Recurrent high-impact mutations at cognate structural positions in class A G protein-coupled receptors expressed in tumors

An in silico framework to visualize how cancer-associated mutations influence structural plasticity of the chemokine receptor CCR3

G protein Coupled Receptors (GPCRs) are the largest family of cell surface receptors in humans. Somatic mutations in GPCRs are implicated in cancer progression and metastasis, but mechanisms are poorly understood. Emerging evidence implicates perturbation of intra-receptor activation pathway motifs whereby extracellular signals are transmitted intracellularly. Recently, sufficiently sensitive methodology was described to calculate structural strain as a function of missense mutations in AlphaFold-predicted model structures, which was extensively validated on experimental and predicted structural datasets. When paired with Molecular Dynamics (MD) simulations, these tools provide a facile approach to screen mutations in silico. We applied this framework to calculate the structural and dynamic effects of cancer-associated mutations in the chemokine receptor CCR3, a Class A GPCR involved in cancer and autoimmune disorders. Residue-residue contact scoring refined effective strain results, highlighting significant remodeling of inter- and intra-motif contacts along the highly conserved GPCR activation pathway network. We then integrated AlphaFold-derived predicted Local Distance Difference Test scores with per-residue Root Mean Square Fluctuations and activation pathway Contact Analysis (CONAN) from coarse grain MD simulations to identify statistically significant changes in receptor dynamics upon mutation. Finally, analysis of negative control mutants suggests false positive results in AlphaFold pipelines should be considered but can be mitigated with stricter control of statistical analysis. Our results indicate selected mutants influence structural plasticity of CCR3 related to ligand interaction, activation, and G protein coupling, using a framework that could be applicable to a wide range of biochemically relevant protein targets following further validation.

Complex N-glycosylation of mGluR6 is required for trans-synaptic interaction with ELFN adhesion proteins

Synaptic transmission from retinal photoreceptors to downstream ON-type bipolar cells (BCs) depends on the postsynaptic metabotropic glutamate receptor mGluR6, located at the BC dendritic tips. Glutamate binding to mGluR6 initiates G-protein signaling that ultimately leads to BC depolarization in response to light. The mGluR6 receptor also engages in trans-synaptic interactions with presynaptic ELFN adhesion proteins. The roles of post-translational modifications in mGluR6 trafficking and function are unknown. Treatment with glycosidase enzymes PNGase F and Endo H demonstrated that both endogenous and heterologously expressed mGluR6 contain complex N-glycosylation acquired in the Golgi. Pull-down experiments with ELFN1 and ELFN2 extracellular domains revealed that these proteins interact exclusively with the complex glycosylated form of mGluR6. Mutation of the four predicted N-glycosylation sites, either singly or in combination, revealed that all four sites are glycosylated. Single mutations partially reduced, but did not abolish, surface expression in heterologous cells, while triple mutants had little or no surface expression, indicating that no single glycosylation site is necessary or sufficient for plasma membrane trafficking. Mutation at N445 severely impaired both ELFN1 and ELFN2 binding. All single mutants exhibited dendritic tip enrichment in rod BCs, as did the triple mutant with N445 as the sole N-glycosylation site, demonstrating that glycosylation at N445 is sufficient but not necessary for dendritic tip localization. The quadruple mutant was completely mislocalized. These results reveal a key role for complex N-glycosylation in regulating mGluR6 trafficking and ELFN binding, and by extension, function of the photoreceptor synapses.

Application of bioluminescence resonance energy transfer to quantitate cell-surface expression of membrane proteins

We report a bioluminescence resonance energy transfer (BRET) assay to quantitate the fraction of an engineered membrane protein at the cell surface versus inside the cell. As test cases, we engineered two different G protein-coupled receptors (GPCRs) in which a NanoLuc luciferase (NLuc) and a HaloTag are fused to the extracellular amino-terminal tail of the receptors. We then employed a pulse-chase labeling approach relying on two different fluorescent dyes with distinctive cell permeability properties. The dyes are efficiently excited by luminescence from NLuc, but are spectrally distinct. Measuring BRET from the chemiluminescence of the NLuc to the fluorophores bound to the HaloTag minimizes the limitations of in-cell fluorescence resonance energy transfer (FRET)-based approaches such as photobleaching and autofluorescence. The BRET surface expression assay can quantitatively differentiate between the labeling of receptors at the cell surface and receptors inside of the cell. The assay is shown to be quantitative and robust compared with other approaches to measure cell surface expression of membrane proteins such as enzyme-linked immunosorbent assay or immunoblotting, and significantly increases the throughput because the assay is designed to be carried out in microtiter plate format.

