Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2023.04.20.537598v1?rss=1
Authors: Peng, C., Wang, Z., Zhao, P., Ge, W., Huang, C.
Abstract:
Motivation: Antibodies are a group of proteins generated by B cells, which are crucial for the immune system. The importance of antibodies is ever-growing in pharmaceutics and biotherapeutics. Despite recent advancements pioneered by AlphaFold in general protein 3D structure prediction, accurate structure prediction of antibodies still lags behind, primarily due to the difficulty in modeling the Complementarity-determining regions (CDRs), especially the most variable CDR-H3 loop. Results: This paper presents AbFold, a transfer learning antibody structure prediction model with 3D point cloud refinement and unsupervised learning techniques. AbFold consistently produces state-of-the-art results on the prediction accuracy of the six CDR loops. The predictions of AbFold achieve an average RMSD of 1.51 [A] for both heavy and light chains and an average RMSD of 3.04 [A] for CDR-H3, bettering current models AlphaFold and IgFold. AbFold will contribute to antibody structure prediction and design processes.
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