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Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Published on: April 17, 2017

Mapping antibody footprints using binding profiles.

Asaf Azulay^1,2, Liel Cohen-Lavi^2,3, Lilach M Friedman^1,2

¹The Shraga Segal Department of Microbiology, Immunology and Genetics, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

Cell Reports Methods

|September 6, 2023

Summary

This summary is machine-generated.

We developed a high-throughput method using antibody binding profiles to characterize monoclonal antibodies (mAbs) against influenza hemagglutinin (HA). This approach, including the mAb-Patch tool, efficiently predicts antibody epitopes and aids in selecting mAbs for further studies.

Keywords:

antibody characterization antibody footprints antibody profiling antigenic cartography epitope prediction mAb-Patch monoclonal antibody

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Area of Science:

Immunology
Structural Biology
Bioinformatics

Background:

Monoclonal antibodies (mAbs) are crucial in medicine and biology.
Efficient characterization of mAb binding epitopes is essential.
Influenza hemagglutinin (HA) is a key target for antibody development.

Purpose of the Study:

To develop a high-throughput antibody footprinting method using binding profiles.
To create an epitope prediction tool, mAb-Patch, based on binding data and antigen structure.
To characterize anti-influenza hemagglutinin (HA) mAbs and their specificities.

Main Methods:

Utilized an antigen microarray to profile 23 anti-influenza HA mAbs against HA proteins from 43 influenza strains (1918-2018).
Developed the mAb-Patch method integrating mAb binding profiles with antigen 3D structures for epitope prediction.
Validated mAb-Patch using four mAbs with known HA structures.

Main Results:

mAb binding profiles accurately characterized influenza subtype specificity, binding region, and binding site.
mAb-Patch successfully identified over 67% of true epitopes within limited antigen positions (50-60).
Demonstrated proof of concept for using binding profiles to screen and down-select mAbs.

Conclusions:

Antibody binding profiles offer a powerful strategy for high-throughput epitope characterization.
mAb-Patch provides an efficient computational tool for predicting antibody epitopes.
This method facilitates the selection of promising mAbs for functional validation in influenza research.