Rapid Identification of H3N2 Influenza A Virus HA-Targeting Antibody Epitopes Driven by Computational Antibody Similarity Measures
- 1Academy of Military Medical Sciences , ,
- 2The 961st Hospital of Joint Logistics Support Force , ,
Abstract
Antibodies are important therapeutic agents for the prevention and treatment of influenza, and hemagglutinin (HA) serves as the primary target for the development of therapeutic antibodies. However, the identification of epitopes remains an unmet need in the development of such therapeutic antibodies. This study investigates the characteristics of antibody–HA binding interfaces (i.e., epitopes and paratopes) through a computational analysis of all antibody–HA complexes with nonredundant experimentally determined structures from SAbDab, the largest reported antibody structure data set to date. After experimentally identifying and selecting H3N2 HA-specific binding antibodies, a reference antibody library was established. For anti-H3N2 HA antibodies with unknown structures, epitope grouping was conducted based on competitive binding assays that assess interference with the binding of antibodies from the reference library. Based on the complementary relationship between epitope and paratope structures, we selected the structure-based similarity measure most relevant to epitope convergence. Furthermore, we prioritized the experimental validation of binding interference between candidate antibodies and their most structurally similar reference antibodies. We present an integrated computational and experimental framework. This approach includes the construction of an H3N2-specific reference antibody set, computational modeling of antibodies, measurement of antibody similarity, and is complemented by antibody competition binding assays. The dry–wet iterative protocol presented here yielded accurate assessment of the neutralizing potency and breadth of the previously uncharacterized antibody P2A10. Together, the presented pipeline offers a powerful approach for identifying epitopes on anti-H3N2 HA antibodies and for epitope-targeted antibody discovery, while demonstrating the effectiveness of structural analytics in reducing experimental labor.
Related
- A Cell-based Screening Method Using an Intracellular Antibody for Discovering Small Molecules Targeting Hard-to-drug Proteins
- Computational Design of a TCR-like Human VH Domain Antibody That Specifically Recognizes a WT1-Derived Peptide Presented by Class I Major Histocompatibility Complex
- A genome-wide computational approach for therapeutic targets identification and synthetic peptide assembly enable rational vaccine design against Arcanobacterium haemolyticum
- Recombinant SIRT1 expression and SIRT1 METTL3 interaction analysis with computational identification of phytochemical SIRT1 activators and inhibitors
- An Integrative Computational Approach to Predict Viral Epitopes by Targeting the MHC-TCR Complexation
BibTeX
@article{zhu2026rapid,
title = {{Rapid Identification of H3N2 Influenza A Virus HA-Targeting Antibody Epitopes Driven by Computational Antibody Similarity Measures}},
author = {Zheng Zhu and Zhengshan Chen and Jinxia Wu and Jin Han and Linkuo Li and Kewen Qian and Yue Cui and Zeya Li and Rongjun Ding and Shixin Pang and Guanying Zhang and Changming Yu and Xiangyang Chi},
journal = {ACS Omega},
year = {2026},
month = oct,
doi = {10.1021/acsomega.6c04366},
url = {https://pubs.acs.org/acsodf/article/11/42/64613/5452211/Rapid-Identification-of-H3N2-Influenza-A-Virus-HA},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Rapid Identification of H3N2 Influenza A Virus HA-Targeting Antibody Epitopes Driven by Computational Antibody Similarity Measures
AU - Zheng Zhu
AU - Zhengshan Chen
AU - Jinxia Wu
AU - Jin Han
AU - Linkuo Li
AU - Kewen Qian
AU - Yue Cui
AU - Zeya Li
AU - Rongjun Ding
AU - Shixin Pang
AU - Guanying Zhang
AU - Changming Yu
AU - Xiangyang Chi
PY - 2026
DA - 2026/10/15
JO - ACS Omega
DO - 10.1021/acsomega.6c04366
UR - https://pubs.acs.org/acsodf/article/11/42/64613/5452211/Rapid-Identification-of-H3N2-Influenza-A-Virus-HA
AB - Antibodies are important therapeutic agents for the prevention and treatment of influenza, and hemagglutinin (HA) serves as the primary target for the development of therapeutic antibodies. However, the identification of epitopes remains an unmet need in the development of such therapeutic antibodies. This study investigates the characteristics of antibody–HA binding interfaces (i.e., epitopes and paratopes) through a computational analysis of all antibody–HA complexes with nonredundant experimentally determined structures from SAbDab, the largest reported antibody structure data set to date. After experimentally identifying and selecting H3N2 HA-specific binding antibodies, a reference antibody library was established. For anti-H3N2 HA antibodies with unknown structures, epitope grouping was conducted based on competitive binding assays that assess interference with the binding of antibodies from the reference library. Based on the complementary relationship between epitope and paratope structures, we selected the structure-based similarity measure most relevant to epitope convergence. Furthermore, we prioritized the experimental validation of binding interference between candidate antibodies and their most structurally similar reference antibodies. We present an integrated computational and experimental framework. This approach includes the construction of an H3N2-specific reference antibody set, computational modeling of antibodies, measurement of antibody similarity, and is complemented by antibody competition binding assays. The dry–wet iterative protocol presented here yielded accurate assessment of the neutralizing potency and breadth of the previously uncharacterized antibody P2A10. Together, the presented pipeline offers a powerful approach for identifying epitopes on anti-H3N2 HA antibodies and for epitope-targeted antibody discovery, while demonstrating the effectiveness of structural analytics in reducing experimental labor.
SN - 2470-1343
ER -