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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Predictive energy landscapes for folding α-helical transmembrane proteins</title><author><name sortKey="Kim, Bobby L" sort="Kim, Bobby L" uniqKey="Kim B" first="Bobby L." last="Kim">Bobby L. Kim</name></author><author><name sortKey="Schafer, Nicholas P" sort="Schafer, Nicholas P" uniqKey="Schafer N" first="Nicholas P." last="Schafer">Nicholas P. Schafer</name></author><author><name sortKey="Wolynes, Peter G" sort="Wolynes, Peter G" uniqKey="Wolynes P" first="Peter G." last="Wolynes">Peter G. Wolynes</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25030446</idno><idno type="pmc">4121805</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4121805</idno><idno type="RBID">PMC:4121805</idno><idno type="doi">10.1073/pnas.1410529111</idno><date when="2014">2014</date><idno type="wicri:Area/Pmc/Corpus">000411</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Predictive energy landscapes for folding α-helical transmembrane proteins</title><author><name sortKey="Kim, Bobby L" sort="Kim, Bobby L" uniqKey="Kim B" first="Bobby L." last="Kim">Bobby L. Kim</name></author><author><name sortKey="Schafer, Nicholas P" sort="Schafer, Nicholas P" uniqKey="Schafer N" first="Nicholas P." last="Schafer">Nicholas P. Schafer</name></author><author><name sortKey="Wolynes, Peter G" sort="Wolynes, Peter G" uniqKey="Wolynes P" first="Peter G." last="Wolynes">Peter G. Wolynes</name></author></analytic><series><title level="j">Proceedings of the National Academy of Sciences of the United States of America</title><idno type="ISSN">0027-8424</idno><idno type="eISSN">1091-6490</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><title>Significance</title><p>The understanding of how membrane proteins fold pales in comparison with the understanding of globular protein folding. This discrepancy is partly due to the fact that membrane proteins are difficult to work with experimentally. In turn, the lack of high-quality experimental data has caused modeling of membrane proteins to lag behind. Also, the extent to which the translocon assists transmembrane domains in folding is unclear. The number of experimentally determined membrane protein structures has recently increased, and we may now be at the stage where it has become possible to derive transferable simulation models for studying transmembrane protein folding. We describe the optimization of one such model and its application to predicting helical packings within the native topology.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Proc Natl Acad Sci U S A</journal-id><journal-id journal-id-type="iso-abbrev">Proc. Natl. Acad. Sci. U.S.A</journal-id><journal-id journal-id-type="hwp">pnas</journal-id><journal-id journal-id-type="pmc">pnas</journal-id><journal-id journal-id-type="publisher-id">PNAS</journal-id><journal-title-group><journal-title>Proceedings of the National Academy of Sciences of the United States of America</journal-title></journal-title-group><issn pub-type="ppub">0027-8424</issn><issn pub-type="epub">1091-6490</issn><publisher><publisher-name>National Academy of Sciences</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">25030446</article-id><article-id pub-id-type="pmc">4121805</article-id><article-id pub-id-type="publisher-id">201410529</article-id><article-id pub-id-type="doi">10.1073/pnas.1410529111</article-id><article-categories><subj-group subj-group-type="heading"><subject>Biological Sciences</subject><subj-group><subject>Biophysics and Computational Biology</subject></subj-group></subj-group></article-categories><title-group><article-title>Predictive energy landscapes for folding α-helical transmembrane proteins</article-title><alt-title alt-title-type="short">Energy landscapes of α-helical membrane proteins</alt-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Kim</surname><given-names>Bobby L.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Schafer</surname><given-names>Nicholas P.</given-names></name></contrib><contrib contrib-type="author"><name><surname>Wolynes</surname><given-names>Peter G.</given-names></name><xref ref-type="corresp" rid="cor1"><sup>1</sup></xref></contrib><aff id="aff1">Departments of Chemistry and Physics and Astronomy and the Center for Theoretical Biological Physics,<institution>Rice University</institution>, Houston,<addr-line>TX</addr-line> 77005</aff></contrib-group><author-notes><corresp id="cor1"><sup>1</sup>To whom correspondence should be addressed. Email: <email>pwolynes@rice.edu</email>.</corresp><fn fn-type="edited-by"><p>Contributed by Peter G. Wolynes, June 11, 2014 (sent for review May 19, 2014; reviewed by Zaida A. Luthey-Schulten, Shoji Takada, and Margaret Cheung)</p></fn><fn fn-type="con"><p>Author contributions: B.L.K., N.P.S., and P.G.W. designed research; B.L.K. and N.P.S. performed research; B.L.K. and N.P.S. contributed new reagents/analytic tools; B.L.K., N.P.S., and P.G.W. analyzed data; and B.L.K., N.P.S., and P.G.W. wrote the paper.</p></fn><fn fn-type="con"><p>Reviewers: Z.A.L.-S., University of Illinois at Urbana-Champaign; S.T., Kyoto University; and M.C., University of Houston.</p></fn></author-notes><pub-date pub-type="ppub"><day>29</day><month>7</month><year>2014</year></pub-date><pub-date pub-type="epub"><day>16</day><month>7</month><year>2014</year></pub-date><volume>111</volume><issue>30</issue><fpage>11031</fpage><lpage>11036</lpage><self-uri xlink:title="pdf" xlink:type="simple" xlink:href="pnas.201410529.pdf"></self-uri><abstract abstract-type="executive-summary"><title>Significance</title><p>The understanding of how membrane proteins fold pales in comparison with the understanding of globular protein folding. This discrepancy is partly due to the fact that membrane proteins are difficult to work with experimentally. In turn, the lack of high-quality experimental data has caused modeling of membrane proteins to lag behind. Also, the extent to which the translocon assists transmembrane domains in folding is unclear. The number of experimentally determined membrane protein structures has recently increased, and we may now be at the stage where it has become possible to derive transferable simulation models for studying transmembrane protein folding. We describe the optimization of one such model and its application to predicting helical packings within the native topology.</p></abstract><abstract><p>We explore the hypothesis that the folding landscapes of membrane proteins are funneled once the proteins’ topology within the membrane is established. We extend a protein folding model, the associative memory, water-mediated, structure, and energy model (AWSEM) by adding an implicit membrane potential and reoptimizing the force field to account for the differing nature of the interactions that stabilize proteins within lipid membranes, yielding a model that we call AWSEM-membrane. Once the protein topology is set in the membrane, hydrophobic attractions play a lesser role in finding the native structure, whereas polar–polar attractions are more important than for globular proteins. We examine both the quality of predictions made with AWSEM-membrane when accurate knowledge of the topology and secondary structure is available and the quality of predictions made without such knowledge, instead using bioinformatically inferred topology and secondary structure based on sequence alone. When no major errors are made by the bioinformatic methods used to assign the topology of the transmembrane helices, these two types of structure predictions yield roughly equivalent quality structures. Although the predictive energy landscape is transferable and not structure based, within the correct topological sector we find the landscape is indeed very funneled: Thermodynamic landscape analysis indicates that both the total potential energy and the contact energy decrease as native contacts are formed. Nevertheless the near symmetry of different helical packings with respect to native contact formation can result in multiple packings with nearly equal thermodynamic occupancy, especially at temperatures just below collapse.</p></abstract><kwd-group><kwd>energy landscape theory</kwd><kwd>molecular dynamics</kwd></kwd-group><counts><page-count count="6"></page-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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