GPCRome-wide analysis of G-protein-coupling diversity using a computational biology approach

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 G_s vs G_i 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, G_s-GPCR complexes have more conserved interfaces, while G_i/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 G_s than G_i/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 G_12/13.

Coevolutionary signals in metabotropic glutamate receptors capture residue contacts and long-range functional interactions

Upon ligand binding to a G protein-coupled receptor, extracellular signals are transmitted into a cell through sets of residue interactions that translate ligand binding into structural rearrangements. These interactions needed for functions impose evolutionary constraints so that, on occasion, mutations in one position may be compensated by other mutations at functionally coupled positions. To quantify the impact of amino acid substitutions in the context of major evolutionary divergence in the G protein-coupled receptor subfamily of metabotropic glutamate receptors (mGluRs), we combined two phylogenetic-based algorithms, Evolutionary Trace and covariation Evolutionary Trace, to infer potential structure-function couplings and roles in mGluRs. We found a subset of evolutionarily important residues at known functional sites and evidence of coupling among distinct structural clusters in mGluR. In addition, experimental mutagenesis and functional assays confirmed that some highly covariant residues are coupled, revealing their synergy. Collectively, these findings inform a critical step toward understanding the molecular and structural basis of amino acid variation patterns within mGluRs and provide insight for drug development, protein engineering, and analysis of naturally occurring variants.

Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors

G Protein-coupled receptors (GPCRs) are the most frequently exploited drug target family, moreover they are often found mutated in cancer. Here we used a dataset of mutations found in patient samples derived from the Genomic Data Commons and compared it to the natural human variance as exemplified by data from the 1000 genomes project. We explored cancer-related mutation patterns in all GPCR classes combined and individually. While the location of the mutations across the protein domains did not differ significantly in the two datasets, a mutation enrichment in cancer patients was observed among class-specific conserved motifs in GPCRs such as the Class A "DRY" motif. A Two-Entropy Analysis confirmed the correlation between residue conservation and cancer-related mutation frequency. We subsequently created a ranking of high scoring GPCRs, using a multi-objective approach (Pareto Front Ranking). Our approach was confirmed by re-discovery of established cancer targets such as the LPA and mGlu receptor families, but also discovered novel GPCRs which had not been linked to cancer before such as the P2Y Receptor 10 (P2RY10). Overall, this study presents a list of GPCRs that are amenable to experimental follow up to elucidate their role in cancer.

Biophysical insights into OR2T7: Investigation of a potential prognostic marker for glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive and prevalent form of brain cancer, with an expected survival of 12-15 months following diagnosis. GBM affects the glial cells of the central nervous system, which impairs regular brain function including memory, hearing, and vision. GBM has virtually no long-term survival even with treatment, requiring novel strategies to understand disease progression. Here, we identified a somatic mutation in OR2T7, a G-protein-coupled receptor (GPCR), that correlates with reduced progression-free survival for glioblastoma (log rank p-value = 0.05), suggesting a possible role in tumor progression. The mutation, D125V, occurred in 10% of 396 glioblastoma samples in The Cancer Genome Atlas, but not in any of the 2504 DNA sequences in the 1000 Genomes Project, suggesting that the mutation may have a deleterious functional effect. In addition, transcriptome analysis showed that the p38α mitogen-activated protein kinase (MAPK), c-Fos, c-Jun, and JunB proto-oncogenes, and putative tumor suppressors RhoB and caspase-14 were underexpressed in glioblastoma samples with the D125V mutation (false discovery rate < 0.05). Molecular modeling and molecular dynamics simulations have provided preliminary structural insight and indicate a dynamic helical movement network that is influenced by the membrane-embedded, cytofacial-facing residue 125, demonstrating a possible obstruction of G-protein binding on the cytofacial exposed region. We show that the mutation impacts the "open" GPCR conformation, potentially affecting Gα-subunit binding and associated downstream activity. Overall, our findings suggest that the Val125 mutation in OR2T7 could affect glioblastoma progression by downregulating GPCR-p38 MAPK tumor-suppression pathways and impacting the biophysical characteristics of the structure that facilitates Gα-subunit binding. This study provides the theoretical basis for further experimental investigation required to confirm that the D125V mutation in OR2T7 is not a passenger mutation. With validation, the aforementioned mutation could represent an important prognostic marker and a potential therapeutic target for glioblastoma.

PRECOGx: exploring GPCR signaling mechanisms with deep protein representations

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.