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bioRxiv · 10.1101/2023.08.19.553933

H3-OPT: Accurate prediction of CDR-H3 loop structures of antibodies with deep learning

Tian, B.·Chen, H.·Fan, X.·Zhu, S.·Pei, Y.·Zhang, X.·Liu, L.·Qian, F.
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Abstract

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model, and provides a 2.24 [A] average RMSDC between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding, and to engineer therapeutic antibodies with biophysical properties for specialized drug administration route.

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Tian, B., Chen, H., Fan, X., Zhu, S., Pei, Y., Zhang, X., Liu, L., Qian, F.. 2023-08-21. H3-OPT: Accurate prediction of CDR-H3 loop structures of antibodies with deep learning. https://doi.org/10.1101/2023.08.19.553933

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