Identifying endogenous peptide receptors by combining structure and transmembrane topology prediction

Abstract

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.

AlphaFold2 enables accurate deorphanization of ligands to single-pass receptors

“…3l-p). The ranking of receptors was not significantly different when using the average ipTM, median ipTM, penalized ipTM, or pDockQ 22,38 , demonstrating robust prediction with low variability ( Extended Data Fig. 3b ).…”

“…3a) . To rank predictions we used the ipTM value of five predictions from AF2 and penalized receptors with high variation as previously described 22 . Remarkably, with one exception, the correct receptors for the eight test cases consistently ranked among the top three receptors with ipTM values between 0.6-0.8 for all ligand-receptor pairs.…”

“…The most striking finding was the accuracy in the identification of seven receptor-ligand pairs where the true receptor was identified within the top three receptors in a library with over 1,100 receptors. Based on the data presented in this paper, this resource could potentially be expanded to include other classes of cell-surface receptors, including ion-gated channels, multi-pass receptors, and G-protein coupled receptors (GPCRs) 21,22 .…”

“…3a). To rank predictions we used the ipTM value of five predictions from AF2 and penalized receptors with high variation as previously described 22 .…”

“…With the revolutionizing ability to predict protein 3D structures from their amino acid sequences, AlphaFold has become an omnipresent tool in the field of structural biology 16,17 . As the 3D structure of a protein is closely related to its function and interactions with other molecules, AlphaFold has been tremendously useful in predicting intracellular protein-protein interactions, heterodimeric protein complex [18][19][20] , as well as extracellular interactions using structure and topology prediction 21,22 . However, a reliable method to assign the binding of secreted ligands to single-pass transmembrane receptors has not previously been developed.…”