Secreted proteins play crucial roles in paracrine and endocrine signaling; however, identifying ligand-receptor interactions remains challenging. Here, we benchmarked AlphaFold2 (AF2) as a screening approach to identify extracellular ligands to single-pass transmembrane receptors. Key to the approach is the optimization of AF2 input and output for screening ligands against receptors to predict the most probable ligand-receptor interactions. The predictions were performed on ligand-receptor pairs not used for AF2 training. We demonstrate high discriminatory power and a success rate of close to 90% for known ligand-receptor pairs and 50% for a diverse set of experimentally validated interactions. Further, we show that screen accuracy does not correlate linearly with prediction of ligand-receptor interaction. These results demonstrate a proof of concept of a rapid and accurate screening platform to predict high-confidence cell-surface receptors for a diverse set of ligands by structural binding prediction, with potentially wide applicability for the understanding of cell-cell communication.
Keywords: AlphaFold; binding prediction; deorphanization; ligand-receptor interactions.
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