Name
Abstract | ||
|---|---|---|
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. Here, we present DeepAb, a deep learning method for predicting accurate antibody FV structures from sequence. We evaluate DeepAb on a set of structurally diverse, therapeutically relevant 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 our network attends to physically important residue pairs (e.g., proximal aromatics and key hydrogen bonding interactions). Finally, we present a novel mutant scoring metric derived from network confidence and show that for a particular antibody, all eight of the top-ranked mutations improve binding affinity. This model will be useful for a broad range of antibody prediction and design tasks. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patter.2021.100406 | Patterns |
Keywords | DocType | Volume |
antibody design,deep learning,protein structure prediction,model interpretability | Journal | 3 |
Issue | ISSN | Citations |
2 | 2666-3899 | 2 |
PageRank | References | Authors |
0.89 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Ruffolo | 1 | 2 | 1.57 |
| Jeremias Sulam | 2 | 2 | 0.89 |
| Jeffrey J Gray | 3 | 2 | 0.89 |