Algorithmic approaches to protein-protein interaction site prediction

Algorithms for Molecular Biology

Tristan T Aumentado-Armstrong¹  Bogdan Istrate¹  Robert A Murgita² 

[1] School of Computer Science, McGill University, Montreal, Canada

[2] Department of Microbiology and Immunology, McGill University, Montreal, Canada

关键词: Homology;    Biological databases;    Machine learning;    Interface types;    Protein structure;    Feature selection;    Protein-protein binding;    Protein-protein interface;    Protein-protein interaction;    Prediction algorithm;   

Others  :  1141275
DOI  :  10.1186/s13015-015-0033-9

 received in 2014-08-23, accepted in 2015-01-07,  发布年份 2015

【 摘 要 】

Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

【 授权许可】

   
2015 Aumentado-Armstrong et al.; licensee BioMed Central.

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