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

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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.

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Data availability

The data used in this work are available at https://web.iitm.ac.in/bioinfo2/deepbsrpred/download.html.

Abbreviations

An-Ab:

Antigen–antibody

EC:

Enzyme containing

GP:

G-protein containing

IN:

Inhibitor containing

RC:

Receptor containing

MS:

Miscellaneous

ASA:

Accessible surface area

AUROC:

Area under the receiver operating characteristic curve

AUPRC:

Area under precision-recall

PSSM:

Position-specific scoring matrix

F1:

F1-score

MCC:

Matthew’s correlation coefficient

Polar real:

ASA of Polar residues

BIOV880102:

Information value for accessibility (Biou et al. 1988)

NADH010102:

Hydropathy scale based on self-information values in the two-state model (Naderi-Manesh et al. 2001)

VALDAR:

Protein conservation metrics (Valdar and Thornton, 2001)

dASA:

Solvent accessible surface area for protein unfolding

PONJ960101:

Average volumes of residues (Pontius et al. 1996)

FASG760101:

Molecular weight (Fasman 1976)

GRAR740103:

Volume (Grantham 1974)

HB acceptor:

Hydrogen bond acceptor

ASAD:

Solvent accessible surface area for denatured protein (Gromiha et al. 1999)

ASAN:

Solvent accessible surface area for native protein (Gromiha et al. 1999)

TAYLOR_GAPS:

Conservation score (Taylor 1986)

PSSM sum:

Summation of PSSM values; Residue depth: Residue depth is computed using python

SMERFS:

Conservation from AAcon tool (Manning et al. 2008)

Contact count:

Number of contacts of the residue

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Acknowledgements

We thank Indian Institute of Technology Madras and the High-Performance Computing Environment (HPCE) for computational facilities. The work is partially supported by the Department of Science and Technology, Government of India (No. DST/INT/SWD/P-05/2016).

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Authors and Affiliations

  1. Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India

    Rahul Nikam, Kumar Yugandhar & M. Michael Gromiha

  2. Department of Computational Biology, Cornell University, New York, NY, USA

    Kumar Yugandhar

  3. Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan

    M. Michael Gromiha

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  1. Rahul Nikam
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  2. Kumar Yugandhar
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  3. M. Michael Gromiha
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Contributions

Conceptualization: MMG; methodology: MMG, software/code: RN; investigation: RN, KY; discussion: RN, KY, MMG; writing original draft: RN; review & editing: MMG, KY; supervision: MMG. All authors read and approved the manuscript.

Corresponding author

Correspondence to M. Michael Gromiha.

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Nikam, R., Yugandhar, K. & Gromiha, M.M. DeepBSRPred: deep learning-based binding site residue prediction for proteins. Amino Acids 55, 1305–1316 (2023). https://doi.org/10.1007/s00726-022-03228-3

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