Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)

Lee, Yoonji; Lazim, Raudah; Macalino, Stephani Joy Y.; Choi, Sun

doi:10.1016/j.sbi.2019.03.015

Cited by 36 publications

(39 citation statements)

References 61 publications

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“…This virtual screening can be made either directly through MD simulations of the chosen ligands or indirectly by ligand docking on selected receptor structures from MD simulations of apo receptors or ligand-bound receptors-the latter in the case of evaluating allosteric modulators. In this way, MD-based virtual screening can be a complement to the more classical structure-based virtual screening, which targets static crystal structures [9][10][11] . MD is a computational technique especially appropriate for addressing the flexibility of the target proteins, GPCRs in the present study.…”

Section: Resultsmentioning

confidence: 99%

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Statistics for the analysis of molecular dynamics simulations: providing P values for agonist-dependent GPCR activation

Bruzzese

Dalton

Giraldo

2020

Sci Rep

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Molecular dynamics (MD) is the common computational technique for assessing efficacy of GPCR-bound ligands. Agonist efficacy measures the capability of the ligand-bound receptor of reaching the active state in comparison with the free receptor. In this respect, agonists, neutral antagonists and inverse agonists can be considered. A collection of MD simulations of both the ligand-bound and the free receptor are needed to provide reliable conclusions. Variability in the trajectories needs quantification and proper statistical tools for meaningful and non-subjective conclusions. Multiple-factor (time, ligand, lipid) ANOVA with repeated measurements on the time factor is proposed as a suitable statistical method for the analysis of agonist-dependent GPCR activation MD simulations. Inclusion of time factor in the ANOVA model is consistent with the time-dependent nature of MD. Ligand and lipid factors measure agonist and lipid influence on receptor activation. Previously reported MD simulations of adenosine A2a receptor (A2aR) are reanalyzed with this statistical method. TM6–TM3 and TM7–TM3 distances are selected as dependent variables in the ANOVA model. The ligand factor includes the presence or absence of adenosine whereas the lipid factor considers DOPC or DOPG lipids. Statistical analysis of MD simulations shows the efficacy of adenosine and the effect of the membrane lipid composition. Subsequent application of the statistical methodology to NECA A2aR agonist, with resulting P values in consistency with its pharmacological profile, suggests that the method is useful for ligand comparison and potentially for dynamic structure-based virtual screening.

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Section: Resultsmentioning

confidence: 99%

“…10:19942 | https://doi.org/10.1038/s41598-020-77072-4 www.nature.com/scientificreports/ Examination of structural features depicting adenosine-dependent receptor activation in A2aR from 2 µs -length MD simulations. Lipid (DOPC, DOPG) and ligand (adenosine, APO) experimental conditions were considered.…”

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confidence: 99%

Statistics for the analysis of molecular dynamics simulations: providing P values for agonist-dependent GPCR activation

Bruzzese

Dalton

Giraldo

2020

Sci Rep

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“…G protein-coupled receptors (GPCRs) have been known as the largest family of human membrane protein that plays crucial roles in many biological processes such as vision, sensing, and neurotransmission [1][2][3][4][5][6][7][8]. Signal transmission through GPCRs is initiated by the binding of extracellular ligands including drugs, hormones, and other stimuli.…”

Section: Introductionmentioning

confidence: 99%

“…With the continuous solving of the 3D conformations of GPCRs, a thorough understanding of the structural features of the receptors, such as orthosteric and allosteric binding site configurations, structural flexibility, and domain motions, can be attained and utilized in SBDD strategies for the development of novel GPCR drugs with ameliorated efficacy, efficiency, and specificity. The application of MD simulations in GPCR studies has been broadly reported, giving useful insights into ligand-binding interactions and GPCR dynamics [4][5][6][7]. However, to obtain a better picture of the binding site conformation as well as the chemical properties of ligands bound to GPCRs, more accurate descriptions of binding sites are necessary.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies

et al. 2020

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G protein-coupled receptors (GPCRs) are major drug targets due to their ability to facilitate signal transduction across cell membranes, a process that is vital for many physiological functions to occur. The development of computational technology provides modern tools that permit accurate studies of the structures and properties of large chemical systems, such as enzymes and GPCRs, at the molecular level. The advent of multiscale molecular modeling permits the implementation of multiple levels of theories on a system of interest, for instance, assigning chemically relevant regions to high quantum mechanics (QM) level of theory while treating the rest of the system using classical force field (molecular mechanics (MM) potential). Multiscale QM/MM molecular modeling have far-reaching applications in the rational design of GPCR drugs/ligands by affording precise ligand binding configurations through the consideration of conformational plasticity. This enables the identification of key binding site residues that could be targeted to manipulate GPCR function. This review will focus on recent applications of multiscale QM/MM molecular simulations in GPCR studies that could boost the efficiency of future structure-based drug design (SBDD) strategies.

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“…A LTHOUGH in silico methods are thought to be effective strategies for improving the long, expensive process of drug discovery, in silico drug discovery is, at best, still under development [1]- [3]. In addition to the two main approaches for drug discovery, i.e., ligand-based drug discovery [4]- [6] and structure-based drug discovery [7]- [9], interest in gene expression profile-based drug discovery [10] has recently increased. For this process, it is important to understand how drug treatments alter gene expression profiles.…”

Section: Introductionmentioning

confidence: 99%

Novel method for the prediction of drug-drug Interaction based on Gene Expression profiles

Taguchi

Turki

2020

Preprint

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The accurate prediction of new interactions between drugs is important for avoiding unknown (mild or severe) adverse reactions to drug combinations. The development of effective in silico methods for evaluating drug interactions based on gene expression data requires an understanding of how various drugs alter gene expression. Current computational methods for the prediction of drug-drug interactions (DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited in the absence of known predictive DDIs. To improve DDIs' interpretation, a recent study has demonstrated strong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervised learning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D. We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. We found that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot always be attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs.

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Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)

Cited by 36 publications

References 61 publications

Statistics for the analysis of molecular dynamics simulations: providing P values for agonist-dependent GPCR activation

Statistics for the analysis of molecular dynamics simulations: providing P values for agonist-dependent GPCR activation

Multiscale Molecular Modeling in G Protein-Coupled Receptor (GPCR)-Ligand Studies

Novel method for the prediction of drug-drug Interaction based on Gene Expression profiles

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