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Cellular and Molecular Biology Letters

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Volume 16, Issue 2 (Jun 2011)

PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

Piyali Chatterjee
  • Netaji Subhash Engineering College
  • Email:
/ Subhadip Basu
  • Jadavpur University
  • Email:
/ Mahantapas Kundu
  • Jadavpur University
  • Email:
/ Mita Nasipuri
  • Jadavpur University
  • Email:
/ Dariusz Plewczynski
  • University of Warsaw
  • Email:
Published Online: 2011-03-26 | DOI: https://doi.org/10.2478/s11658-011-0008-x

Abstract

Protein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

Keywords: Protein-protein interaction; Domain-frequency values; Domaindomain interaction affinity value; Proteome; Interactome; Brainstorming; Machine learning; Consensus; DIP; Protein domains; Sequences; Structures; Protein-protein complexes

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About the article

Published Online: 2011-03-26

Published in Print: 2011-06-01



Citation Information: Cellular and Molecular Biology Letters, ISSN (Online) 1689-1392, DOI: https://doi.org/10.2478/s11658-011-0008-x. Export Citation

© 2011 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

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