scholarly journals Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Sangmin Seo ◽  
Jonghwan Choi ◽  
Soon Kil Ahn ◽  
Kil Won Kim ◽  
Jaekwang Kim ◽  
...  
Keyword(s):  
Ligand Binding ◽  
3D Structure ◽  
Area Under The Curve ◽  
G Protein Coupled Receptors ◽  
Machine Learning Algorithms ◽  
Amino Acid Motif ◽  
Average Area ◽  
Novel Method ◽  
Cycle Structures ◽  
G Protein Coupled

We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good ligand-receptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported by several studies.

Identification Methods of G Protein-Coupled Receptors

2011 ◽  
Vol 2 (4) ◽  
pp. 35-52
Author(s):  
Meriem Zekri ◽  
Karima Alem ◽  
Labiba Souici-Meslati
Keyword(s):  
Immune Responses ◽  
G Protein ◽  
Pharmaceutical Research ◽  
G Protein Coupled Receptors ◽  
Machine Learning Algorithms ◽  
Signaling Networks ◽  
Identification Methods ◽  
Physiological Processes ◽  
Cell Signaling Networks ◽  
G Protein Coupled

The G protein-coupled receptors (GPCRs) include one of the largest and most important families of multifunctional proteins known to molecular biology. They play a key role in cell signaling networks that regulate many physiological processes, such as vision, smell, taste, neurotransmission, secretion, immune responses, metabolism, and cell growth. These proteins are thus very important for understanding human physiology and they are involved in several diseases. Therefore, many efforts in pharmaceutical research are to understand their structures and functions, which is not an easy task, because although thousands GPCR sequences are known, many of them remain orphans. To remedy this, many methods have been developed using methods such as statistics, machine learning algorithms, and bio-inspired approaches. In this article, the authors review the approaches used to develop algorithms for classification GPCRs by trying to highlight the strengths and weaknesses of these different approaches and providing a comparison of their performances.

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

scholarly journals A Novel Method for Analyzing Extremely Biased Agonism at G Protein–Coupled Receptors

2015 ◽  
Vol 87 (5) ◽  
pp. 866-877 ◽  
Author(s):  
Edward L. Stahl ◽  
Lei Zhou ◽  
Frederick J. Ehlert ◽  
Laura M. Bohn
Keyword(s):  
G Protein ◽  
G Protein Coupled Receptors ◽  
Biased Agonism ◽  
Novel 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.

Modeling the 3D Structure of Rhodopsin Using a De Novo Approach to Build G-protein−Coupled Receptors

1999 ◽  
Vol 103 (13) ◽  
pp. 2520-2527 ◽  
Author(s):  
Marta Filizola ◽  
Maria Cartenì-Farina ◽  
Juan J. Perez
Keyword(s):  
G Protein ◽  
De Novo ◽  
3D Structure ◽  
G Protein Coupled Receptors ◽  
G Protein Coupled

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.

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