Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites

BMC Bioinformatics

Research

Petr Škoda¹  David Hoksza¹  Jan Jelínek¹ 

[1] Department of Software Engineering, Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, Prague 2, Czech Republic;

关键词: Protein-protein interaction;    Prediction;    Molecular fingerprints;    Data mining;   

DOI  :  10.1186/s12859-017-1921-4

来源: Springer

【 摘 要 】

BackgroundProtein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect.ResultsWe have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE’s behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient.ConclusionIn this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

【 授权许可】

CC BY   
© The Author(s) 2017

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