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Recognizing protein folds by cluster distance geometry

dc.contributor.authorCrippen, Gordon M.en_US
dc.date.accessioned2006-09-20T15:02:12Z
dc.date.available2006-09-20T15:02:12Z
dc.date.issued2005-07-01en_US
dc.identifier.citationCrippen, Gordon M. (2005)."Recognizing protein folds by cluster distance geometry." Proteins: Structure, Function, and Bioinformatics 60(1): 82-89. <http://hdl.handle.net/2027.42/48690>en_US
dc.identifier.issn0887-3585en_US
dc.identifier.issn1097-0134en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/48690
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15861390&dopt=citationen_US
dc.description.abstractCluster distance geometry is a recent generalization of distance geometry whereby protein structures can be described at even lower levels of detail than one point per residue. With improvements in the clustering technique, protein conformations can be summarized in terms of alternative contact patterns between clusters, where each cluster contains four sequentially adjacent amino acid residues. A very simple potential function involving 210 adjustable parameters can be determined that favors the native contacts of 31 small, monomeric proteins over their respective sets of nonnative contacts. This potential then favors the native contacts for 174 small, monomeric proteins that have low sequence identity with any of the training set. A broader search finds 698 small protein chains from the Protein Data Bank where the native contacts are preferred over all alternatives, even though they have low sequence identity with the training set. This amounts to a highly predictive method for ab initio protein folding at low spatial resolution. Proteins 2005;. © 2005 Wiley-Liss, Inc.en_US
dc.format.extent95084 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherWiley Subscription Services, Inc., A Wiley Companyen_US
dc.subject.otherChemistryen_US
dc.subject.otherBiochemistry and Biotechnologyen_US
dc.titleRecognizing protein folds by cluster distance geometryen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumCollege of Pharmacy, University of Michigan, Ann Arbor, Michigan ; College of Pharmacy, University of Michigan, Ann Arbor, MI 48109-1065en_US
dc.identifier.pmid15861390en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/48690/1/20488_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/prot.20488en_US
dc.identifier.sourceProteins: Structure, Function, and Bioinformaticsen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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