Prediction of protease substrates using sequence and structure features
Author(s) -
David T. Barkan,
Daniel R. Hostetter,
Sami Mahrus,
Ursula Pieper,
James A. Wells,
Charles S. Craik,
Andrej Šali
Publication year - 2010
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btq267
Subject(s) - protease , proteases , computational biology , peptide , biology , proteome , computer science , artificial intelligence , support vector machine , peptide sequence , sequence (biology) , bioinformatics , biochemistry , enzyme , gene
Granzyme B (GrB) and caspases cleave specific protein substrates to induce apoptosis in virally infected and neoplastic cells. While substrates for both types of proteases have been determined experimentally, there are many more yet to be discovered in humans and other metazoans. Here, we present a bioinformatics method based on support vector machine (SVM) learning that identifies sequence and structural features important for protease recognition of substrate peptides and then uses these features to predict novel substrates. Our approach can act as a convenient hypothesis generator, guiding future experiments by high-confidence identification of peptide-protein partners.
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