Recognizing protein folds by cluster distance geometry
dc.contributor.author | Crippen, Gordon M. | en_US |
dc.date.accessioned | 2006-09-20T15:02:12Z | |
dc.date.available | 2006-09-20T15:02:12Z | |
dc.date.issued | 2005-07-01 | en_US |
dc.identifier.citation | Crippen, 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.issn | 0887-3585 | en_US |
dc.identifier.issn | 1097-0134 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/48690 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15861390&dopt=citation | en_US |
dc.description.abstract | Cluster 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.extent | 95084 bytes | |
dc.format.extent | 3118 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Wiley Subscription Services, Inc., A Wiley Company | en_US |
dc.subject.other | Chemistry | en_US |
dc.subject.other | Biochemistry and Biotechnology | en_US |
dc.title | Recognizing protein folds by cluster distance geometry | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | College of Pharmacy, University of Michigan, Ann Arbor, Michigan ; College of Pharmacy, University of Michigan, Ann Arbor, MI 48109-1065 | en_US |
dc.identifier.pmid | 15861390 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/48690/1/20488_ftp.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1002/prot.20488 | en_US |
dc.identifier.source | Proteins: Structure, Function, and Bioinformatics | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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