Summary: Computer simulations are giving way to more complex and accurate studies of biological membranes by molecular dynamics (MD) simulations. The analysis of MD trajectories comprises the biophysical characterization of membrane properties or the study of protein–lipid interactions and dynamics. However, there is a lack of automated tools to analyse MD simulations of complex membrane or membrane-protein systems. Here we present MEMBPLUGIN, a plugin for the Visual Molecular Dynamics package that provides algorithms to measure a host of essential biophysical properties in simulated membranes. MEMBPLUGIN features are accessible both through a user-friendly graphical interface and as command-line procedures to be invoked in analysis scripts. Availability and implementation: MEMBPLUGIN is a VMD extension written in Tcl. Multi-platform source code, documentation and tutorials are freely available at http://membplugin.sourceforge.net. Contact:toni.giorgino@isib.cnr.it or jana.selent@upf.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Cholesterol is a key component of cell membranes with a proven modulatory role on the function and ligand-binding properties of G-protein-coupled receptors (GPCRs). Crystal structures of prototypical GPCRs such as the adenosine A_2A receptor (A_2AR) have confirmed that cholesterol finds stable binding sites at the receptor surface suggesting an allosteric role of this lipid. Here we combine experimental and computational approaches to show that cholesterol can spontaneously enter the A_2AR-binding pocket from the membrane milieu using the same portal gate previously suggested for opsin ligands. We confirm the presence of cholesterol inside the receptor by chemical modification of the A_2AR interior in a biotinylation assay. Overall, we show that cholesterol’s impact on A_2AR-binding affinity goes beyond pure allosteric modulation and unveils a new interaction mode between cholesterol and the A_2AR that could potentially apply to other GPCRs.

G protein-coupled receptors (GPCRs) are membrane-spanning transducers mediating the actions of numerous physiological ligands and drugs. The GPCR database GPCRdb supports a large global research community with reference data, analysis, visualization, experiment design and dissemination. Here, we describe our sixth major GPCRdb release starting with an overview of all resources for receptors and ligands. As a major addition, all ∼400 human odorant receptors and their orthologs in major model organisms can now be studied across the various data and tool resources. For the first time, a Data mapper page enables users to map their own data onto receptors visualized as a GPCRome wheel, tree, clusters, list or heatmap. The structure model data have been expanded with models of physiological ligand complexes and updated with new state-specific structure models of all human GPCRs (built using AlphaFold, RoseTTAFold and AlphaFold-Multistate). Furthermore, a structure or model (pdb file) can now be queried against GPCRdb’s entire structure/model collection through a Structuresimilarity search page implementing FoldSeek. Finally, for ligands, new search tools can query names, database identifiers, similarities or substructures against integrated entries from the ChEMBL, Guide to Pharmacology, PDSP K_i, PubChem, DrugCentral and DrugBank databases. GPCRdb is available at https://gpcrdb.org.

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D₂ dopamine (D₂R) and serotonin 5-HT_2A receptors (5-HT_2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D₂R and 5-HT_2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs displayed ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even displayed ligand enrichment better than that of the D₂R and 5-HT_2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models. Three-dimensional structures of proteins combined with computational methods have become widely used to identify starting-points for drug discovery. However, this powerful approach is limited by the lack of atomic resolution structures for many drug targets. G protein-coupled receptors (GPCRs) belong to the largest family of cell surface receptors and play roles in numerous physiological processes. As GPCRs are important therapeutic targets, there is significant interest in applying structure-based in silico screening to accelerate the drug discovery process. However, GPCRs have been notoriously difficult to crystallize and structures are lacking for >80% of the family. We assessed prediction of GPCR structure based on previously determined crystal structures as templates by using the homology modeling method. We explored strategies to identify models suitable for virtual screening with the molecular docking method and to further refine structures using molecular dynamics simulations. Our calculations revealed that the closest homologue of a target is not necessarily the best template and demonstrated how accurate binding site models with excellent ability to identify ligands can be obtained. The results highlight strengths and weaknesses of structure prediction methods and provide guidelines for successful application of virtual screening to proteins of unknown structure.

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy ofin silicomodels of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D₃dopamine receptor (D₃R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D₃R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.