ANTIPASTI: interpretable prediction of antibody binding affinity exploiting Normal Modes and Deep Learning

TL;DR

ANTIPASTI is a deep learning model that predicts antibody binding affinity using structure-based Normal Mode features, offering interpretability and insights into binding mechanisms.

Contribution

The paper introduces ANTIPASTI, a novel CNN model that combines Normal Mode analysis with deep learning for interpretable antibody affinity prediction.

Findings

Normal Mode features improve prediction accuracy

Model reveals binding pattern similarities among antibodies

Long-range correlations influence binding affinity

Abstract

The high binding affinity of antibodies towards their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a Convolutional Neural Network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of Normal Mode correlation maps derived from Elastic Network Models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the Normal Mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.