Deorphanizing Peptides Using Structure Prediction

Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. W...

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Veröffentlicht in:Journal of chemical information and modeling 2023-05, Vol.63 (9), p.2651-2655
Hauptverfasser: Teufel, Felix, Refsgaard, Jan C., Kasimova, Marina A., Deibler, Kristine, Madsen, Christian T., Stahlhut, Carsten, Grønborg, Mads, Winther, Ole, Madsen, Dennis
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Sprache:eng
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Zusammenfassung:Many endogenous peptides rely on signaling pathways to exert their function, but identifying their cognate receptors remains a challenging problem. We investigate the use of AlphaFold-Multimer complex structure prediction together with transmembrane topology prediction for peptide deorphanization. We find that AlphaFold’s confidence metrics have strong performance for prioritizing true peptide–receptor interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide–receptor pairs.
ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.3c00378