DeepBindPPI: Protein-Protein Binding Site Prediction Using Attention Based Graph Convolutional Network.

Autor: Sunny S; Department of CSE, National Institute of Technology, Calicut, Kerala, 673601, India. ssharon099@gmail.com., Prakash PB; Department of ECE, National Institute of Technology, Calicut, Kerala, 673601, India., Gopakumar G; Department of CSE, National Institute of Technology, Calicut, Kerala, 673601, India., Jayaraj PB; Department of CSE, National Institute of Technology, Calicut, Kerala, 673601, India.
Jazyk: angličtina
Zdroj: The protein journal [Protein J] 2023 Aug; Vol. 42 (4), pp. 276-287. Date of Electronic Publication: 2023 May 18.
DOI: 10.1007/s10930-023-10121-9
Abstrakt: Due to the importance of protein-protein interactions in defence mechanism of living body, attempts were made to investigate its attributes, including, but not limited to, binding affinity, and binding region. Contemporary strategies for binding site prediction largely resort to deep learning techniques but turned out to be low precision models. As laboratory experiments for drug discovery tasks utilize this information, increased false positives devalue the computational methods. This emphasize the need to develop enhanced strategies. DeepBindPPI employs deep learning technique to predict the binding regions of proteins, particularly antigen-antibody interaction sites. The results obtained are applied in a docking environment to confirm their correctness. An integration of graph convolutional network with attention mechanism predicts interacting amino acids with improved precision. The model learns the determining factors in interaction from a general pool of proteins and is then fine-tuned using antigen-antibody data. Comparison of the proposed method with existing techniques shows that the developed model has comparable performance. The use of a separate spatial network clearly improved the precision of the proposed method from 0.4 to 0.5. An attempt to utilize the interface information for docking using the HDOCK server gives promising results, with high-quality structures appearing in the top10 ranks.
(© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Databáze: MEDLINE
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