%0 Journal Article
%T Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.
%A Chen H
%A Fan X
%A Zhu S
%A Pei Y
%A Zhang X
%A Zhang X
%A Liu L
%A Qian F
%A Tian B
%J Elife
%V 12
%N 0
%D 2024 Jun 26
%M 38921957
%F 8.713
%R 10.7554/eLife.91512
%X 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.