Attentive cross-modal paratope prediction
This work addresses antibody design for personalized medicine by providing a more efficient and accurate computational method for paratope prediction, representing an incremental improvement over existing deep learning approaches.
The paper tackled the problem of predicting antibody paratope residues by introducing a deep neural network that uses à trous convolutions, self-attention, and cross-modal attention between antibody and antigen residues, achieving new state-of-the-art results with improved computational efficiency compared to Parapred.
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 à 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.