# High-throughput identification of antibody features for sequence-based epitope prediction

> **NIH NIH DP2** · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · 2021 · $1,427,400

## Abstract

PROJECT SUMMARY
Human antibody repertoire is highly diverse due to VDJ recombination and somatic hypermutation. VDJ
recombination is a somatic recombination process that assembles the variable region of an antibody from a
diverse set of gene segments, known as variable (V), diversity (D), and joining (J) genes. During the course of
an immune response, antibodies will increase affinity to their antigens through somatic hypermutation. The
huge diversity of antibodies enables human immune system to confer protection against various pathogens by
recognizing a wide range of antigens and epitopes. Detailed molecular characterization of antibody-antigen
interaction is crucial to vaccine and therapeutic development, as well as the fundamental understanding of the
human immune system.
 The binding specificity and epitope of an antibody are determined by its structure, which in turn is
determined by its amino acid sequence. As a result, information on the binding specificity and epitope of an
antibody are encoded in its amino acid sequence. However, accurately predicting the epitopes of antibodies
from their sequences is an extremely difficult task because our understanding of antibody sequence-function
relationship is far from comprehensive. This proposal aims to develop a library-to-library screening approach to
characterize antibody-antigen interaction in a high-throughput manner, with a focus on influenza A
hemagglutinin (HA) as a proof-of-concept. Specifically, we will determine the HA-binding specificity and
conformational epitope of hundreds of thousands of antibodies in a single experiment. Subsequently, antibody
sequence features that are associated with different epitopes on HA will be systematically identified. We further
aim to use these antibody features to identify HA-binding antibodies from publicly available antibody repertoire
sequencing datasets as well as predict their epitopes on HA. While this proposed project focuses on influenza
HA, our approach can be easily extended to any antigen of interest. This proposal will open up the possibility
for antibody sequence-based epitope prediction and provides new perspectives to the understanding of human
antibody repertoire.

## Key facts

- **NIH application ID:** 10243575
- **Project number:** 1DP2AT011966-01
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
- **Principal Investigator:** Nicholas C. Wu
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,427,400
- **Award type:** 1
- **Project period:** 2021-09-17 → 2024-09-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10243575

## Citation

> US National Institutes of Health, RePORTER application 10243575, High-throughput identification of antibody features for sequence-based epitope prediction (1DP2AT011966-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10243575. Licensed CC0.

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