In silico discovery of nanobody binders to a G-protein coupled receptor using AlphaFold-Multimer
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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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could directly compete with the MRGPRX2 agonist 48/80 in its ability to activate the G protein Gi, an established signaling output of MRGPRX2
I wonder if this might be a better success metric than binding, if the goal is to predict nanobodies that could be developed into therapeutics?
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This receptor was chosen because its shallow binding pocket suggests a more forgiving thermodynamic landscape relative to other GPCRs
MrgprX2 is highly promiscuous (binds a huge list of basic secretagogues). Do you think this makes it easier to predict a functional nanobody? Maybe this is exactly what you're getting at here
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