Predicting Peptide Structures in Native Proteins from Physical Simulations of Fragments
Identifieur interne : 002774 ( Main/Exploration ); précédent : 002773; suivant : 002775Predicting Peptide Structures in Native Proteins from Physical Simulations of Fragments
Auteurs : Vincent A. Voelz [États-Unis] ; M. Scott Shell [États-Unis] ; Ken A. Dill [États-Unis]Source :
- PLoS Computational Biology [ 1553-734X ] ; 2009.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- Artificial Intelligence, Bayes Theorem, Computer Simulation, Logistic Models, Models, Chemical, Models, Molecular, Protein Conformation, Protein Folding, Proteins (chemistry), Proteins (ultrastructure), Proteomics (methods), Solvents (chemistry), Thermodynamics, Water (chemistry), Weights and Measures.
- MESH :
- chemical , chemistry : Proteins, Solvents, Water.
- chemical , ultrastructure : Proteins.
- methods : Proteomics.
- Artificial Intelligence, Bayes Theorem, Computer Simulation, Logistic Models, Models, Chemical, Models, Molecular, Protein Conformation, Protein Folding, Thermodynamics, Weights and Measures.
Abstract
It has long been proposed that much of the information encoding how a protein folds is contained locally in the peptide chain. Here we present a large-scale simulation study designed to examine the extent to which conformations of peptide fragments in water predict native conformations in proteins. We perform replica exchange molecular dynamics (REMD) simulations of 872 8-mer, 12-mer, and 16-mer peptide fragments from 13 proteins using the AMBER 96 force field and the OBC implicit solvent model. To analyze the simulations, we compute various contact-based metrics, such as contact probability, and then apply Bayesian classifier methods to infer which metastable contacts are likely to be native vs. non-native. We find that a simple measure, the observed contact probability, is largely more predictive of a peptide's native structure in the protein than combinations of metrics or multi-body components. Our best classification model is a logistic regression model that can achieve up to 63% correct classifications for 8-mers, 71% for 12-mers, and 76% for 16-mers. We validate these results on fragments of a protein outside our training set. We conclude that local structure provides information to solve some but not all of the conformational search problem. These results help improve our understanding of folding mechanisms, and have implications for improving physics-based conformational sampling and structure prediction using all-atom molecular simulations.
Url:
DOI: 10.1371/journal.pcbi.1000281
PubMed: 19197352
PubMed Central: 2629132
Affiliations:
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Voelz</name><affiliation wicri:level="2"><nlm:aff id="aff1"><addr-line>Department of Chemistry, Stanford University, Stanford, California, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemistry, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Shell, M Scott" sort="Shell, M Scott" uniqKey="Shell M" first="M. Scott" last="Shell">M. Scott Shell</name><affiliation wicri:level="4"><nlm:aff id="aff2"><addr-line>Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Santa Barbara (Californie)</settlement></placeName><orgName type="university">Université de Californie à Santa Barbara</orgName></affiliation></author><author><name sortKey="Dill, Ken A" sort="Dill, Ken A" uniqKey="Dill K" first="Ken A." last="Dill">Ken A. Dill</name><affiliation wicri:level="2"><nlm:aff id="aff3"><addr-line>Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, United States of America</addr-line></nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></analytic><series><title level="j">PLoS Computational Biology</title><idno type="ISSN">1553-734X</idno><idno type="eISSN">1553-7358</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial Intelligence</term><term>Bayes Theorem</term><term>Computer Simulation</term><term>Logistic Models</term><term>Models, Chemical</term><term>Models, Molecular</term><term>Protein Conformation</term><term>Protein Folding</term><term>Proteins (chemistry)</term><term>Proteins (ultrastructure)</term><term>Proteomics (methods)</term><term>Solvents (chemistry)</term><term>Thermodynamics</term><term>Water (chemistry)</term><term>Weights and Measures</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Conformation des protéines</term><term>Eau ()</term><term>Intelligence artificielle</term><term>Modèles chimiques</term><term>Modèles