Semi-supervised prediction of SH2-peptide interactions from imbalanced high-throughput data.

ABSTRACT: Src homology 2 (SH2) domains are the largest family of the peptide-recognition modules (PRMs) that bind to phosphotyrosine containing peptides. Knowledge about binding partners of SH2-domains is key for a deeper understanding of different cellular processes. Given the high binding specificity of SH2, in-silico ligand peptide prediction is of great interest. Currently however, only a few approaches have been published for the prediction of SH2-peptide interactions. Their main shortcomings range from limited coverage, to restrictive modeling assumptions (they are mainly based on position specific scoring matrices and do not take into consideration complex amino acids inter-dependencies) and high computational complexity. We propose a simple yet effective machine learning approach for a large

SUBMITTER: Kundu K 

PROVIDER: S-EPMC3656881 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature