Abstract
Protein-protein interactions (PPIs) mediate myriad biological functions. Estimating the binding affinity for PPIs help us to understand the underlying molecular recognition mechanism. In this work, we utilized deep learning approach to discriminate protein-protein complexes based on their binding affinity. We setup a database of 464 protein-protein complexes along with their experimental binding affinities and developed a deep learning based binary classification model, which showed an accuracy of 81.75% using 5-fold cross-validation. Furthermore, we refined the method for predicting the binding affinity of protein-protein complexes using a large set of complexes (PPA-Pred2). It could predict the binding affinity of the training set and blind test set with a mean absolute error of 1.24 kcal/mol and 1.31 kcal/mol, respectively. We suggest that our methods could serve as efficient tools to study PPIs and provide crucial insights about the underlying mechanism for the molecular recognition process.
R. Nikam and K. Yugandhar—Contributed equally to this work.
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Acknowledgements
We thank Indian Institute of Technology Madras and the High-Performance Computing Environment (HPCE) for computational facilities. The work was partially supported by the Department of Science and Technology, Government of India (DST/INT/SWD/P-05/2016) and of the Swedish Research Council.
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Nikam, R., Yugandhar, K., Michael Gromiha, M. (2018). Discrimination and Prediction of Protein-Protein Binding Affinity Using Deep Learning Approach. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_89
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