scholarly journals Free energy calculations of the functional selectivity of 5-HT2B G protein-coupled receptor

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243313
Author(s):  
Brandon L. Peters ◽  
Jinxia Deng ◽  
Andrew L. Ferguson
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
G Protein ◽  
Intracellular Signaling ◽  
Serotonin Receptor ◽  
Energy Landscape ◽  
Free Energy Calculations ◽  
Free Energy Landscape ◽  
Functional Selectivity ◽  
G Protein Coupled

G Protein-Coupled Receptors (GPCRs) mediate intracellular signaling in response to extracellular ligand binding and are the target of one-third of approved drugs. Ligand binding modulates the GPCR molecular free energy landscape by preferentially stabilizing active or inactive conformations that dictate intracellular protein recruitment and downstream signaling. We perform enhanced sampling molecular dynamics simulations to recover the free energy surfaces of a thermostable mutant of the GPCR serotonin receptor 5-HT2B in the unliganded form and bound to a lysergic acid diethylamide (LSD) agonist and lisuride antagonist. LSD binding imparts a ∼110 kJ/mol driving force for conformational rearrangement into an active state. The lisuride-bound form is structurally similar to the apo form and only ∼24 kJ/mol more stable. This work quantifies ligand-induced conformational specificity and functional selectivity of 5-HT2B and presents a platform for high-throughput virtual screening of ligands and rational engineering of the ligand-bound molecular free energy landscape.

scholarly journals Imaging G protein–coupled receptors while quantifying their ligand-binding free-energy landscape

2015 ◽  
Vol 12 (9) ◽  
pp. 845-851 ◽  
Author(s):  
David Alsteens ◽  
Moritz Pfreundschuh ◽  
Cheng Zhang ◽  
Patrizia M Spoerri ◽  
Shaun R Coughlin ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
G Protein ◽  
Binding Free Energy ◽  
Energy Landscape ◽  
G Protein Coupled Receptors ◽  
Free Energy Landscape ◽  
G Protein Coupled

scholarly journals Ligand-Induced Modulation of the Free-Energy Landscape of G Protein-Coupled Receptors Explored by Adaptive Biasing Techniques

2011 ◽  
Vol 7 (10) ◽  
pp. e1002193 ◽  
Author(s):  
Davide Provasi ◽  
Marta Camacho Artacho ◽  
Ana Negri ◽  
Juan Carlos Mobarec ◽  
Marta Filizola
Keyword(s):  
Free Energy ◽  
G Protein ◽  
Energy Landscape ◽  
G Protein Coupled Receptors ◽  
Free Energy Landscape ◽  
Adaptive Biasing ◽  
G Protein Coupled

The Perturbed Free Energy Landscape: Linking Ligand Binding to Biomolecular Folding

ChemBioChem ◽  
2020 ◽  
Author(s):  
fareed aboul-ela ◽  
Abdallah S Abdelsatter ◽  
Youssef Mansour
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Energy Landscape ◽  
Free Energy Landscape

3-Iodothyronamine Induces Diverse Signaling Effects at Different Aminergic and Non-Aminergic G-Protein Coupled Receptors

2019 ◽  
Vol 128 (06/07) ◽  
pp. 395-400 ◽  
Author(s):  
Heike Biebermann ◽  
Gunnar Kleinau
Keyword(s):  
Body Temperature ◽  
G Protein ◽  
Intracellular Signaling ◽  
Serotonin Receptor ◽  
Fine Tuning ◽  
Specific Cell ◽  
Knock Out ◽  
Alpha2 Adrenoceptor ◽  
Knock Out Mice ◽  
G Protein Coupled

AbstractThe thyroid hormone metabolite 3-iodothyronamine (3-T1AM) exerts diverse physiological reactions such as a decrease of body temperature, and negative inotropic and chronotropic effects. This observed pleomorphic effect in physiology can be barely explained by interaction with only one target protein such as the trace-amine receptor 1 (TAAR1), a class A G-protein coupled receptor (GPCR). Moreover, Taar1 knock-out mice still react to 3-T1AM through physiological responses with a rapid decrease in body temperature. These facts propelled our group and others to search for further targets for this molecule.The group of TAARs evolved early in evolution and, according to sequence similarities, they are closely related to adrenoceptors and other aminergic receptors. Therefore, several of these receptors were characterized by their potential to interplay with 3-T1AM. Indeed, 3-T1AM acts as a positive allosteric modulator on the beta2-adrenoceptor (ADRB2) and as a biased agonist on the serotonin receptor 1B (5HT1b) and the alpha2-adrenoceptor (ADRA2A). In addition, 3-T1AM was reported to be a weak antagonist at a non-aminergic muscarinic receptor (M3).These findings impressively reflect that such trace amines can unselectively and simultaneously function at different receptors expressed by one cell or at different tissues. In conclusion, the role of 3-T1AM is hypothesized to concert the fine-tuning of specific cell reactions by the accentuation of certain pathways dependent on distinct receptors. 3-T1AM acts as a regulator of signals by blocking, modulating, or inducing simultaneously distinct intracellular signaling cascades via different GPCRs.

scholarly journals Free-energy landscape of molecular interactions between endothelin 1 and human endothelin type B receptor: fly-casting mechanism

