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Identifying endogenous peptide receptors by combining structure and transmembrane topology prediction
Felix Teufel · Jan Christian Refsgaard · Christian Toft Madsen · Carsten Stahlhut · Mads Grønborg · Dennis Madsen · Ole Winther

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in Machine Learning in Structural Biology Workshop »

Many secreted endogenous peptides rely on signalling pathways to exert their function in the body. While peptides can be discovered through high throughput technologies, their cognate receptors typically cannot, hindering the understanding of their mode of action. We investigate the use of AlphaFold-Multimer for identifying the cognate receptors of secreted endogenous peptides in human receptor libraries without any prior knowledge about likely candidates. We find that AlphaFold's predicted confidence metrics have strong performance for prioritizing true peptide-receptor interactions. By applying transmembrane topology prediction using DeepTMHMM, we further improve performance by detecting and filtering biologically implausible predicted 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.

Author Information

Felix Teufel (University of Copenhagen)
Jan Christian Refsgaard (Novo Nordisk A/S)
Christian Toft Madsen (Novo Nordisk A/S)
Carsten Stahlhut (Novo Nordisk A/S)
Mads Grønborg (Novo Nordisk A/S)
Dennis Madsen (Novo Nordisk A/S)
Ole Winther (Technical University of Denmark)

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