Prediction of contact residues in anti-HIV neutralizing antibody by deep learning.

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Abstract

Monoclonal antibody 1C10 targets the V3 loop of HIV-1 and neutralizes a broad range of clade B viruses. However, the mode of interaction between 1C10 and the V3 loop remains unclear, because the crystallization of 1C10 and the V3 peptide was unsuccessful because of the flexible nature of 1C10 and the V3 peptide. In this study, we predicted which amino acid residues of 1C10 contact the V3 loop using a deep learning (DL)-based method. Inputs from ROSIE for docking simulation and FastContact, Naccess, and PDBtools, to approximate interactions were processed by Chainer for DL, and outputs were obtained as probabilities of contact residues. Using this DL algorithm, D95, D97, P100a and D100b of CDRH3, D53 and D56 of CDRH2, and D61 of FR3 were highly ranked as contact residues of 1C10. Substitution of these residues to alanine significantly decreased the affinity of 1C10 to the V3 peptide. Moreover, the higher the rank of residue, the more the binding activity was diminished. This study demonstrates that the prediction of contact residues using a DL-based approach is precise and useful for the analysis of antibody-antigen interactions.