scholarly journals Computational Prediction of Alanine Scanning and Ligand Binding Energetics in G-Protein Coupled Receptors

2014 ◽  
Vol 10 (4) ◽  
pp. e1003585 ◽  
Author(s):  
Lars Boukharta ◽  
Hugo Gutiérrez-de-Terán ◽  
Johan Åqvist
Keyword(s):  
Ligand Binding ◽  
G Protein ◽  
Computational Prediction ◽  
G Protein Coupled Receptors ◽  
Alanine Scanning ◽  
Binding Energetics ◽  
G Protein Coupled

Multiparametric Homogeneous Method for Identification of Ligand Binding to G Protein-Coupled Receptors: Receptor–Ligand Binding and β-Arrestin Assay

2013 ◽  
Vol 85 (4) ◽  
pp. 2276-2281 ◽  
Author(s):  
Kari Kopra ◽  
Markus Kainulainen ◽  
Piia Mikkonen ◽  
Anita Rozwandowicz-Jansen ◽  
Pekka Hänninen ◽  
...  
Keyword(s):  
Ligand Binding ◽  
G Protein ◽  
G Protein Coupled Receptors ◽  
Receptor Ligand ◽  
Homogeneous Method ◽  
G Protein Coupled

Family Resemblances? Ligand Binding and Activation of Family A and B G-Protein-Coupled Receptors

2007 ◽  
Vol 35 (4) ◽  
pp. 707-708 ◽  
Author(s):  
D.R. Poyner ◽  
M. Wheatley
Keyword(s):  
Ligand Binding ◽  
G Protein ◽  
Present Article ◽  
G Protein Coupled Receptors ◽  
Society Meeting ◽  
Family Resemblances ◽  
G Protein Coupled ◽  
Compare And Contrast ◽  
Present Meeting

In April 2007, the Biochemical Society held a meeting to compare and contrast ligand binding and activation of Family A and B GPCRs (G-protein-coupled receptors). Being the largest class, Family A GPCRs usually receive the most attention, although a previous Biochemical Society meeting has focused on Family B GPCRs. The aim of the present meeting was to bring researchers of both families together in order to identify commonalities between the two. The present article introduces the proceedings of the meeting, briefly commenting on the focus of each of the following articles.

scholarly journals Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

2019 ◽  
Author(s):  
S. Muk ◽  
S. Ghosh ◽  
S. Achuthan ◽  
X. Chen ◽  
X. Yao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Membrane Proteins ◽  
G Protein ◽  
Three Dimensional ◽  
G Protein Coupled Receptors ◽  
Complement Factor ◽  
Computational Framework ◽  
Alanine Scanning ◽  
Structure And Dynamics ◽  
G Protein Coupled

AbstractAlthough the three-dimensional structures of G-protein-coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence, structure and dynamics based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available dataset. A blind prediction for thermostable mutations of the Complement factor C5a Receptor retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.Statement Of SignifiganceG-protein-coupled receptors (GPCRs), the largest superfamily of membrane proteins play a vital role in cellular physiology and are targets to blockbuster drugs. Hence it is imperative to solve the three dimensional structures of GPCRs in various conformational states with different types of ligands bound. To reduce the experimental burden in identifying thermostable GPCR mutants, we report a computational framework using machine learning algorithms trained on thermostability data for 1231 mutants and features calculated from analysis of GPCR sequences, structure and dynamics to predict thermostable mutations ahead of experiments. This work represents a significant advancement in the development, validation and testing of a computational framework that can be extended to other class A GPCRs and helical membrane proteins.

scholarly journals Locating ligand binding sites in G-protein coupled receptors using combined information from docking and sequence conservation

2018 ◽  
Author(s):  
Ashley R. Vidad ◽  
Stephen Macaspac ◽  
Ho-Leung Ng
Keyword(s):  
Ligand Binding ◽  
G Protein ◽  
Binding Sites ◽  
G Protein Coupled Receptors ◽  
Sequence Conservation ◽  
Surface Geometry ◽  
A2a Adenosine Receptors ◽  
Ligand Binding Sites ◽  
G Protein Coupled ◽  
Homology Models

