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Prediction of the binding interface between monoclonal antibody m102.4 and Nipah attachment glycoprotein using structure-guided alanine scanning and computational docking
Name
s41598-020-75056-y.pdf
Description
Published version
Size
3.1 MB
Format
Adobe PDF
Checksum (MD5)
51c36b0518567389cb8323f77eaa80ff
Author(s)
Tit-oon, Phanthakarn
Tharakaraman, Kannan
Artpradit, Charlermchai
Godavarthi, Abhinav
Sungkeeree, Pareenart
Sasisekharan, Varun
Kerdwong, Jarunee
Miller, Nathaniel Loren
Mahajan, Bhuvna
Khongmanee, Amnart
Date Issued
2020
Journal
Scientific Reports
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020, The Author(s). Nipah Virus (NiV) has been designated as a priority disease with an urgent need for therapeutic development by World Health Organization. The monoclonal antibody m102.4 binds to the immunodominant NiV receptor-binding glycoprotein (GP), and potently neutralizes NiV, indicating its potential as a therapeutic agent. Although the co-crystal structure of m102.3, an m102.4 derivative, in complex with the GP of the related Hendra Virus (HeV) has been solved, the structural interaction between m102.4 and NiV is uncharacterized. Herein, we used structure-guided alanine-scanning mutagenesis to map the functional epitope and paratope residues that govern the antigen–antibody interaction. Our results revealed that the binding of m102.4 is mediated predominantly by two residues in the HCDR3 region, which is unusually small for an antibody-antigen interaction. We performed computational docking to generate a structural model of m102.4-NiV interaction. Our model indicates that m102.4 targets the common hydrophobic central cavity and a hydrophilic rim on the GP, as observed for the m102.3-HeV co-crystal, albeit with Fv orientation differences. In summary, our study provides insight into the m102.4-NiV interaction, demonstrating that structure-guided alanine-scanning and computational modeling can serve as the starting point for additional antibody reengineering (e.g. affinity maturation) to generate potential therapeutic candidates.
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
10.1038/S41598-020-75056-Y
