G protein‐coupled receptors (GPCRs) are one of the major drug targets. In recent years, computational drug design for GPCRs has mainly focused on static structures obtained through X‐ray crystallography, cryogenic electron microscopy (cryo‐EM) or in silico modelling as a starting point for virtual screening campaigns. However, GPCRs are highly flexible entities with the ability to adopt different conformational states that elicit different physiological responses. Including this knowledge in the drug discovery pipeline can help to tailor novel conformation‐specific drugs with an improved therapeutic profile. In this review, we outline our current knowledge about GPCR dynamics that is relevant for receptor activation, signalling bias and allosteric modulation. Ultimately, we highlight new technological implementations such as time‐resolved X‐ray crystallography and cryo‐EM as well as computational algorithms that can contribute to a more comprehensive understanding of receptor dynamics and its relevance for GPCR functionality.LINKED ARTICLES: This article is part of a themed issue Complexity of GPCR Modulation and Signaling (ERNST). To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.14/issuetoc

Cannabinoid receptor (CB1), a G protein coupled receptor (GPCR), is a known pharmacological target in several diseases and modulates key physiological processes through Gi protein-mediated signaling. However, recent evidence suggests that CB1 can also activate other G proteins, including the stimulatory Gs protein, a phenomenon with unclear structural determinants. Here, we use a computational approach to elucidate the structural basis of the CB1-Gs interaction. Protein–protein docking and extensive molecular dynamics simulations yield a model for the CB1-Gs complex that agrees well with both existing experimental data and available GPCR-Gs structures, supporting its validity. This work provides new insights into the structural basis of CB1’s ability to couple with different G-proteins. The model provides a basis for future studies dissecting the functional consequences of CB1-Gs signaling and the development of improved therapeutics targeting the CB1 receptor and the wider endocannabinoid system.

Aims/hypothesis: The glucagon-like peptide-1 receptor (GLP1R) is a key regulator of glucose homeostasis and body weight, and a major therapeutic target for type 2 diabetes and obesity. Individual disease risk and response to treatments may vary widely depending on the presence of missense variants (MVs) in the GLP1R gene, but accurate prediction of the impact of MVs in the G protein-coupled receptor (GPCR) remains challenging. Here we aimed to generate a reliable and GPCR-tailored predictor of the impact of MVs that is applicable for precision medicine. Methods: We developed a GPCR variant impact predictor (GPCR ^VP score) by combining molecular dynamics (MD) simulations with available machine-learning/AI-based models (AlphaMissense and REVEL) to substantially improve the predictive performance of existing models. MD simulations of wild-type and mutant GLP1R complexes were used to extract GPCR-specific structural and dynamic features that capture membrane context, allosteric communication, and mutation-induced contact rearrangements. These descriptors were integrated with existing variant-effect predictors (AlphaMissense and REVEL) and trained on 58 experimentally characterised GLP1R variants to develop GPCR ^VP , a GPCR-specific variant predictor. Results: The GPCR ^VP predictor showed strong agreement with experimental data and robust performance on independent validation datasets. Notably, it demonstrated a substantial reduction in false-positive predictions (approximately 78%) in the validation sets compared with AlphaMissense, indicating improved specificity and reliability. Generation of structural models for all known human GLP1R MVs (174 outside the training and validation sets) produced a comprehensive resource showing the predictive impact of all GLP1R MVs. Conclusions/interpretation: These findings establish the GPCR ^VP score as a GPCR-specific and reliable predictor for GLP1R MVs that is likely to be of great relevance for clinical decision-making without the time- and cost-consuming generation of experimental data.