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Cited by 8Protein Structure and DynamicsMonoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approaches

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Evaluation of AlphaFold modeling for elucidation of nanobody–peptide epitope interactions

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Sachdev, Shivani, Roy, Swarnali, Saha, Shubhra J., Zhao, Gengxiang, Kumariya, Rashmi, Creemer, Brendan A., Yin, Rui, Pierce, Brian G., Bewley, Carole A., Cheloha, Ross W.·2025·Journal of Biological Chemistry
DOI 10.1016/j.jbc.2025.110268PMC PMC12212259

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Abstract

Models of Ab-antigen complexes can be used to understand interaction mechanisms and for improving affinity. This study evaluates the use of the protein structure prediction algorithm AlphaFold (AF) for exploration of interactions between peptide epitope tags and the smallest functional antibody fragments, nanobodies (Nbs). Although past studies of AF for modeling antibody-target (antigen) interactions suggested modest algorithm performance, those were primarily focused on Ab-protein interactions, while the performance and utility of AF for Nb-peptide interactions, which are generally less complex due to smaller antigens, smaller binding domains, and fewer chains, is less clear. In this study we evaluated the performance of AF for predicting the structures of Nbs bound to experimentally validated, linear, short peptide epitopes (Nb-tag pairs). We expanded the pool of experimental data available for comparison through crystallization and structural determination of a previously reported Nb-tag complex (Nb127). Models of Nb-tag pair structures generated from AF were variable with respect to consistency with experimental data, with good performance in just over half (4 out of 6) of cases. Even among Nb-tag pairs successfully modeled in isolation, efforts to translate modeling to more complex contexts failed, suggesting an underappreciated role of the size and complexity of inputs in AF modeling success. Finally, the model of a Nb-tag pair with minimal previous characterization was used to guide the design of a peptide-electrophile conjugate that undergoes covalent crosslinking with Nb upon binding. These findings highlight the utility of minimized antibody and antigen structures to maximize insights from AF modeling.

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Metadata

Authors
Sachdev, S., Roy, S., Saha, S. J., Zhao, G., Kumariya, R., Creemer, B. A., Yin, R., Pierce, B. G., Bewley, C. A., & Cheloha, R. W.
Affiliations
  • National Institutes of Health (US)
  • Institute for Bioscience and Biotechnology Research (US)
Corresponding author
Cheloha, Ross W.
ORCID
Venue
Journal of Biological Chemistry
Publisher
Elsevier BV
Published
2025-07
Volume
301
Issue
7
Pages
110268
ISSN
0021-9258 (ISSN-L)
DOI
10.1016/j.jbc.2025.110268
PMC
PMC12212259
OpenAlex
W4410558146
Cited by
8
References
6
Citations by year
2026: 5 · 2025: 3
FWCI
2.40
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Biological Chemistry (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, public-domain)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, public-domain)
  • Europe PMC (PubMed Central) (submittedVersion, public-domain)
Funders
  • NIH National Institute of General Medical Sciences (GM144083)
  • National Institutes of Health
  • National Institute of Diabetes and Digestive and Kidney Diseases (1ZIADK075157, 1ZIADK075184, ZIADK032103)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
Keywords
AlphaFold, nanobodies, peptide epitopes, epitope tags, antigen-antibody reaction, protein structure prediction, crystallization, structure determination, covalent cross-linking, binding affinity
MeSH terms
Algorithms, Antigen-Antibody Complex, Epitopes, Humans, Models, Molecular, Peptides, Protein Binding, Single-Domain Antibodies

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