2019 ◽  
Vol 32 (7) ◽  
pp. 297-308 ◽  
Author(s):  
Junichi Higo ◽  
Kota Kasahara ◽  
Mitsuhito Wada ◽  
Bhaskar Dasgupta ◽  
Narutoshi Kamiya ◽  
...  
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Binding Pocket ◽  
Free Energy Landscape ◽  
Type B ◽  
Endothelin 1 ◽  
Reaction Coordinates ◽  
Explicit Solvent ◽  
Virtual System ◽  
G Protein Coupled

Abstract The free-energy landscape of interaction between a medium-sized peptide, endothelin 1 (ET1), and its receptor, human endothelin type B receptor (hETB), was computed using multidimensional virtual-system coupled molecular dynamics, which controls the system’s motions by introducing multiple reaction coordinates. The hETB embedded in lipid bilayer was immersed in explicit solvent. All molecules were expressed as all-atom models. The resultant free-energy landscape had five ranges with decreasing ET1–hETB distance: completely dissociative, outside-gate, gate, binding pocket, and genuine-bound ranges. In the completely dissociative range, no ET1–hETB interaction appeared. In the outside-gate range, an ET1–hETB attractive interaction was the fly-casting mechanism. In the gate range, the ET1 orientational variety decreased rapidly. In the binding pocket range, ET1 was in a narrow pathway with a steep free-energy slope. In the genuine-bound range, ET1 was in a stable free-energy basin. A G-protein-coupled receptor (GPCR) might capture its ligand from a distant place.

scholarly journals The Science Behind G Protein-Coupled Receptors (GPCRs) and Their Accurate Visual Representation in Scientific Research

2017 ◽  
Vol 41 (1) ◽  
Author(s):  
Amy Sojka ◽  
Kevin Brennan ◽  
Evelyn Maizels ◽  
Christine Young
Keyword(s):  
Ligand Binding ◽  
G Protein ◽  
Conformational Changes ◽  
Intracellular Signaling ◽  
Three Dimensional ◽  
G Protein Coupled Receptors ◽  
Membrane Topology ◽  
Extracellular Domains ◽  
Gpcr Structure ◽  
G Protein Coupled

G Protein-Coupled Receptors (GPCRs) are transmembrane (TM) proteins that span the cell membrane seven times, and contain intracellular and extracellular domains, comprised of connecting loops, as well as terminal extension sequences. GPCRs bind ligands within their transmembrane and/or extracellular domains. Ligand binding elicits conformational changes that initiate downstream intracellular signaling events through arrestins and G proteins. GPCRs play central roles in many physiological processes, from sensory to neurological, cardiovascular, endocrine, and reproductive functions. This paper strives to provide an entry point to current GPCR science, and to identify visual approaches to communicate select aspects of GPCR structure and function with clarity and accuracy. The overall GPCR structure, primary sequence and the implications of sequence for membrane topology, ligand binding and helical rearrangements accompanying activation are considered and discussed in the context of visualization strategies, including two-dimensional topological diagrams, three-dimensional representations, and common errors that arise from these representation.

Detecting Ligand-Binding Events and Free Energy Landscape while Imaging Membrane Receptors at Subnanometer Resolution

Nano Letters ◽  
2017 ◽  
Vol 17 (5) ◽  
pp. 3261-3269 ◽  
Author(s):  
Moritz Pfreundschuh ◽  
Daniel Harder ◽  
Zöhre Ucurum ◽  
Dimitrios Fotiadis ◽  
Daniel J. Müller
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Energy Landscape ◽  
Membrane Receptors ◽  
Free Energy Landscape

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

Determination of Base Flipping Free Energy Landscapes from Nonequilibrium Stratification

2019 ◽  
Author(s):  
Xiaohui Wang ◽  
Zhaoxi Sun
Keyword(s):  
Free Energy ◽  
Energy Landscape ◽  
Sampling Technique ◽  
Umbrella Sampling ◽  
Energy Simulation ◽  
Nonlinear Dependence ◽  
Free Energy Landscape ◽  
Convergence Criterion ◽  
Base Flipping ◽  
Convergence Behavior

<p>Correct calculation of the variation of free energy upon base flipping is crucial in understanding the dynamics of DNA systems. The free energy landscape along the flipping pathway gives the thermodynamic stability and the flexibility of base-paired states. Although numerous free energy simulations are performed in the base flipping cases, no theoretically rigorous nonequilibrium techniques are devised and employed to investigate the thermodynamics of base flipping. In the current work, we report a general nonequilibrium stratification scheme for efficient calculation of the free energy landscape of base flipping in DNA duplex. We carefully monitor the convergence behavior of the equilibrium sampling based free energy simulation and the nonequilibrium stratification and determine the empirical length of time blocks required for converged sampling. Comparison between the performances of equilibrium umbrella sampling and nonequilibrium stratification is given. The results show that nonequilibrium free energy simulation is able to give similar accuracy and efficiency compared with the equilibrium enhanced sampling technique in the base flipping cases. We further test a convergence criterion we previously proposed and it comes out that the convergence behavior determined by this criterion agrees with those given by the time-invariant behavior of PMF and the nonlinear dependence of standard deviation on the sample size. </p>

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