Dataset Information

Computational Simulation of the Activation Cycle of G? Subunit in the G Protein Cycle Using an Elastic Network Model.

ABSTRACT: Agonist-activated G protein-coupled receptors (GPCRs) interact with GDP-bound G protein heterotrimers (G???) promoting GDP/GTP exchange, which results in dissociation of G? from the receptor and G??. The GTPase activity of G? hydrolyzes GTP to GDP, and the GDP-bound G? interacts with G??, forming a GDP-bound G protein heterotrimer. The G protein cycle is allosterically modulated by conformational changes of the G? subunit. Although biochemical and biophysical methods have elucidated the structure and dynamics of G?, the precise conformational mechanisms underlying the G protein cycle are not fully understood yet. Simulation methods could help to provide additional details to gain further insight into G protein signal transduction mechanisms. In this study, using the available X-ray crystal structures of G?, we simulated the entire G protein cycle and described not only the steric features of the G? structure, but also conformational changes at each step. Each reference structure in the G protein cycle was modeled as an elastic network model and subjected to normal mode analysis. Our simulation data suggests that activated receptors trigger conformational changes of the G? subunit that are thermodynamically favorable for opening of the nucleotide-binding pocket and GDP release. Furthermore, the effects of GTP binding and hydrolysis on mobility changes of the C and N termini and switch regions are elucidated. In summary, our simulation results enabled us to provide detailed descriptions of the structural and dynamic features of the G protein cycle.

SUBMITTER: Kim MH

PROVIDER: S-EPMC4970668 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Computational Simulation of the Activation Cycle of Gα Subunit in the G Protein Cycle Using an Elastic Network Model.

Kim Min Hyeok MH Kim Young Jin YJ Kim Hee Ryung HR Jeon Tae-Joon TJ Choi Jae Boong JB Chung Ka Young KY Kim Moon Ki MK

PloS one 20160802 8

Agonist-activated G protein-coupled receptors (GPCRs) interact with GDP-bound G protein heterotrimers (Gαβγ) promoting GDP/GTP exchange, which results in dissociation of Gα from the receptor and Gβγ. The GTPase activity of Gα hydrolyzes GTP to GDP, and the GDP-bound Gα interacts with Gβγ, forming a GDP-bound G protein heterotrimer. The G protein cycle is allosterically modulated by conformational changes of the Gα subunit. Although biochemical and biophysical methods have elucidated the structur ...[more]

PMID: 27483005

Similar Datasets

Project description:The empirical harmonic potential function of elastic network models (ENMs) is augmented by three- and four-body interactions as well as by a parameter-free connection rule. In the new bend-twist-stretch (BTS) model the complexity of the parametrization is shifted from the spatial level of detail to the potential function, enabling an arbitrary coarse graining of the network. Compared to distance cutoff-based Hookean springs, the approach yields a more stable parametrization of coarse-grained ENMs for biomolecular dynamics. Traditional ENMs give rise to unbounded zero-frequency vibrations when (pseudo)atoms are connected to fewer than three neighbors. A large cutoff is therefore chosen in an ENM (about twice the average nearest-neighbor distance), resulting in many false-positive connections that reduce the spatial detail that can be resolved. More importantly, the required three-neighbor connectedness also limits the coarse graining, i.e., the network must be dense, even in the case of low-resolution structures that exhibit few spatial features. The new BTS model achieves such coarse graining by extending the ENM potential to include three-and four-atom interactions (bending and twisting, respectively) in addition to the traditional two-atom stretching. Thus, the BTS model enables reliable modeling of any three-dimensional graph irrespective of the atom connectedness. The additional potential terms were parametrized using continuum elastic theory of elastic rods, and the distance cutoff was replaced by a competitive Hebb connection rule, setting all free parameters in the model. We validate the approach on a carbon-alpha representation of adenylate kinase and illustrate its use with electron microscopy maps of E. coli RNA polymerase, E. coli ribosome, and eukaryotic chaperonin containing T-complex polypeptide 1, which were difficult to model with traditional ENMs. For adenylate kinase, we find excellent reproduction (>90% overlap) of the ENM modes and B factors when BTS is applied to the carbon-alpha representation as well as to coarser descriptions. For the volumetric maps, coarse BTS yields similar motions (70%-90% overlap) to those obtained from significantly denser representations with ENM. Our Python-based algorithms of ENM and BTS implementations are freely available.

