. A promising approach to infer spatial proximity between residues is the study of evolutionary covariance from multiple sequence alignments, especially in light of recent algorithmic improvements and the fast growing size of sequence databases. 
In this paper, we present a simple, fast and accurate algorithm for the prediction of residue-residue contacts based on regularized least squares. The method incorporates in a very natural manner amino acid similarity in the calculation of covariance, and accounts for low number of observations by a regularization parameter that depends on the effective number of sequences in the alignment. Most importantly, inversion of the sample covariance matrix allows the computation of partial correlations between pairs of residues, thereby removing the effect of spurious transitive correlations. When tested on a set of protein families from PFAM, we found the RLS algorithm to have superior performance compared to PSICOV, a state-of-the-art method for contact prediction.
]]></description>
<dc:subject>protein-folding machine-learning partial-solutions prediction nudge-targets consider:more details</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:a5f9740f8e49/</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:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:partial-solutions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:consider:more"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:details"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.0867">
    <title>[1312.0867] Frustration in Biomolecules</title>
    <dc:date>2014-03-30T11:28:16+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.0867</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Biomolecules are the prime information processing elements of living matter. Most of these inanimate systems are polymers that compute their structures and dynamics using as input seemingly random character strings of their sequence, following which they coalesce and perform integrated cellular functions. In large computational systems with a finite interaction-codes, the appearance of conflicting goals is inevitable. Simple conflicting forces can lead to quite complex structures and behaviors, leading to the concept of "frustration" in condensed matter. We present here some basic ideas about frustration in biomolecules and how the frustration concept leads to a better appreciation of many aspects of the architecture of biomolecules, and how structure connects to function. These ideas are simultaneously both seductively simple and perilously subtle to grasp completely. The energy landscape theory of protein folding provides a framework for quantifying frustration in large systems and has been implemented at many levels of description. We first review the notion of frustration from the areas of abstract logic and its uses in simple condensed matter systems. We discuss then how the frustration concept applies specifically to heteropolymers, testing folding landscape theory in computer simulations of protein models and in experimentally accessible systems. Studying the aspects of frustration averaged over many proteins provides ways to infer energy functions useful for reliable structure prediction. We discuss how frustration affects folding, how a large part of the biological functions of proteins are related to subtle local frustration effects and how frustration influences the appearance of metastable states, the nature of binding processes, catalysis and allosteric transitions. We hope to illustrate how Frustration is a fundamental concept in relating function to structural biology.
]]></description>
<dc:subject>protein-folding energy-landscapes biophysics simulation entropy complexology</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:83b56a9e9f3a/</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:energy-landscapes"/>
	<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:entropy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
</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/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/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/1207.6431">
    <title>[1207.6431] Optimal reconstruction of the folding landscape using differential energy surface analysis</title>
    <dc:date>2013-05-25T11:10:38+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.6431</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In experiments and in simulations, the free energy of a state of a system can be determined from the probability that the state is occupied. However, it is often necessary to impose a biasing potential on the system so that high energy states are sampled with sufficient frequency. The unbiased energy is typically obtained from the data using the weighted histogram analysis method (WHAM). Here we present differential energy surface analysis (DESA), in which the gradient of the energy surface, dE/dx, is extracted from data taken with a series of harmonic biasing potentials. It is shown that DESA produces a maximum likelihood estimate of the folding landscape gradient. DESA is demonstrated by analyzing data from a simulated system as well as data from a single-molecule unfolding experiment in which the end-to-end distance of a DNA hairpin is measured. It is shown that the energy surface obtained from DESA is indistinguishable from the energy surface obtained when WHAM is applied to the same data. Two criteria are defined which indicate whether the DESA results are self-consistent. It is found that these criteria can detect a situation where the energy is not a single-valued function of the measured reaction coordinate. The criteria were found to be satisfied for the experimental data analyzed, confirming that end-to-end distance is a good reaction coordinate for the experimental system. The combination of DESA and the optical trap assay in which a structure is disrupted under harmonic constraint facilitates an extremely accurate measurement of the folding energy surface.
]]></description>
<dc:subject>biochemistry protein-folding fitness-landscapes nudge-targets algorithms performance-measure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:33b681fa8b38/</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:fitness-landscapes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:performance-measure"/>
</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/1303.6231">
    <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/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/1211.0662">
    <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/1206.6332">
    <title>[1206.6332] Characterizing protein crystal contacts and their role in protein crystallization</title>
    <dc:date>2012-08-29T12:33:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.6332</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Crystallography may be the gold standard of protein structure determination, but obtaining the necessary high-quality crystals is akin to prospecting for the precious mineral. The fields of structural biology and soft matter have independently sought out fundamental principles to rationalize the process, but the conceptual differences and the limited crosstalk between the two disciplines have prevented a comprehensive understanding of the phenomenon to emerge. Here we conduct a computational study of proteins from the rubredoxin family that bridges the two fields. Using atomistic simulations, we characterize the crystal contacts, and then parameterize patchy particle models. Comparing the phase diagrams of these models with experimental results enables us to critically examine the assumptions behind the two approaches and to reveal key features of protein-protein interactions that facilitate their crystallization.]]></description>
<dc:subject>protein-folding simulation experiment meta-modeling nudge-targets proxy-modeling</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:77b055aa8367/</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:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:experiment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:meta-modeling"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nudge-targets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:proxy-modeling"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1102.5694">
    <title>[1102.5694] Evolutionary Dynamics in a Simple Model of Self-Assembly</title>
    <dc:date>2011-04-02T12:11:10+00:00</dc:date>
    <link>http://arxiv.org/abs/1102.5694</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We investigate the evolutionary dynamics of an idealised model for the robust self-assembly of two-dimensional structures called polyominoes. The model includes rules that encode interactions between sets of square tiles that drive the self-assembly process. The relationship between the model's rule set and its resulting self-assembled structure can be viewed as a genotype-phenotype map and incorporated into a genetic algorithm."]]></description>
<dc:subject>self-assembly genetic-programming genetic-algorithm nanotechnology complexology protein-folding nudge-targets</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:504e3efd868b/</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:genetic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:genetic-algorithm"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:nanotechnology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:complexology"/>
	<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://precedings.nature.com/documents/1490/version/1">
    <title>Understanding Hydrogen-Bond Patterns in Proteins using a Novel Statistical Model</title>
    <dc:date>2008-01-06T14:45:38+00:00</dc:date>
    <link>http://precedings.nature.com/documents/1490/version/1</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[Bond motifs.
]]></description>
<dc:subject>protein-folding crystallography machine-learning pattern-discovery data-mining bioinformatics structural-biology</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:aee500c2adfa/</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:crystallography"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:pattern-discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:bioinformatics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:Vaguery/t:structural-biology"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>