Challenges in antibody structure prediction
Open accessPublished version of a preprint
Abstract
Advances in structural biology and the exponential increase in the amount of high-quality experimental structural data available in the Protein Data Bank has motivated numerous studies to tackle the grand challenge of predicting protein structures. In 2020 AlphaFold2 revolutionized the field using a combination of artificial intelligence and the evolutionary information contained in multiple sequence alignments. Antibodies are one of the most important classes of biotherapeutic proteins. Accurate structure models are a prerequisite to advance biophysical property predictions and consequently antibody design. Specialized tools used to predict antibody structures based on different principles have profited from current advances in protein structure prediction based on artificial intelligence. Here, we emphasize the importance of reliable protein structure models and highlight the enormous advances in the field, but we also aim to increase awareness that protein structure models, and in particular antibody models, may suffer from structural inaccuracies, namely incorrect cis-amide bonds, wrong stereochemistry or clashes. We show that these inaccuracies affect biophysical property predictions such as surface hydrophobicity. Thus, we stress the importance of carefully reviewing protein structure models before investing further computing power and setting up experiments. To facilitate the assessment of model quality, we provide a tool "TopModel" to validate structure models.
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Cites
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Lidbrink, S. E., Howard, R. J., Haloi, N., & Lindahl, E. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013187 · Cited by 9 · see citing works
- Authors
- Fernández-Quintero, M. L., Kokot, J., Waibl, F., Fischer, A.-L. M., Quoika, P. K., Deane, C. M., & Liedl, K. R.
- Affiliations
- University of Innsbruck
- Universität Innsbruck (AT)
- Technical University of Munich (DE)
- University of Oxford (GB)
- Corresponding author
- Liedl, Klaus R.
- ORCID
- Venue
- mAbs
- Publisher
- Informa UK Limited
- Published
- 2023-02-12
- Volume
- 15
- Issue
- 1
- Pages
- 2175319-2175319
- ISSN
- 1942-0862 (ISSN-L)
- 1942-0870
- PMC
- PMC9928471
- OpenAlex
- W4320495379
- Cited by
- 55
- References
- 11
- Citations by year
- 2026: 17 · 2025: 14 · 2024: 14 · 2023: 10
- FWCI
- 4.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- mAbs (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Austrian Science Fund (P34518)
- the Austrian Academy of sciences APART-MINT postdoctoral fellowship to M.L.F.Q
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Keywords
- antibody structure prediction, protein structure model, structural accuracy, steric clashes, surface hydrophobicity, antibody design, model validation, TOPMODEL, AlphaFold2
- MeSH terms
- Antibodies, Artificial Intelligence, Protein Conformation, Proteins, Computational Biology, Databases, Protein
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