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Native and modeled disulfide bonds in proteins: Knowledge‐based approaches toward structure prediction of disulfide‐rich polypeptides
Author(s) -
Thangudu Ratna Rajesh,
Vinayagam A.,
Pugalenthi G.,
Mamani A.,
Offmann B.,
Sowdhamini R.
Publication year - 2005
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.20369
Subject(s) - disulfide bond , chemistry , computational biology , exploit , structural motif , protein structure prediction , protein structure , peptide sequence , structural bioinformatics , computer science , biochemistry , biology , gene , computer security
Structure prediction and three-dimensional modeling of disulfide-rich systems are challenging due to the limited number of such folds in the structural databank. We exploit the stereochemical compatibility of substructures in known protein structures to accommodate disulfide bonds in predicting the structures of disulfide-rich polypeptides directly from disulfide connectivity pattern and amino acid sequence in the absence of structural homologs and any other structural information. This knowledge-based approach is illustrated using structure prediction of 40 nonredundant bioactive disulfide-rich polypeptides such as toxins, growth factors, and endothelins available in the structural databank. The polypeptide conformation could be predicted in 35 out of 40 nonredundant entries (87%). Nonhomologous templates could be identified and models could be obtained within 2 A deviation from the query in 29 peptides (72%). This procedure can be accessed from the World Wide Web (http://www.ncbs.res.in/ approximately faculty/mini/dsdbase/dsdbase.html).