Binding MOAD (Mother Of All Databases)
dc.contributor.author | Hu, Liegi | en_US |
dc.contributor.author | Benson, Mark L. | en_US |
dc.contributor.author | Smith, Richard D. | en_US |
dc.contributor.author | Lerner, Michael G. | en_US |
dc.contributor.author | Carlson, Heather A. | en_US |
dc.date.accessioned | 2006-09-20T15:02:16Z | |
dc.date.available | 2006-09-20T15:02:16Z | |
dc.date.issued | 2005-08-15 | en_US |
dc.identifier.citation | Hu, Liegi; Benson, Mark L.; Smith, Richard D.; Lerner, Michael G.; Carlson, Heather A. (2005)."Binding MOAD (Mother Of All Databases)." Proteins: Structure, Function, and Bioinformatics 60(3): 333-340. <http://hdl.handle.net/2027.42/48691> | en_US |
dc.identifier.issn | 0887-3585 | en_US |
dc.identifier.issn | 1097-0134 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/48691 | |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=15971202&dopt=citation | en_US |
dc.description.abstract | Binding MOAD (Mother of All Databases) is the largest collection of high-quality, protein–ligand complexes available from the Protein Data Bank. At this time, Binding MOAD contains 5331 protein–ligand complexes comprised of 1780 unique protein families and 2630 unique ligands. We have searched the crystallography papers for all 5000+ structures and compiled binding data for 1375 (26%) of the protein–ligand complexes. The binding-affinity data ranges 13 orders of magnitude. This is the largest collection of binding data reported to date in the literature. We have also addressed the issue of redundancy in the data. To create a nonredundant dataset, one protein from each of the 1780 protein families was chosen as a representative. Representatives were chosen by tightest binding, best resolution, etc. For the 1780 “best” complexes that comprise the nonredundant version of Binding MOAD, 475 (27%) have binding data. This significant collection of protein–ligand complexes will be very useful in elucidating the biophysical patterns of molecular recognition and enzymatic regulation. The complexes with binding-affinity data will help in the development of improved scoring functions and structure-based drug discovery techniques. The dataset can be accessed at http://www.BindingMOAD.org . Proteins 2005. © 2005 Wiley-Liss, Inc. | en_US |
dc.format.extent | 309315 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 | Binding MOAD (Mother Of All Databases) | 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 | Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Bioinformatics Program, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Biophysics Research Division, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Biophysics Research Division, University of Michigan, Ann Arbor, Michigan | en_US |
dc.contributor.affiliationum | Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, Michigan ; Bioinformatics Program, University of Michigan, Ann Arbor, Michigan ; Biophysics Research Division, University of Michigan, Ann Arbor, Michigan ; University of Michigan, College of Pharmacy, 428 Church St., Ann Arbor, MI 48109-1065 | en_US |
dc.identifier.pmid | 15971202 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/48691/1/20512_ftp.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1002/prot.20512 | en_US |
dc.identifier.source | Proteins: Structure, Function, and Bioinformatics | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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