What is it about?

This study relies on a multilayered bioinformatic approach that leverages genomic data across diverse species in combination with AI-based structural modeling to identify true ligand and receptor homologues and subsequently predict their binding mechanisms. In silico findings were validated by multiple experimental approaches, which investigated the effect of amino acid changes in the proposed binding pockets on ligand-binding, complex formation with a coreceptor essential for downstream signaling, and activation of downstream signaling.

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Why is it important?

This study presents proof-of-concept for a rapid and inexpensive alternative to classical structure-based approaches for resolving ligand–receptor binding mechanisms, which could be easily implemented by most laboratories.

Perspectives

AI-driven complex predictions are improving and thus will play an important role in unraveling other peptide–receptor interactions in the future.

Simon Snoeck
Universitat Zurich

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This page is a summary of: Leveraging coevolutionary insights and AI-based structural modeling to unravel receptor–peptide ligand-binding mechanisms, Proceedings of the National Academy of Sciences, August 2024, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2400862121.
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