ArticleNature communications2026
In silico discovery of nanobody binders to a G-protein coupled receptor using AlphaFold-Multimer.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Combining AI Structure Prediction and Integrative Modeling for Nanobody-Antigen Complexes.Journal of chemical information and modeling · 2026Article
- Multivalent antibody-based conjugates as new tools for tailored modulation of G protein-coupled receptors.British journal of pharmacology · 2026Review
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Nanobodies unlock new mechanisms to target G protein-coupled receptors.Molecular pharmacology · 2026Review
- Advances in Therapeutic Antibody Discovery and Development Targeting G Protein-Coupled Receptors.Pharmacology research & perspectives · 2026Review
- Machine learning enables efficient and effective affinity maturation of nanobodies.bioRxiv : the preprint server for biology · 2026Article
- Nanobody-drug conjugates for targeting specific GPCR pairs.PLoS biology · 2025Article
- Progress toward new function and design of extracellular G protein-coupled receptor nanobodies.Molecular pharmacology · 2025Review
Corrections and comments
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Authors and funding
17 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.