Accurate prediction of CDR-H3 loop structures of antibodies with deep learning
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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 Å average RMSD Cα 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.
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Flagged first, then by citations
- Authors
- Chen, H., Fan, X., Zhu, S., Pei, Y., Zhang, X., Zhang, X., Liu, L., Qian, F., & Tian, B.
- Affiliations
- Goethe University Frankfurt (DE)
- Corresponding author
- Qian, Feng
- Tian, Boxue
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2024-06-26
- Volume
- 12
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC11208048
- OpenAlex
- W4387745032
- Cited by
- 8
- References
- 20
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 1
- FWCI
- 0.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Tsinghua University (Initiative Scientific Research Program 20231080030)
- Vanke Special Fund for Public Health and Health Discipline Development (2022Z82WKJ009)
- Tsinghua-Peking University Center for Life Sciences (20111770319)
- Tsinghua University-Peking Union Medical College and Hospital Collaboration Foundation (20191080837)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- monoclonal antibodies, nanobodies, AlphaFold2, protein language models, antigen-antibody reaction
- MeSH terms
- Deep Learning, Antibodies, Monoclonal, Humans, Models, Molecular, Protein Conformation, Complementarity Determining Regions, Single-Domain Antibodies
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