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SH2 Domains pp 83-97 | Cite as

An Efficient Semi-supervised Learning Approach to Predict SH2 Domain Mediated Interactions

  • Kousik Kundu
  • Rolf Backofen
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1555)

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.

Key words

Src homology 2 domain Signal transduction Protein–protein interaction Phosphotyrosine peptides Support vector machine Semi-supervised learning 

Notes

Acknowledgements

This chapter is based on our previous publication [18]. This work was funded by Bundesministerium für Bildung und Forschung (e-bio; FKZ 0316174A to Rolf Backofen), and the Centre for Biological Signalling Studies (BIOSS), University of Freiburg.

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Bioinformatics Group, Department of Computer ScienceUniversity of FreiburgFreiburgGermany
  2. 2.Department of Human GeneticsThe Wellcome Trust Sanger InstituteHinxton, CambridgeUK
  3. 3.Department of HaematologyUniversity of CambridgeCambridgeUK
  4. 4.Centre for Biological Signalling Studies (BIOSS), University of FreiburgFreiburgGermany

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