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Structure‐based prediction of protein–peptide specificity in rosetta
Author(s) -
King Christopher A.,
Bradley Philip
Publication year - 2010
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.22851
Subject(s) - peptide , computational biology , benchmark (surveying) , generality , protein structure prediction , computer science , structural bioinformatics , protein structure , set (abstract data type) , sequence (biology) , biology , biochemistry , geodesy , psychotherapist , programming language , geography , psychology
Protein–peptide interactions mediate many of the connections in intracellular signaling networks. A generalized computational framework for atomically precise modeling of protein–peptide specificity may allow for predicting molecular interactions, anticipating the effects of drugs and genetic mutations, and redesigning molecules for new interactions. We have developed an extensible, general algorithm for structure‐based prediction of protein–peptide specificity as part of the Rosetta molecular modeling package. The algorithm is not restricted to any one peptide‐binding domain family and, at minimum, does not require an experimentally characterized structure of the target protein nor any information about sequence specificity; although known structural data can be incorporated when available to improve performance. We demonstrate substantial success in specificity prediction across a diverse set of peptide‐binding proteins, and show how performance is affected when incorporating varying degrees of input structural data. We also illustrate how structure‐based approaches can provide atomic‐level insight into mechanisms of peptide recognition and can predict the effects of point mutations on peptide specificity. Shortcomings and artifacts of our benchmark predictions are explained and limits on the generality of the method are explored. This work provides a promising foundation upon which further development of completely generalized, de novo prediction of peptide specificity may progress. Proteins 2010. © 2010 Wiley‐Liss, Inc.