In silico discovery of nanobody binders to a G-protein coupled receptor using AlphaFold-Multimer
Open accessPublished version of a preprint
Abstract
Antibodies are central mediators of the adaptive immune response, and they are powerful research tools and therapeutics. Antibody discovery requires substantial experimental effort, such as immunization campaigns or in vitro library screening. Predicting antibody-antigen binding a priori remains challenging. However, recent machine learning methods raise the possibility of in silico antibody discovery, bypassing or reducing initial experimental bottlenecks. Here, we report a virtual screen using AlphaFold-Multimer (AF-M) that prospectively identified nanobody binders to MRGPRX2, a G protein-coupled receptor (GPCR) and therapeutic target for the treatment of pseudoallergic inflammation and itch. Using previously reported nanobody-GPCR structures, we identified a set of AF-M outputs that effectively discriminate between interacting and non-interacting nanobody-GPCR pairs. We used these outputs to perform a prospective in silico screen, identified nanobodies that bind MRGPRX2 with high affinity, and confirmed activity in signaling and functional cellular assays. Our results provide a proof of concept for fully computational antibody discovery pipelines that can circumvent laboratory experiments.
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Combining AI Structure Prediction and Integrative Modeling for Nanobody-Antigen Complexes
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“…deep-learning – based structure prediction tools such as AlphaFold and RoseTTAFold have been integrated with generative design engines, including RFdiffusion, to diversity and expand binder design (Abramson et al., 2024; Adolf-Bryfogle et al., 2018; Harvey et al., 2025; Watson et al., 2023).”
- Authors
- Harvey, E. P., Smith, J. S., Hurley, J. D., Granados, A. J., Schmid, E. W., Liang-Lin, J. G., Zhang, H., Meara, E. M., Wren, E. K., Paul, S., Ferguson, M. P., Calvillo-Miranda, V. G., Alcantar, M. A., Marks, D. S., Walter, J. C., Kruse, A. C., & Susa, K. J.
- Affiliations
- Harvard University (US)
- Brigham and Women's Hospital (US)
- University of California, San Francisco (US)
- Broad Institute (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Kruse, Andrew C.
- Susa, Katherine J.
- ORCID
- Venue
- Nature Communications
- Published
- 2026-04-23
- Volume
- 17
- Issue
- 1
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4408279937
- Cited by
- 7
- References
- 48
- Citations by year
- 2026: 5 · 2025: 2
- FWCI
- 17.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (DP5OD036136)
- National Heart, Lung, and Blood Institute (R01HL098316)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Keywords
- G protein-coupled receptors, MRGPRX2, AlphaFold-Multimer, virtual screening, pruritus, receptor signaling
- MeSH terms
- Animals, Computer Simulation, Humans, Nerve Tissue Proteins, Protein Binding, Receptors, Neuropeptide, Receptors, G-Protein-Coupled, HEK293 Cells, Single-Domain Antibodies
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