Characterizing clinically relevant natural variants of GPCRs using computational approaches

Sengupta, Durba; Sonar, Krushna; Joshi, M. P.

doi:10.1016/bs.mcb.2017.07.013

Cited by 7 publications

(2 citation statements)

References 66 publications

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“…Such functional differences can be caused by alterations in dynamic processes of ligand binding pathways, ligand binding interactions, constitutive receptor activity, or recognition of intracellular effector proteins (e.g., G protein binding). Hence, MD simulations are a promising approach to elucidate the molecular mechanisms that explain functional differences between wild type and variant GPCRs, providing genotypic-phenotypic correlations [ 100 , 101 , 102 , 103 ]. MD simulations were used, for example, to determine the molecular basis of the effect of a commonly found variant: the Arg16Gly variant of the β 2 AR.…”

Section: Complementing Static Datamentioning

confidence: 99%

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

Torrens‐Fontanals

Stępniewski

Aranda-García

et al. 2020

IJMS

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G protein-coupled receptors (GPCRs) are implicated in nearly every physiological process in the human body and therefore represent an important drug targeting class. Advances in X-ray crystallography and cryo-electron microscopy (cryo-EM) have provided multiple static structures of GPCRs in complex with various signaling partners. However, GPCR functionality is largely determined by their flexibility and ability to transition between distinct structural conformations. Due to this dynamic nature, a static snapshot does not fully explain the complexity of GPCR signal transduction. Molecular dynamics (MD) simulations offer the opportunity to simulate the structural motions of biological processes at atomic resolution. Thus, this technique can incorporate the missing information on protein flexibility into experimentally solved structures. Here, we review the contribution of MD simulations to complement static structural data and to improve our understanding of GPCR physiology and pharmacology, as well as the challenges that still need to be overcome to reach the full potential of this technique.

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Section: Complementing Static Datamentioning

confidence: 99%

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

Torrens‐Fontanals

Stępniewski

Aranda-García

et al. 2020

IJMS

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“…Structure-based virtual screening for GPCR ligands has become more feasible in recent years due to increased structure quality and availability [32]. Since computational capabilities are rapidly advancing as well, docking studies are now often accompanied by molecular dynamics simulations, which allow more detailed studies of the GPCR-recognition process but remains infeasible large ligand libraries [33][34][35][36]. However, while the availability of experimental GPCR structures is growing, the currently available structures are mostly limited to inactive states.…”

Section: Structure-based Virtual Screeningmentioning

confidence: 99%

Automated discovery of GPCR bioactive ligands

Raschka

2019

Current Opinion in Structural Biology

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While G-protein coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Due to the involvement of GPCRs in various signaling pathways and physiological roles, the identification of endogenous ligands as well as designing novel drugs is of high interest to the research and medical communities. Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances for automating the discovery of bioactive ligands using machine learning. Machine learning is centered around the development and applications of algorithms that can learn from data automatically. Such an approach offers immense opportunities for bioactivity prediction as well as quantitative structureactivity relationship studies. This review describes the most recent and successful applications of machine learning for bioactive ligand discovery, concluding with an outlook on deep learning methods that are capable of automatically extracting salient information from structural data as a promising future direction for rapid and efficient bioactive ligand discovery.

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