Predicting peptide-mediated interactions on a genome-wide scale.
We describe a method to predict protein-protein interactions (PPIs) formed between structured domains and short peptide motifs. We take an integrative approach based on consensus patterns of known motifs in databases, structures of domain-motif complexes from the PDB and various sources of non-structural evidence. We combine this set of clues using a Bayesian classifier that reports the likelihood of an interaction and obtain significantly improved prediction performance when compared to individual sources of evidence and to previously reported algorithms. Our Bayesian approach was integrated into PrePPI, a structure-based PPI prediction method that, so far, has been limited to interactions formed between two structured domains. Around 80,000 new domain-motif mediated interactions were predicted, thus enhancing PrePPI's coverage of the human protein interactome.
Howard Hughes Medical Institute, Columbia University, New York, New York, United States of America; Department of Systems Biology, Columbia University, New York, New York, United States of America; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America; Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America.
T Scott
TS
Chen
Howard Hughes Medical Institute, Columbia University, New York, New York, United States of America; Department of Systems Biology, Columbia University, New York, New York, United States of America; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America; Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America.
Donald
D
Petrey
Howard Hughes Medical Institute, Columbia University, New York, New York, United States of America; Department of Systems Biology, Columbia University, New York, New York, United States of America; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America; Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America.
Jose Ignacio
JI
Garzon
Howard Hughes Medical Institute, Columbia University, New York, New York, United States of America; Department of Systems Biology, Columbia University, New York, New York, United States of America; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York, United States of America; Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America.
Barry
B
Honig
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology; D030562:Databases, Protein / Q000706:statistics & numerical data; D015894:Genome, Human; D006801:Humans; D016013:Likelihood Functions; D008954:Models, Biological; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D040901:Proteomics / Q000706:statistics & numerical data; D060388:Support Vector Machine