Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome

Rodríguez‐Espigares, Ismael; Torrens‐Fontanals, Mariona; Tiemann, Johanna K. S.; Aranda-García, David; Ramírez-Anguita, Juan Manuel; Stępniewski, Tomasz Maciej; Worp, Nathalie; Varela-Rial, Alejandro; Morales-Pastor, Adrián; Medel-Lacruz, Brian; Pándy-Szekeres, Gáspár; Mayol, Eduardo; Giorgino, Toni; Carlsson, Jens; Deupí, Xavier; Filipek, Sławomir; Filizola, Marta; Gómez-Tamayo, José Carlos; González, Ángel F.; Gutiérrez‐de‐Terán, Hugo; Jiménez‐Rosés, Mireia; Jespers, Willem; Kapla, Jon; Khelashvili, George; Kolb, Peter; Latek, Dorota; Martí-Solano, Maria; Matricon, P.; Matsoukas, Minos‐Timotheos; Miszta, Przemysław; Olivella, Mireia; Pérez‐Benito, Laura; Provasi, Davide; Ríos, Santiago; Torrecillas, Iván R.; Sallander, Jessica; Sztyler, Agnieszka; Vasile, Silvana; Weinstein, Harel; Zachariae, Ulrich; Hildebrand, Peter W.; Fabritiis, Gianni De; Sanz, Ferrán; Gloriam, David E.; Cordomí, Arnau; Guixà-González, Ramón; Selent, Jana

doi:10.1038/s41592-020-0928-3

Cited by 3 publications

(3 citation statements)

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“…The MD simulation trajectories of the structure (ID 73) are available in the GPCRmd database (), , and there are three 500 ns trajectories with IDs 10713, 10714, and 10715. Figure S2 gives the time evolutions of root-mean-square deviation (RMSD) of backbone atoms of DOP during the three simulations.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, combined with CPL analysis, the AAN with edge weighted by cross-correlation from fcfGNM MD is used to explore the key residues in the allosteric communication. The MD simulation trajectories of the structure (ID 73) are available in the GPCRmd database (http://gpcrmd.org/), 40,41 and there are three 500 ns trajectories with IDs 10713, 10714, and 10715. Figure S2 gives the time evolutions of root-meansquare deviation (RMSD) of backbone atoms of DOP during the three simulations.…”

Section: ■ Introductionmentioning

confidence: 99%

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Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

Chen

Gong

Han

et al. 2022

J. Chem. Inf. Model.

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Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The δ opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNMMD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing functionally low-frequency modes. Additionally, fcfGNMMD can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNMMD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in δ opioid receptor and can provide valuable information for drug design.

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Section: Methodsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

Chen

Gong

Han

et al. 2022

J. Chem. Inf. Model.

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show abstract

“…The tool works directly in the web browser, enabling rapid analysis of structures. In 2020, a collaborative initiative resulted in the establishment of an open, interactive, and standardized database for GPCR MD simulations ( Rodríguez-Espigares et al, 2020a ; Rodríguez-Espigares et al, 2020b ). This web platform incorporates visualization features along with a comprehensive analysis toolbox, allowing users to visualize, analyze, and share GPCR molecular dynamics data.…”

Section: Scaling Up To Simulation Ensembles and Data Sharingmentioning

confidence: 99%

From complex data to clear insights: visualizing molecular dynamics trajectories

Belghit,

Spivak,

Dauchez

et al. 2024

Front. Bioinform.

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Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

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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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Publisher Correction: GPCRmd uncovers the dynamics of the 3D-GPCRome

Cited by 3 publications

References 0 publications

Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

From complex data to clear insights: visualizing molecular dynamics trajectories

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

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