Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways.
Ontology highlight
ABSTRACT:
SUBMITTER: Kohlhoff KJ
PROVIDER: S-EPMC3923464 | biostudies-literature | 2014 Jan
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:G protein-coupled receptors (GPCRs) are a vital class of proteins that transduce biological signals across the cell membrane. However, their allosteric activation mechanism is not fully understood; crystal structures of active and inactive receptors have been reported, but the functional pathway between these two states remains elusive. Here, we use structure-based (Gō-like) models to simulate activation of two GPCRs, rhodopsin and the β₂ adrenergic receptor (β₂AR). We used data-derived reaction coordinates that capture the activation mechanism for both proteins, showing that activation proceeds through quantitatively different paths in the two systems. Both reaction coordinates are determined from the dominant concerted motions in the simulations so the technique is broadly applicable. There were two surprising results. First, the main structural changes in the simulations were distributed throughout the transmembrane bundle, and not localized to the obvious areas of interest, such as the intracellular portion of Helix 6. Second, the activation (and deactivation) paths were distinctly nonmonotonic, populating states that were not simply interpolations between the inactive and active structures. These transitions also suggest a functional explanation for β₂AR's basal activity: it can proceed through a more broadly defined path during the observed transitions.
| S-EPMC4177312 | biostudies-literature
Project description:Nuclear receptor (NR) ligands occupy a pocket that lies within the core of the NR ligand-binding domain (LBD), and most NR LBDs lack obvious entry/exit routes upon the protein surface. Thus, significant NR conformational rearrangements must accompany ligand binding and release. The precise nature of these processes, however, remains poorly understood. Here, we utilize locally enhanced sampling (LES) molecular dynamics computer simulations to predict molecular motions of x-ray structures of thyroid hormone receptor (TR) LBDs and determine events that permit ligand escape. We find that the natural ligand 3,5,3'-triiodo-L-thyronine (T(3)) dissociates from the TRalpha1 LBD along three competing pathways generated through i), opening of helix (H) 12; ii), separation of H8 and H11 and the Omega-loop between H2 and H3; and iii), opening of H2 and H3, and the intervening beta-strand. Similar pathways are involved in dissociation of T(3) and the TRbeta-selective ligand GC24 from TRbeta; the TR agonist IH5 from the alpha- and beta-TR forms; and Triac from two natural human TRbeta mutants, A317T and A234T, but are detected with different frequencies in simulations performed with the different structures. Path I was previously suggested to represent a major pathway for NR ligand dissociation. We propose here that Paths II and III are also likely ligand escape routes for TRs and other NRs. We also propose that different escape paths are preferred in different situations, implying that it will be possible to design NR ligands that only associate stably with their cognate receptors in specific cellular contexts.
| S-EPMC1366704 | biostudies-literature
Project description:BACKGROUND:Bioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis. MAIN TEXT:We present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others. CONCLUSIONS:Keemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes.
| S-EPMC4906574 | biostudies-literature
Project description:GPCRs regulate all aspects of human physiology, and biophysical studies have deepened our understanding of GPCR conformational regulation by different ligands. Yet there is no experimental evidence for how sidechain dynamics control allosteric transitions between GPCR conformations. To address this deficit, we generated samples of a wild-type GPCR (A2AR) that are deuterated apart from 1H/13C NMR probes at isoleucine δ1 methyl groups, which facilitated 1H/13C methyl TROSY NMR measurements with opposing ligands. Our data indicate that low [Na+] is required to allow large agonist-induced structural changes in A2AR, and that patterns of sidechain dynamics substantially differ between agonist (NECA) and inverse agonist (ZM241385) bound receptors, with the inverse agonist suppressing fast ps-ns timescale motions at the G protein binding site. Our approach to GPCR NMR creates a framework for exploring how different regions of a receptor respond to different ligands or signaling proteins through modulation of fast ps-ns sidechain dynamics.
| S-EPMC5650471 | biostudies-literature
Project description:G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.
| S-EPMC10959162 | biostudies-literature
Project description:We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200??M for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design.
