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

• DOI: 10.7554/elife.91512.4
• Publication Date: 2024-06-26T13:22:57Z
• Authors: Hedi Chen; Xiaoyu Fan; Shuqian Zhu; Yuchan Pei; Xiaochun Zhang; Xiaonan Zhang; Lihang Liu; Feng Qian; Boxue Tian
• Keywords: CDR-H3; Models, Molecular; 0301 basic medicine; 0303 health sciences; QH301-705.5; Protein Conformation; Science; Q; R; deep learning; Antibodies, Monoclonal; Single-Domain Antibodies; Complementarity Determining Regions; nanobody; Deep Learning; antibody; Medicine; Humans; Biology (General); Computational and Systems Biology

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 Å average RMSDCα 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 engineer therapeutic antibodies with biophysical properties for specialized drug administration route.