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MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains
Authors:Hugo YK Lam  Philip M Kim  Janine Mok  Raffi Tonikian  Sachdev S Sidhu  Benjamin E Turk  Michael Snyder  Mark B Gerstein
Affiliation:(1) Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA;(2) Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA;(3) Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, CT 06520, USA;(4) Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada;(5) Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada;(6) Department of Pharmacology, Yale University, New Haven, CT 06520, USA;(7) Department of Computer Science, Yale University, New Haven, CT 06520, USA;(8) Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, M5S 3E1, Canada;(9) Stanford Genome Technology Center, Department of Biochemistry, Stanford University, Palo Alto, CA 94304, USA;(10) Department of Genetics, Stanford University, Palo Alto, CA 94305, USA
Abstract:

Background  

Many protein interactions, especially those involved in signaling, involve short linear motifs consisting of 5-10 amino acid residues that interact with modular protein domains such as the SH3 binding domains and the kinase catalytic domains. One straightforward way of identifying these interactions is by scanning for matches to the motif against all the sequences in a target proteome. However, predicting domain targets by motif sequence alone without considering other genomic and structural information has been shown to be lacking in accuracy.
Keywords:
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