| S-EPMC4302315 | biostudies-literature
Project description:G protein-coupled receptors (GPCRs) are a family of transmembrane proteins that act as major mediators of cellular signaling, and are the primary targets for a large portion of clinical therapeutics. Despite their critical role in biology and medicine, a large number of GPCRs are poorly understood, lacking validated ligands or potent synthetic modulators. Ligand-induced GPCR activation can be measured in cell-based assays to test hypotheses about ligand-receptor interactions or to evaluate efficacy of synthetic agonists or antagonists. However, the techniques necessary to develop and implement a cell-based assay to study a given receptor of interest are not commonplace in all laboratories. This chapter outlines methods to develop a cell-based assay to evaluate agonist-induced activation for a GPCR of interest, which can be useful to evaluate the effectiveness of predicted ligands. Examples of sample preparation protocols and data analysis are provided to help researchers from interdisciplinary fields, especially those in fields with relatively little molecular biology or cell culture experience.
| S-EPMC9000926 | biostudies-literature
Project description:G-protein coupled receptors (GPCRs) play a pivotal role in transmitting signals at the cellular level. Structural insights can be exploited to support GPCR structure-based drug discovery endeavours. Despite advances in GPCR crystallography, active state structures are scarce. Molecular dynamics (MD) simulations have been used to explore the conformational landscape of GPCRs. Efforts have been made to retrieve active state conformations starting from inactive structures, however to date this has not been possible without using an energy bias. Here, we reconstruct the activation pathways of the apo adenosine receptor (A2A), starting from an inactive conformation, by applying adaptive sampling MD combined with a goal-oriented scoring function. The reconstructed pathways reconcile well with experiments and help deepen our understanding of A2A regulatory mechanisms. Exploration of the apo conformational landscape of A2A reveals the existence of ligand-competent states, active intermediates and state-dependent cholesterol hotspots of relevance for drug discovery. To the best of our knowledge this is the first time an activation process has been elucidated for a GPCR starting from an inactive structure only, using a non-biased MD approach, opening avenues for the study of ligand binding to elusive yet pharmacologically relevant GPCR states.
| S-EPMC6775061 | biostudies-literature
Project description:Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.Highlights
| S-EPMC11299028 | biostudies-literature
Project description:Adenosine receptors and ?-adrenoceptors are G-protein-coupled receptors (GPCRs) that activate intracellular G proteins on binding the agonists adenosine or noradrenaline, respectively. GPCRs have similar structures consisting of seven transmembrane helices that contain well-conserved sequence motifs, indicating that they are probably activated by a common mechanism. Recent structures of ?-adrenoceptors highlight residues in transmembrane region 5 that initially bind specifically to agonists rather than to antagonists, indicating that these residues have an important role in agonist-induced activation of receptors. Here we present two crystal structures of the thermostabilized human adenosine A(2A) receptor (A(2A)R-GL31) bound to its endogenous agonist adenosine and the synthetic agonist NECA. The structures represent an intermediate conformation between the inactive and active states, because they share all the features of GPCRs that are thought to be in a fully activated state, except that the cytoplasmic end of transmembrane helix 6 partially occludes the G-protein-binding site. The adenine substituent of the agonists binds in a similar fashion to the chemically related region of the inverse agonist ZM241385 (ref. 8). Both agonists contain a ribose group, not found in ZM241385, which extends deep into the ligand-binding pocket where it makes polar interactions with conserved residues in H7 (Ser?277(7.42) and His?278(7.43); superscripts refer to Ballesteros-Weinstein numbering) and non-polar interactions with residues in H3. In contrast, the inverse agonist ZM241385 does not interact with any of these residues and comparison with the agonist-bound structures indicates that ZM241385 sterically prevents the conformational change in H5 and therefore it acts as an inverse agonist. Comparison of the agonist-bound structures of A(2A)R with the agonist-bound structures of ?-adrenoceptors indicates that the contraction of the ligand-binding pocket caused by the inward motion of helices 3, 5 and 7 may be a common feature in the activation of all GPCRs.
| S-EPMC3146096 | biostudies-literature