Attentive cross-modal paratope prediction

Andreea Deac, Petar Veličkovi´c, Pietro Sormanni

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, using just the
antibody data, we outperform the results of Parapred by producing a model which
is computationally significantly more efficient by using `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.

Keywords: à trous, antibody, attention, antigen, cross-modal, paratope, self-attention

TL;DR: We use self and cross-modal attention to predict binding probabilities of antibody residues, obtaining state-of-the-art performance as well as new qualitative insights.