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The Development of a Universal In Silico Predictor of Protein-Protein Interactions

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  • Guilherme T Valente
  • Marcio L Acencio
  • Cesar Martins
  • Ney Lemke

Abstract

Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation.

Suggested Citation

  • Guilherme T Valente & Marcio L Acencio & Cesar Martins & Ney Lemke, 2013. "The Development of a Universal In Silico Predictor of Protein-Protein Interactions," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    • Handle: RePEc:plo:pone00:0065587
    DOI: 10.1371/journal.pone.0065587
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    References listed on IDEAS

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    1. Benjamin A Shoemaker & Anna R Panchenko, 2007. "Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners," PLOS Computational Biology, Public Library of Science, vol. 3(4), pages 1-7, April.
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    4. Xinyi Liu & Bin Liu & Zhimin Huang & Ting Shi & Yingyi Chen & Jian Zhang, 2012. "SPPS: A Sequence-Based Method for Predicting Probability of Protein-Protein Interaction Partners," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-6, January.
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    1. Saeid Rasti & Chrysafis Vogiatzis, 2019. "A survey of computational methods in protein–protein interaction networks," Annals of Operations Research, Springer, vol. 276(1), pages 35-87, May.

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