Journal Article

Predicting physiologically relevant SH3 domain mediated protein–protein interactions in yeast

Shobhit Jain and Gary D. Bader

in Bioinformatics

Volume 32, issue 12, pages 1865-1872
Published in print June 2016 | ISSN: 1367-4803
Published online February 2016 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btw045

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology

GO

Show Summary Details

Preview

Motivation: Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein–protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins.

Results: A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein–protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy of 0.97.

Availability and implementation: Domain-Motif Mediated Interaction Prediction (DoMo-Pred) command line tool and all relevant datasets are available under GNU LGPL license for download from http://www.baderlab.org/Software/DoMo-Pred. The DoMo-Pred command line tool is implemented using Python 2.7 and C ++.

Contact: gary.bader@utoronto.ca

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5172 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.