Exploring the conformational transitions of biomolecular systems using a simple two-state anisotropic network model.
Identifieur interne : 003362 ( PubMed/Curation ); précédent : 003361; suivant : 003363Exploring the conformational transitions of biomolecular systems using a simple two-state anisotropic network model.
Auteurs : Avisek Das [États-Unis] ; Mert Gur [États-Unis] ; Mary Hongying Cheng [États-Unis] ; Sunhwan Jo [États-Unis] ; Ivet Bahar [États-Unis] ; Benoît Roux [États-Unis]Source :
- PLoS computational biology [ 1553-7358 ] ; 2014.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- chemical , chemistry : Adenylate Kinase, Amino Acid Transport System X-AG, Carrier Proteins, Leucine, Sarcoplasmic Reticulum Calcium-Transporting ATPases.
- enzymology : Sarcoplasmic Reticulum.
- Models, Molecular, Protein Conformation.
Abstract
Biomolecular conformational transitions are essential to biological functions. Most experimental methods report on the long-lived functional states of biomolecules, but information about the transition pathways between these stable states is generally scarce. Such transitions involve short-lived conformational states that are difficult to detect experimentally. For this reason, computational methods are needed to produce plausible hypothetical transition pathways that can then be probed experimentally. Here we propose a simple and computationally efficient method, called ANMPathway, for constructing a physically reasonable pathway between two endpoints of a conformational transition. We adopt a coarse-grained representation of the protein and construct a two-state potential by combining two elastic network models (ENMs) representative of the experimental structures resolved for the endpoints. The two-state potential has a cusp hypersurface in the configuration space where the energies from both the ENMs are equal. We first search for the minimum energy structure on the cusp hypersurface and then treat it as the transition state. The continuous pathway is subsequently constructed by following the steepest descent energy minimization trajectories starting from the transition state on each side of the cusp hypersurface. Application to several systems of broad biological interest such as adenylate kinase, ATP-driven calcium pump SERCA, leucine transporter and glutamate transporter shows that ANMPathway yields results in good agreement with those from other similar methods and with data obtained from all-atom molecular dynamics simulations, in support of the utility of this simple and efficient approach. Notably the method provides experimentally testable predictions, including the formation of non-native contacts during the transition which we were able to detect in two of the systems we studied. An open-access web server has been created to deliver ANMPathway results.
DOI: 10.1371/journal.pcbi.1003521
PubMed: 24699246
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Pennsylvania</wicri:regionArea></affiliation></author><author><name sortKey="Jo, Sunhwan" sort="Jo, Sunhwan" uniqKey="Jo S" first="Sunhwan" last="Jo">Sunhwan Jo</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, University of Chicago, Chicago, Illinois, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, University of Chicago, Chicago, Illinois</wicri:regionArea></affiliation></author><author><name sortKey="Bahar, Ivet" sort="Bahar, Ivet" uniqKey="Bahar I" first="Ivet" last="Bahar">Ivet Bahar</name><affiliation wicri:level="1"><nlm:affiliation>Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania</wicri:regionArea></affiliation></author><author><name sortKey="Roux, Benoit" sort="Roux, Benoit" uniqKey="Roux B" first="Benoît" last="Roux">Benoît Roux</name><affiliation wicri:level="1"><nlm:affiliation>Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, University of Chicago, Chicago, Illinois, United States of America.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, University of Chicago, Chicago, Illinois</wicri:regionArea></affiliation></author></analytic><series><title level="j">PLoS computational biology</title><idno type="eISSN">1553-7358</idno><imprint><date when="2014" type="published">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adenylate Kinase (chemistry)</term><term>Amino Acid Transport System X-AG (chemistry)</term><term>Carrier Proteins (chemistry)</term><term>Leucine (chemistry)</term><term>Models, Molecular</term><term>Protein Conformation</term><term>Sarcoplasmic Reticulum (enzymology)</term><term>Sarcoplasmic Reticulum Calcium-Transporting ATPases (chemistry)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adenylate kinase ()</term><term>Conformation des protéines</term><term>Leucine ()</term><term>Modèles moléculaires</term><term>Protéines de transport ()</term><term>Réticulum sarcoplasmique (enzymologie)</term><term>Sarcoplasmic Reticulum Calcium-Transporting ATPases ()</term><term>Système X-AG de transport d'acides aminés ()</term></keywords><keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en"><term>Adenylate Kinase</term><term>Amino Acid Transport System X-AG</term><term>Carrier Proteins</term><term>Leucine</term><term>Sarcoplasmic Reticulum Calcium-Transporting ATPases</term></keywords><keywords scheme="MESH" qualifier="enzymologie" xml:lang="fr"><term>Réticulum sarcoplasmique</term></keywords><keywords scheme="MESH" qualifier="enzymology" xml:lang="en"><term>Sarcoplasmic Reticulum</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Models, Molecular</term><term>Protein Conformation</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adenylate kinase</term><term>Conformation des protéines</term><term>Leucine</term><term>Modèles moléculaires</term><term>Protéines de transport</term><term>Sarcoplasmic Reticulum Calcium-Transporting ATPases</term><term>Système X-AG de transport d'acides aminés</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Biomolecular conformational transitions are essential to biological functions. Most experimental methods report on the long-lived functional states of biomolecules, but information about the transition pathways between these stable states is generally scarce. Such transitions involve short-lived conformational states that are difficult to detect experimentally. For this reason, computational methods are needed to produce plausible hypothetical transition pathways that can then be probed experimentally. Here we propose a simple and computationally efficient method, called ANMPathway, for constructing a physically reasonable pathway between two endpoints of a conformational transition. We adopt a coarse-grained representation of the protein and construct a two-state potential by combining two elastic network models (ENMs) representative of the experimental structures resolved for the endpoints. The two-state potential has a cusp hypersurface in the configuration space where the energies from both the ENMs are equal. We first search for the minimum energy structure on the cusp hypersurface and then treat it as the transition state. The continuous pathway is subsequently constructed by following the steepest descent energy minimization trajectories starting from the transition state on each side of the cusp hypersurface. Application to several systems of broad biological interest such as adenylate kinase, ATP-driven calcium pump SERCA, leucine transporter and glutamate transporter shows that ANMPathway yields results in good agreement with those from other similar methods and with data obtained from all-atom molecular dynamics simulations, in support of the utility of this simple and efficient approach. Notably the method provides experimentally testable predictions, including the formation of non-native contacts during the transition which we were able to detect in two of the systems we studied. An open-access web server has been created to deliver ANMPathway results.