Learning the interaction rules of antibody-antigen binding

Introduction

Antibody-antigen binding is the basis of two fundamental biotherapeutic pillars: monoclonal antibodies (1) and vaccines (2). To accelerate therapeutics discovery, we need to perform antibody (Ab) and antigen (Ag) design in silico. Specifically, we need to address a fundamental immuno-biotechnological challenge: understanding the interaction rules that predict Ab-Ag binding. Solving this challenge demands the convergence of biotechnology, computational structural biology, and machine learning (ML). My lab is one of the few worldwide to have this transdisciplinary expertise.

Research Problem

Currently, the predictive performance of Ab-Ag binding is poor, and an understanding of the underlying rules of Ab-Ag binding is mostly absent. We previously showed that both unprecedentedly large datasets (>10^5 Ab-Ag sequence pairs) and extensive structural information on the Ab-Ag binding interface (paratope, epitope) are needed to increase prediction accuracy and recover binding rules.

Targeted Breakthrough

To address the lack of large-scale Ab-Ag sequence and structural data, we will develop a method for high-throughput screening of:

1. 10^3 Ab paratope-mutated variants

2. 10^3 Ag epitope-mutated variants

This will generate sequence data of Ab-Ag binding pairs at an unprecedented scale (>10^6 sequence Ab-Ag pairs). Structural information of the entirety of the sequence-based Ab-Ag binding data will be generated by building and innovating on recent breakthroughs in computational structural biology.

To derive Ab-Ag interaction rules from the generated data, we will develop ML techniques for Ab-Ag binding prediction and rule recovery. We will demonstrate experimentally that we have begun to understand Ab-Ag interaction rules.

Impact

The proposed research generates the exact data necessary to recover the rules of Ab-Ag binding and provides a first groundbreaking insight into those rules, moving us closer to in silico on-demand antibody and vaccine design.