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/s41592-020-0884-y

Cited by 102 publications

(97 citation statements)

References 42 publications

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“…Three-dimensional structures of GPCRs reveal allosteric binding sites spanning extracellular, transmembrane, and intracellular regions; identification of novel allosteric sites in GPCRs can provide alternative options for drug discovery 39 . To demonstrate the use of BiteNet in spatiotemporal identification of GPCR binding sites we analyzed molecular dynamics trajectories of the human adenosine A2A receptor (A2A) retrieved from the GPCRmd repository 40 .…”

Section: Resultsmentioning

confidence: 99%

Spatiotemporal identification of druggable binding sites using deep learning

Kozlovskii

Popov

2020

Commun Biol

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Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site identification resemble the object detection problem in computer vision. Here we introduce a computational approach for the large-scale detection of protein binding sites, that considers protein conformations as 3D-images, binding sites as objects on these images to detect, and conformational ensembles of proteins as 3D-videos to analyze. BiteNet is suitable for spatiotemporal detection of hard-to-spot allosteric binding sites, as we showed for conformation-specific binding site of the epidermal growth factor receptor, oligomer-specific binding site of the ion channel, and binding site in G protein-coupled receptor. BiteNet outperforms state-of-the-art methods both in terms of accuracy and speed, taking about 1.5 minutes to analyze 1000 conformations of a protein with ~2000 atoms.

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Section: Resultsmentioning

confidence: 99%

Spatiotemporal identification of druggable binding sites using deep learning

Kozlovskii

Popov

2020

Commun Biol

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“…Such information would help us better understand GPCR physiology and inform the design of molecules with a specific signaling profile [ 52 ]. A valuable resource of ligand binding dynamics is found in the recently established GPCRmd server [ 53 ], which provides intuitive visualization and analysis tools currently covering 70% of crystallized receptor subtypes ( Figure 2 ).…”

Section: Complementing Static Datamentioning

confidence: 99%

“…To analyze them, more specialized tools have been developed, such as the python module GetContacts [ 141 ]. A good example of the capabilities of this module can be found in the Receptor Meta-analysis web tool ( ) included in the GPCRmd platform [ 53 ]. This tool analyzes and compares different types of non-covalent interactions obtained from a large GPCR simulation dataset using GetContacts scripts, and displays them into a series of interactive plots ( Figure 5 ).…”

Section: MD Analysis—extracting Data From the Simulationsmentioning

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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“…Consequently, the call for public availability and conservation of data has extended to molecular dynamics (MD) simulation trajectories of biomolecules, [20][21][22] and the discussion on how and by whom such databanks for dynamic structures would be set up is currently active. [23][24][25][26] While there are currently no general MD databanks in operation, individual databanks are accepting contributions on nucleic acid, 27 protein/DNA/RNA, 28 cyclodextrin, 29 G-protein-coupled receptor, 30 and lipid bilayer 31 simulations.…”

Section: Introductionmentioning

confidence: 99%

Using open data to rapidly benchmark biomolecular simulations: Phospholipid conformational dynamics

Antila

Ferreira

Ollila

et al. 2020

Preprint

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Molecular dynamics (MD) simulations are widely used to monitor time-resolved motions of biomacromolecules, although it often remains unknown how closely the conformational dynamics correspond to those occurring in real life. Here, we used a large set of open-access MD trajectories of phosphatidylcholine (PC) lipid bilayers to benchmark the conformational dynamics in several contemporary MD models (force fields) against nuclear magnetic resonance (NMR) data available in the literature: effective correlation times and spin-lattice relaxation rates.We found none of the tested MD models to fully reproduce the conformational dynamics. That said, the dynamics in CHARMM36 and Slipids are more realistic than in the Amber Lipid14, OPLS-based MacRog, and GROMOS-based Berger force fields, whose sampling of the glycerol backbone conformations is too slow. The performance of CHARMM36 persists when cholesterol is added to the bilayer, and when the hydration level is reduced. However, for conformational dynamics of the PC headgroup, both with and without cholesterol, Slipids provides the most realistic description, because CHARMM36 overestimates the relative weight of ~1-ns processes in the headgroup dynamics.We stress that not a single new simulation was run for the present work. This demonstrates the worth of open-access MD trajectory databanks for the indispensable step of any serious MD study: Benchmarking the available force fields. We believe this proof of principle will inspire other novel applications of MD trajectory databanks, and thus aid in developing biomolecular MD simulations into a true computational microscope—not only for lipid membranes, but for all biomacromolecular systems.Graphical TOC Entry

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GPCRmd uncovers the dynamics of the 3D-GPCRome

Cited by 102 publications

References 42 publications

Spatiotemporal identification of druggable binding sites using deep learning

Spatiotemporal identification of druggable binding sites using deep learning

How Do Molecular Dynamics Data Complement Static Structural Data of GPCRs

Using open data to rapidly benchmark biomolecular simulations: Phospholipid conformational dynamics

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