logistiques</term><term>Modèles moléculaires</term><term>Pliage des protéines</term><term>Poids et mesures</term><term>Protéines ()</term><term>Protéines (ultrastructure)</term><term>Protéomique ()</term><term>Simulation numérique</term><term>Solvants ()</term><term>Thermodynamique</term><term>Théorème de Bayes</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Proteins</term><term>Solvents</term><term>Water</term></keywords><keywords scheme="MESH" type="chemical" qualifier="ultrastructure" xml:lang="en"><term>Proteins</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Proteomics</term></keywords><keywords scheme="MESH" qualifier="ultrastructure" xml:lang="fr"><term>Protéines</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Artificial Intelligence</term><term>Bayes Theorem</term><term>Computer Simulation</term><term>Logistic Models</term><term>Models, Chemical</term><term>Models, Molecular</term><term>Protein Conformation</term><term>Protein Folding</term><term>Thermodynamics</term><term>Weights and Measures</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Conformation des protéines</term><term>Eau</term><term>Intelligence artificielle</term><term>Modèles chimiques</term><term>Modèles logistiques</term><term>Modèles moléculaires</term><term>Pliage des protéines</term><term>Poids et mesures</term><term>Protéines</term><term>Protéomique</term><term>Simulation numérique</term><term>Solvants</term><term>Thermodynamique</term><term>Théorème de Bayes</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>It has long been proposed that much of the information encoding how a protein folds is contained locally in the peptide chain. Here we present a large-scale simulation study designed to examine the extent to which conformations of peptide fragments in water predict native conformations in proteins. We perform replica exchange molecular dynamics (REMD) simulations of 872 8-mer, 12-mer, and 16-mer peptide fragments from 13 proteins using the AMBER 96 force field and the OBC implicit solvent model. To analyze the simulations, we compute various contact-based metrics, such as contact probability, and then apply Bayesian classifier methods to infer which metastable contacts are likely to be native vs. non-native. We find that a simple measure, the observed contact probability, is largely more predictive of a peptide's native structure in the protein than combinations of metrics or multi-body components. Our best classification model is a logistic regression model that can achieve up to 63% correct classifications for 8-mers, 71% for 12-mers, and 76% for 16-mers. We validate these results on fragments of a protein outside our training set. We conclude that local structure provides information to solve some but not all of the conformational search problem. These results help improve our understanding of folding mechanisms, and have implications for improving physics-based conformational sampling and structure prediction using all-atom molecular simulations.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Simons, K" uniqKey="Simons K">K Simons</name></author><author><name sortKey="Kooperberg, C" uniqKey="Kooperberg C">C Kooperberg</name></author><author><name sortKey="Huang, E" uniqKey="Huang E">E Huang</name></author><author><name sortKey="Baker, D" uniqKey="Baker D">D Baker</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rohl, C" uniqKey="Rohl C">C Rohl</name></author><author><name sortKey="Strauss, C" uniqKey="Strauss C">C Strauss</name></author><author><name sortKey="Misura, K" uniqKey="Misura K">K Misura</name></author><author><name sortKey="Baker, D" uniqKey="Baker D">D 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uniqKey="Brenner S">SE Brenner</name></author><author><name sortKey="Murzin, Ag" uniqKey="Murzin A">AG Murzin</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Californie</li></region><settlement><li>Santa Barbara (Californie)</li></settlement><orgName><li>Université de Californie à Santa Barbara</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Voelz, Vincent A" sort="Voelz, Vincent A" uniqKey="Voelz V" first="Vincent A." last="Voelz">Vincent A. Voelz</name></region><name sortKey="Dill, Ken A" sort="Dill, Ken A" uniqKey="Dill K" first="Ken A." last="Dill">Ken A. Dill</name><name sortKey="Shell, M Scott" sort="Shell, M Scott" uniqKey="Shell M" first="M. Scott" last="Shell">M. Scott Shell</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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