Machine learning approaches for interpretable antibody property prediction using structural data

TL;DR

This paper reviews machine learning methods that incorporate antibody structural data to improve property prediction and interpretability, highlighting two frameworks that use graph-based neural networks for global and local antibody properties.

Contribution

It introduces two novel ML frameworks integrating structural data with neural networks for antibody property prediction and provides a comprehensive overview of these approaches.

Findings

ANTIPASTI predicts binding affinity effectively.

INFUSSE estimates residue flexibility accurately.

Structural data integration enhances interpretability of antibody models.

Abstract

Understanding the relationship between antibody sequence, structure and function is essential for the design of antibody-based therapeutics and research tools. Recently, machine learning (ML) models mostly based on the application of large language models to sequence information have been developed to predict antibody properties. Yet there are open directions to incorporate structural information, not only to enhance prediction but also to offer insights into the underlying molecular mechanisms. This chapter provides an overview of these approaches and describes two ML frameworks that integrate structural data (via graph representations) with neural networks to predict properties of antibodies: ANTIPASTI predicts binding affinity (a global property) whereas INFUSSE predicts residue flexibility (a local property). We survey the principles underpinning these models; the ways in which they encode structural knowledge; and the strategies that can be used to extract biologically relevant statistical signals that can help discover and disentangle molecular determinants of the properties of interest.