scholarly journals Compound Ranking Based on Fuzzy Three-Dimensional Similarity Improves the Performance of Docking into Homology Models of G-Protein-Coupled Receptors

ACS Omega ◽  
2017 ◽  
Vol 2 (6) ◽  
pp. 2583-2592 ◽  
Author(s):  
Andrew Anighoro ◽  
Jürgen Bajorath
Keyword(s):  
G Protein ◽  
Three Dimensional ◽  
G Protein Coupled Receptors ◽  
G Protein Coupled ◽  
Homology Models

Modulation of proteostasis and protein trafficking: a therapeutic avenue for misfolded G protein-coupled receptors causing disease in humans

2019 ◽  
Vol 3 (1) ◽  
pp. 39-52
Author(s):  
Alfredo Ulloa-Aguirre ◽  
Jo Ann Janovick
Keyword(s):  
G Protein ◽  
Protein Trafficking ◽  
Protein Misfolding ◽  
Hypogonadotropic Hypogonadism ◽  
Three Dimensional ◽  
G Protein Coupled Receptors ◽  
Dimensional Structure ◽  
Stable Conformation ◽  
Mutant Proteins ◽  
G Protein Coupled

Abstract Proteostasis refers to the process whereby the cell maintains in equilibrium the protein content of different compartments. This system consists of a highly interconnected network intended to efficiently regulate the synthesis, folding, trafficking, and degradation of newly synthesized proteins. Molecular chaperones are key players of the proteostasis network. These proteins assist in the assembly and folding processes of newly synthesized proteins in a concerted manner to achieve a three-dimensional structure compatible with export from the endoplasmic reticulum to other cell compartments. Pharmacologic interventions intended to modulate the proteostasis network and tackle the devastating effects of conformational diseases caused by protein misfolding are under development. These include small molecules called pharmacoperones, which are highly specific toward the target protein serving as a molecular framework to cause misfolded mutant proteins to fold and adopt a stable conformation suitable for passing the scrutiny of the quality control system and reach its correct location within the cell. Here, we review the main components of the proteostasis network and how pharmacoperones may be employed to correct misfolding of two G protein-coupled receptors, the vasopressin 2 receptor and the gonadotropin-releasing hormone receptor, whose mutations lead to X-linked nephrogenic diabetes insipidus and congenital hypogonadotropic hypogonadism in humans respectively.

scholarly journals Advances in Determination of a High-Resolution Three-Dimensional Structure of Rhodopsin, a Model of G-Protein-Coupled Receptors (GPCRs)†,‡

Biochemistry ◽  
2001 ◽  
Vol 40 (26) ◽  
pp. 7761-7772 ◽  
Author(s):  
David C. Teller ◽  
Tetsuji Okada ◽  
Craig A. Behnke ◽  
Krzysztof Palczewski ◽  
Ronald E. Stenkamp
Keyword(s):  
High Resolution ◽  
G Protein ◽  
Three Dimensional ◽  
G Protein Coupled Receptors ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
G Protein Coupled

Demystifying the three dimensional structure of G protein-coupled receptors (GPCRs) with the aid of molecular modeling

2003 ◽  
pp. 2949 ◽  
Author(s):  
Stefano Moro ◽  
Francesca Deflorian ◽  
Giampiero Spalluto ◽  
Giorgia Pastorin ◽  
Barbara Cacciari ◽  
...  
Keyword(s):  
Molecular Modeling ◽  
G Protein ◽  
Three Dimensional ◽  
G Protein Coupled Receptors ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
G Protein Coupled

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.

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