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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Valente, Guilherme T. [1] ; Acencio, Marcio L. [2] ; Martins, Cesar [1] ; Lemke, Ney [2]
Total Authors: 4
Affiliation:
[1] Univ Estadual Paulista, UNESP, Dept Morphol, Botucatu, SP - Brazil
[2] Univ Estadual Paulista, UNESP, Dept Phys & Biophys, Botucatu, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PLoS One; v. 8, n. 5 MAY 31 2013.
Web of Science Citations: 16
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. (AU)

FAPESP's process: 13/02018-4 - Machine learning for molecular systems biology (MLMSB) application on synthetic lethality, conditionally essential genes and cooperative transcription
Grantee:Ney Lemke
Support Opportunities: Regular Research Grants
FAPESP's process: 09/05234-4 - Elucidation of sex determining mechanisms in fishes: contributions from physical chromosome mapping and nucleotide sequence characterization of genes involved in sex determination and differentiation in cichlid fishes
Grantee:César Martins
Support Opportunities: Regular Research Grants