Project description:In mammals, vasopressin (AVP) is released from magnocellular neurons of the hypothalamus when osmotic pressure exceeds a fixed set-point. AVP participates to the hydromineral homeostasis (HH) by controlling water excretion at the level of the kidneys. Our current understanding of the HH and AVP secretion is the result of a vast amount of data collected over the five past decades. This experimental data was collected using a number of systems under different conditions, giving a fragmented view of the components involved in HH.Here, we present a high-level model of the rat HH based on selected published results to predict short-term (hours) to long-term (days) variation of six major homeostatic parameters: (1) the extracellular sodium concentration, (2) the AVP concentration, (3) the intracellular volume, (4) the extracellular volume, (5) the urine volume and (6) the water intake. The simulation generates quantitative predictions like the daily mean of the extracellular sodium concentration (142.2 mmol/L), the AVP concentration, (1.7 pg/ml), the intracellular volume (45.3 ml/100 g body weight--bw), the extracellular volume (22.6 ml/100 g bw), the urine volume (11.8 ml/100 g bw) and the cumulative water intake (18 ml/100 g bw). The simulation also computes the dynamics of all these parameters with a high temporal resolution of one minute. This high resolution predicts the circadian fluctuation of the AVP secretion (5 ± 2 pg/ml) and defines the limits of a restoration and a maintenance phase in the HH (2.1 pg/ml). Moreover, the simulation can predict the action of pharmacological compounds that disrupt the HH. As an example, we tested the action of a diuretic (furosemide) combined with a sodium deficient diet to generate quantitative prediction on the extracellular sodium concentration (134 mmol/L) and the need-induced water intake (20.3 ml/100 g bw). These simulated data are compatible with experimental data (136 ± 3 mmol/L and 17.5 ± 3.5 ml/100 g bw, respectively).The quantitative agreement of the predictions with published experimental data indicates that our simplified model of the HH integrates most of the essential systems to predict realistic physiological values and dynamics under a set of normal and perturbed hydromineral conditions.

Project description:The cellular protein-protein interaction network that governs cellular proliferation (cell cycle) is highly complex. Here, we have developed a novel computational model of human mitotic cell cycle, integrating diverse cellular mechanisms, for the purpose of generating new hypotheses and predicting new experiments designed to help understand complex diseases. The pathogenic state investigated is infection by a human herpesvirus. The model starts at mitotic entry initiated by the activities of Cyclin-dependent kinase 1 (CDK1) and Polo-like kinase 1 (PLK1), transitions through Anaphase-promoting complex (APC/C) bound to Cell division cycle protein 20 (CDC20), and ends upon mitotic exit mediated by APC/C bound to CDC20 homolog 1 (CDH1). It includes syntheses and multiple mechanisms of degradations of the mitotic proteins. Prior to this work, no such comprehensive model of the human mitotic cell cycle existed. The new model is based on a hybrid framework combining Michaelis-Menten and mass action kinetics for the mitotic interacting reactions. It simulates temporal changes in 12 different mitotic proteins and associated protein complexes in multiple states using 15 interacting reactions and 26 ordinary differential equations. We have defined model parameter values using both quantitative and qualitative data and using parameter values from relevant published models, and we have tested the model to reproduce the cardinal features of human mitosis determined experimentally by numerous laboratories. Like cancer, viruses create dysfunction to support infection. By simulating infection of the human herpesvirus, cytomegalovirus, we hypothesize that virus-mediated disruption of APC/C is necessary to establish a unique mitotic collapse with sustained CDK1 activity, consistent with known mechanisms of virus egress. With the rapid discovery of cellular protein-protein interaction networks and regulatory mechanisms, we anticipate that this model will be highly valuable in helping us to understand the network dynamics and identify potential points of therapeutic interventions.

Dataset Information

Computational Simulation of the Activation Cycle of G? Subunit in the G Protein Cycle Using an Elastic Network Model.

Publications

Computational Simulation of the Activation Cycle of Gα Subunit in the G Protein Cycle Using an Elastic Network Model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets