title: An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions.
abstract: Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data. However, these high-throughput data are often affected by a low signal to noise ratio. Furthermore, the prediction methods have several additional shortcomings, such as linearity problem, high computational complexity, etc. Thus, computational identification of SH2-peptide interactions using high-throughput data remains challenging. Here, we propose a machine learning approach based on an efficient semi-supervised learning technique for the prediction of 51 SH2 domain mediated interactions in the human proteome. In our study, we have successfully employed several strategies to tackle the major problems in computational identification of SH2-peptide interactions.
authors.affiliation: Department of Human Genetics, The Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. kk8@sanger.ac.uk.|Department of Haematology, University of Cambridge, Cambridge, UK. kk8@sanger.ac.uk.
authors.forename: Kousik
authors.initials: K
authors.lastname: Kundu
authors.affiliation: Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.|Centre for Biological Signalling Studies (BIOSS), University of Freiburg, Freiburg, Germany.
authors.forename: Rolf
authors.initials: R
authors.lastname: Backofen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D006801:Humans; D019000:Phosphotyrosine / Q000378:metabolism; D011485:Protein Binding; D054730:Protein Interaction Domains and Motifs; D025941:Protein Interaction Mapping / Q000379:methods*; D015203:Reproducibility of Results; D015398:Signal Transduction; D000069553:Supervised Machine Learning; D064878:Web Browser; D057188:Workflow; D018909:src Homology Domains
chemical_list: D019000:Phosphotyrosine
keywords: Phosphotyrosine peptides; Protein–protein interaction; Semi-supervised learning; Signal transduction; Src homology 2 domain; Support vector machine