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24699246</PMID><DateCreated><Year>2014</Year><Month>04</Month><Day>04</Day></DateCreated><DateCompleted><Year>2014</Year><Month>12</Month><Day>08</Day></DateCompleted><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1553-7358</ISSN><JournalIssue CitedMedium="Internet"><Volume>10</Volume><Issue>4</Issue><PubDate><Year>2014</Year><Month>Apr</Month></PubDate></JournalIssue><Title>PLoS computational biology</Title><ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>Exploring the conformational transitions of biomolecular systems using a simple two-state anisotropic network model.</ArticleTitle><Pagination><MedlinePgn>e1003521</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1003521</ELocationID><Abstract><AbstractText>Biomolecular conformational transitions are essential to biological functions. Most experimental methods report on the long-lived functional states of biomolecules, but information about the transition pathways between these stable states is generally scarce. Such transitions involve short-lived conformational states that are difficult to detect experimentally. For this reason, computational methods are needed to produce plausible hypothetical transition pathways that can then be probed experimentally. Here we propose a simple and computationally efficient method, called ANMPathway, for constructing a physically reasonable pathway between two endpoints of a conformational transition. We adopt a coarse-grained representation of the protein and construct a two-state potential by combining two elastic network models (ENMs) representative of the experimental structures resolved for the endpoints. The two-state potential has a cusp hypersurface in the configuration space where the energies from both the ENMs are equal. We first search for the minimum energy structure on the cusp hypersurface and then treat it as the transition state. The continuous pathway is subsequently constructed by following the steepest descent energy minimization trajectories starting from the transition state on each side of the cusp hypersurface. Application to several systems of broad biological interest such as adenylate kinase, ATP-driven calcium pump SERCA, leucine transporter and glutamate transporter shows that ANMPathway yields results in good agreement with those from other similar methods and with data obtained from all-atom molecular dynamics simulations, in support of the utility of this simple and efficient approach. Notably the method provides experimentally testable predictions, including the formation of non-native contacts during the transition which we were able to detect in two of the systems we studied. An open-access web server has been created to deliver ANMPathway results.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Das</LastName><ForeName>Avisek</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, University of Chicago, Chicago, Illinois, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gur</LastName><ForeName>Mert</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cheng</LastName><ForeName>Mary Hongying</ForeName><Initials>MH</Initials><AffiliationInfo><Affiliation>Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Jo</LastName><ForeName>Sunhwan</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, University of Chicago, Chicago, Illinois, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bahar</LastName><ForeName>Ivet</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Roux</LastName><ForeName>Benoît</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>Department of Biochemistry and Molecular Biology, Gordon Center for Integrative Science, University of Chicago, Chicago, Illinois, United States of America.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 GM086238</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>P41GM103712</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01GM086238</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>U54 GM087519</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01-GM099738</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>P41 GM103712</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01 GM099738</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>U54-GM087519</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2014</Year><Month>04</Month><Day>03</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS Comput Biol</MedlineTA><NlmUniqueID>101238922</NlmUniqueID><ISSNLinking>1553-734X</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D027322">Amino Acid Transport System X-AG</NameOfSubstance></Chemical><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D002352">Carrier Proteins</NameOfSubstance></Chemical><Chemical><RegistryNumber>EC 2.7.4.3</RegistryNumber><NameOfSubstance UI="D000263">Adenylate Kinase</NameOfSubstance></Chemical><Chemical><RegistryNumber>EC 3.6.3.8</RegistryNumber><NameOfSubstance UI="D053498">Sarcoplasmic Reticulum Calcium-Transporting ATPases</NameOfSubstance></Chemical><Chemical><RegistryNumber>GMW67QNF9C</RegistryNumber><NameOfSubstance UI="D007930">Leucine</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Proc Natl Acad Sci U S A. 2007 Nov 20;104(47):18496-501</RefSource><PMID Version="1">18000050</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Biophys J. 2012 Mar 21;102(6):1331-40</RefSource><PMID Version="1">22455916</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>PLoS Comput Biol. 2008 Mar;4(3):e1000047</RefSource><PMID 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MajorTopicYN="N">chemistry</QualifierName></MeshHeading></MeshHeadingList><OtherID Source="NLM">PMC3974643</OtherID></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year><Month>09</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2014</Year><Month>02</Month><Day>01</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>4</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>4</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>12</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24699246</ArticleId><ArticleId IdType="doi">10.1371/journal.pcbi.1003521</ArticleId><ArticleId IdType="pii">PCOMPBIOL-D-13-01573</ArticleId><ArticleId 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