Attentive cross-modal paratope prediction

TL;DR

This paper introduces a novel deep learning model for cross-modal paratope prediction that significantly improves efficiency and accuracy by integrating e0 trous convolutions, self-attention, and cross-modal attention mechanisms, advancing antibody-antigen interaction understanding.

Contribution

The paper presents a more efficient neural network architecture with cross-modal attention for paratope prediction, achieving state-of-the-art results and providing interpretability.

Findings

Outperforms Parapred in accuracy and efficiency

Achieves state-of-the-art results on paratope prediction

Provides interpretable attention mechanisms

Abstract

Antibodies are a critical part of the immune system, having the function of directly neutralising or tagging undesirable objects (the antigens) for future destruction. Being able to predict which amino acids belong to the paratope, the region on the antibody which binds to the antigen, can facilitate antibody design and contribute to the development of personalised medicine. The suitability of deep neural networks has recently been confirmed for this task, with Parapred outperforming all prior physical models. Our contribution is twofold: first, we significantly outperform the computational efficiency of Parapred by leveraging \`a trous convolutions and self-attention. Secondly, we implement cross-modal attention by allowing the antibody residues to attend over antigen residues. This leads to new state-of-the-art results on this task, along with insightful interpretations.