Protein-protein binding interaction is the most prevalent biological activity that mediates a great variety of biological processes. The increasing availability of experimental data of protein-protein interaction allows a systematic construction of protein-protein interaction networks, significantly contributing to a better understanding of protein functions and their roles in cellular pathways and human diseases. Compared to well-established classification for protein-protein interactions (PPIs), limited work has been conducted for estimating protein-protein binding free energy, which can provide informative real-value regression models for characterizing the protein-protein binding affinity. In this study, we propose a novel ensemble computational framework, termed ProBAPred (Protein-protein Binding Affinity Predictor), for quantitative estimation of protein-protein binding affinity. A large number of sequence and structural features, including physical-chemical properties, binding energy and conformation annotations, were collected and calculated from currently available protein binding complex datasets and the literature. Feature selection based on the WEKA package was performed to identify and characterize the most informative and contributing feature subsets. Experiments on the independent test showed that our ensemble method achieved the lowest MAE (Mean Absolute Error; 1.657 kcal/mol) and the second highest correlation coefficient (R-value=0.467), compared with the existing methods. We anticipate that the developed ProBAPred regression models can facilitate computational characterization and experimental studies of protein-protein binding affinity

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If you find our tool useful, please kindly cite our manuscript in your work:

Bangli Lu, Chen Li, Qingfeng Chen and Jiangning Song. ProBAPred: inferring protein-protein binding affinity by incorporating protein sequence and structural features, Journal of Bioinformatics and Computational Biology, accepted.


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