Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies
By Jeffrey A. Ruffolo, Lee-Shin Chu, Sai Pooja Mahajan, Jeffrey J. Gray. bioRxiv, 2022
344 citations recorded by the source corpus.
Prediction of antibody structures is critical for understanding and designing novel therapeutic and diagnostic molecules. Here, the authors present IgFold: a fast, accurate method for antibody structure prediction using an end-to-end deep learning model. Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold’s capabilities, we predicted structures for 1.4 million paired antibody sequences, providing structural insights to 500-fold more antibodies than have experimentally determined structures.
Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/81e20d4641b6261c23184f98fd1585d64c85eb40
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In Molecular Biology
- Sheng Wang
- Jinbo Xu
- Jianzhu Ma
- Jian Peng
- Ali Madani
- Jeffrey A. Ruffolo
- Erik Nijkamp
- Nikhil Vijay Naik
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (2022, methodology): “Recently, based on prior protein structure prediction algorithms, methods for predicting antibody CDR structures have emerged [40, 39], but they are not able to design CDR sequences.”
- ProGen2: Exploring the Boundaries of Protein Language Models (2022, methodology): “IgFold (Ruffolo et al., 2022) was used to predict structures for all generated antibody sequences.”