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

Autor(es)

Valente, Guilherme T. ^[1] ; Acencio, Marcio L. ^[2] ; Martins, Cesar ^[1] ; Lemke, Ney ^[2]

Afiliação do(s) autor(es)

^[1] Univ Estadual Paulista, UNESP, Dept Morphol, Botucatu, SP - Brazil

^[2] Univ Estadual Paulista, UNESP, Dept Phys & Biophys, Botucatu, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PLoS One; v. 8, n. 5 MAY 31 2013.
• Citações Web of Science: 16

Resumo

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)

• Processo FAPESP: 09/05234-4 - Elucidação dos mecanismos de determinação sexual nos peixes: contribuições obtidas através do mapeamento físico cromossômico e caracterização nucleotídica de genes envolvidos na determinação e diferenciação sexual em peixes ciclídeos
• Beneficiário: César Martins
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 13/02018-4 - Aprendizado de máquina em biologia molecular de sistemas (AMBiS) aplicação em letalidade sintética, genes condicionalmente essenciais e transcrição gênica cooperativa
• Beneficiário: Ney Lemke
• Linha de fomento: Auxílio à Pesquisa - Regular