In this study we show that protein language models can encode structural and functional information of GPCR sequences that can be used to predict their signaling and functional repertoire. We used the ESM1b protein embeddings as features and the binding information known from publicly available studies to develop PRECOGx, a machine learning predictor to explore GPCR interactions with G protein and β-arrestin, which we made available through a new webserver (https://precogx.bioinfolab.sns.it/). PRECOGx outperformed its predecessor (e.g. PRECOG) in predicting GPCR-transducer couplings, being also able to consider all GPCR classes. The webserver also provides new functionalities, such as the projection of input sequences on a low-dimensional space describing essential features of the human GPCRome, which is used as a reference to track GPCR variants. Additionally, it allows inspection of the sequence and structural determinants responsible for coupling via the analysis of the most important attention maps used by the models as well as through predicted intramolecular contacts. We demonstrate applications of PRECOGx by predicting the impact of disease variants (ClinVar) and alternative splice forms from healthy tissues (GTEX) of human GPCRs, revealing the power to dissect system biasing mechanisms in both health and disease.

GPCRs are master regulators of cell signaling by transducing extracellular stimuli into the cell via selective coupling to intracellular G-proteins. Here we present a computational analysis of the structural determinants of G-protein-coupling repertoire of experimental and predicted 3D GPCR-G-protein complexes. Interface contact analysis recapitulates structural hallmarks associated with G-protein-coupling specificity, including TM5, TM6 and ICLs. We employ interface contacts as fingerprints to cluster Gs vs Gi complexes in an unsupervised fashion, suggesting that interface residues contribute to selective coupling. We experimentally confirm on a promiscuous receptor (CCKAR) that mutations of some of these specificity-determining positions bias the coupling selectivity. Interestingly, Gs-GPCR complexes have more conserved interfaces, while Gi/o proteins adopt a wider number of alternative docking poses, as assessed via structural alignments of representative 3D complexes. Binding energy calculations demonstrate that distinct structural properties of the complexes are associated to higher stability of Gs than Gi/o complexes. AlphaFold2 predictions of experimental binary complexes confirm several of these structural features and allow us to augment the structural coverage of poorly characterized complexes such as G12/13.

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We explored the dysregulation of GPCR ligand signaling systems in cancer transcriptomics datasets. We derived a network of interacting ligands and biosynthetic enzymes from public databases, that we combined with cognate GPCRs and downstream effectors to quantify GPCR signaling pathways. We found multiple GPCRs differentially regulated together with their ligands across cancers, suggesting a widespread perturbation of these signaling axes in specific cancer molecular subtypes. We showed that biosynthetic pathway enrichment from enzyme expression recapitulates pathway activity signatures from metabolomics datasets, therefore providing valuable surrogate information for receptor-organic ligand systems. The expression of several GPCRs signaling components is significantly associated with either lower or higher patient survival in a cancer subtype specific fashion. In particular, the expression of both receptor-ligand (or biosynthetic enzyme) interaction partners improves the stratification of patients based on survival, suggesting a potential synergistic role for the activation of certain receptor axes in modulating cancer phenotypes. Strikingly, we have found that receptors from these actionable axes are the targets of several drugs displaying anti-growth effects in large, drug repurposing screens in cancer cell lines. This study provides a comprehensive map to exploit GPCR signaling axes as actionable targets for personalized cancer treatments. We have made the results generated in this study freely available for further exploration to the community through a webapp (gpcrcanceraxes.bioinfolab.sns.it).