AbstractG-protein coupled receptors (GPCRs) are the largest protein family of drug targets. Detailed mechanisms of binding are unknown for many important GPCR-ligand pairs due to the difficulties of GPCR recombinant expression, biochemistry, and crystallography. We describe our new method, ConDock, for predicting ligand binding sites in GPCRs using combined information from surface conservation and docking starting from crystal structures or homology models. We demonstrate the effectiveness of ConDock on well-characterized GPCRs such as the β2 adrenergic and A2A adenosine receptors. We also demonstrate that ConDock successfully predicts ligand binding sites from high-quality homology models. Finally, we apply ConDock to predict ligand binding sites on a structurally uncharacterized GPCR, GPER. GPER is the G-protein coupled estrogen receptor, with four known ligands: estradiol, G1, G15, and tamoxifen. ConDock predicts that all four ligands bind to the same location on GPER, centered on L119, H307, and N310; this site is deeper in the receptor cleft than predicted by previous studies. We compare the sites predicted by ConDock and traditional methods that utilize information from surface geometry, surface conservation, and ligand chemical interactions. Incorporating sequence conservation information in ConDock overcomes errors introduced from physics-based scoring functions and homology modeling.

New small molecule fluorescent probes for G protein-coupled receptors: valuable tools for drug discovery

2021 ◽  
Vol 13 (1) ◽  
pp. 63-90
Author(s):  
Joshua W Conner ◽  
Daniel P Poole ◽  
Manuela Jörg ◽  
Nicholas A Veldhuis
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
G Protein ◽  
Small Molecule ◽  
G Protein Coupled Receptors ◽  
Signaling Proteins ◽  
Drug Candidates ◽  
Compound Screening ◽  
G Protein Coupled ◽  
Single Approach

G protein-coupled receptors (GPCRs) are essential signaling proteins and tractable therapeutic targets. To develop new drug candidates, GPCR drug discovery programs require versatile, sensitive pharmacological tools for ligand binding and compound screening. With the availability of new imaging modalities and proximity-based ligand binding technologies, fluorescent ligands offer many advantages and are increasingly being used, yet labeling small molecules remains considerably more challenging relative to peptides. Focusing on recent fluorescent small molecule studies for family A GPCRs, this review addresses some of the key challenges, synthesis approaches and structure–activity relationship considerations, and discusses advantages of using high-resolution GPCR structures to inform conjugation strategies. While no single approach guarantees successful labeling without loss of affinity or selectivity, the choice of fluorophore, linker type and site of attachment have proved to be critical factors that can significantly affect their utility in drug discovery programs, and as discussed, can sometimes lead to very unexpected results.

scholarly journals A Microdomain Formed by the Extracellular Ends of the Transmembrane Domains Promotes Activation of the G Protein-Coupled α-Factor Receptor

2004 ◽  
Vol 24 (5) ◽  
pp. 2041-2051 ◽  
Author(s):  
Jennifer C. Lin ◽  
Ken Duell ◽  
James B. Konopka
Keyword(s):  
Ligand Binding ◽  
G Protein ◽  
Transmembrane Domain ◽  
Receptor Activation ◽  
G Protein Coupled Receptors ◽  
Transmembrane Domains ◽  
Consecutive Series ◽  
Subsequent Step ◽  
G Protein Coupled ◽  
Specific Reagent

ABSTRACT The α-factor receptor (Ste2p) that promotes mating in Saccharomyces cerevisiae is similar to other G protein-coupled receptors (GPCRs) in that it contains seven transmembrane domains. Previous studies suggested that the extracellular ends of the transmembrane domains are important for Ste2p function, so a systematic scanning mutagenesis was carried out in which 46 residues near the ends of transmembrane domains 1, 2, 3, 4, and 7 were replaced with cysteine. These mutants complement mutations constructed previously near the ends of transmembrane domains 5 and 6 to analyze all the extracellular ends. Eight new mutants created in this study were partially defective in signaling (V45C, N46C, T50C, A52C, L102C, N105C, L277C, and A281C). Treatment with 2-([biotinoyl] amino) ethyl methanethiosulfonate, a thiol-specific reagent that reacts with accessible cysteine residues but not membrane-embedded cysteines, identified a drop in the level of reactivity over a consecutive series of residues that was inferred to be the membrane boundary. An unusual prolonged zone of intermediate reactivity near the extracellular end of transmembrane domain 2 suggests that this region may adopt a special structure. Interestingly, residues implicated in ligand binding were mainly accessible, whereas residues involved in the subsequent step of promoting receptor activation were mainly inaccessible. These results define a receptor microdomain that provides an important framework for interpreting the mechanisms by which functionally important residues contribute to ligand binding and activation of Ste2p and other GPCRs.

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
Sign in / Sign up

Export Citation Format

Share Document