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Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.
- Source :
-
eLife . 6/26/2024, p1-27. 27p. - Publication Year :
- 2024
-
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 Å average RMSDCa 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2050084X
- Database :
- Academic Search Index
- Journal :
- eLife
- Publication Type :
- Academic Journal
- Accession number :
- 178202986
- Full Text :
- https://doi.org/10.7554/eLife.91512