Prediction of Contact Residues in Anti-HIV Neutralizing Antibody by Deep Learning.

Abstract:

:The 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 crystallization of 1C10 and the V3 peptide was unsuccessful due to the flexible regions present in both 1C10 and the V3 peptide. In this study, we predicted the 1C10 amino acid residues that make contact with 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 with alanine significantly decreased the affinity of 1C10 to the V3 peptide. Moreover, the higher the rank of the residue, the more the binding activity diminished. This study demonstrates that the prediction of contact residues using a DL-based approach is a precise and useful tool for the analysis of antibody-antigen interactions.

journal_name

Jpn J Infect Dis

authors

Kaku Y,Kuwata T,Gorny MK,Matsushita S

doi

10.7883/yoken.JJID.2019.496

subject

Has Abstract

pub_date

2020-05-22 00:00:00

pages

235-241

issue

3

eissn

1344-6304

issn

1884-2836

journal_volume

73

pub_type

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