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. 2023 Oct;55(10):1305-1316.
doi: 10.1007/s00726-022-03228-3. Epub 2022 Dec 27.

DeepBSRPred: deep learning-based binding site residue prediction for proteins

Rahul Nikam  1 , Kumar Yugandhar  1   2 , M Michael Gromiha  3   4
Affiliations

DeepBSRPred: deep learning-based binding site residue prediction for proteins

Rahul Nikam et al. Amino Acids. 2023 Oct.

Abstract

Motivation: Proteins-protein interactions (PPIs) are important to govern several cellular activities. Amino acid residues, which are located at the interface are known as the binding sites and the information about binding sites helps to understand the binding affinities and functions of protein-protein complexes.

Results: We have developed a deep neural network-based method, DeepBSRPred, for predicting the binding sites using protein sequence information and predicted structures from AlphaFold2. Specific sequence and structure-based features include position-specific scoring matrix (PSSM), solvent accessible surface area, conservation score and amino acid properties, and residue depth, respectively. Our method predicted the binding sites with an average F1 score of 0.73 in a dataset of 1236 proteins. Further, we compared the performance with other existing methods in the literature using four benchmark datasets and our method outperformed those methods.

Availability and implementation: The DeepBSRPred web server can be found at https://web.iitm.ac.in/bioinfo2/deepbsrpred/index.html , along with all datasets used in this study. The trained models, the DeepBSRPred standalone source code, and the feature computation pipeline are freely available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html .

Keywords: AlphaFold; Binding sites; Deep learning; Neural network; Protein–protein interactions.

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