Antibody structure prediction using interpretable deep learning [article]

Jeffrey A Ruffolo, Jeremias Sulam, Jeffrey J. Gray
2021 bioRxiv   pre-print
Therapeutic antibodies make up a rapidly growing segment of the biologics market. However, rational design of antibodies is hindered by reliance on experimental methods for determining antibody structures. In recent years, deep learning methods have driven significant advances in general protein structure prediction. Here, we present DeepAb, a deep learning method for predicting accurate antibody FV structures from sequence. We evaluate DeepAb on two benchmark sets - one balanced for structural
more » ... diversity and the other composed of clinical-stage therapeutic antibodies - and find that our method consistently outperforms the leading alternatives. Previous deep learning methods have operated as "black boxes" and offered few insights into their predictions. By introducing a directly interpretable attention mechanism, we show that our network attends to physically important residue pairs. For example, in prediction of one CDR H3 residue conformation, the network attends to proximal aromatics and a key hydrogen bonding interaction that constrain the loop conformation. Finally, we present a novel mutant scoring metric derived from network confidence and show that for a particular antibody, all ten of the top-ranked mutations improve binding affinity. These results suggest that this model will be useful for a broad range of antibody prediction and design tasks.
doi:10.1101/2021.05.27.445982 fatcat:zxblrtj6zbf7dh5zj4kxju5czi