ArticlePloS one2012
Hybrid molecular mechanics/coarse-grained simulations for structural prediction of G-protein coupled receptor/ligand complexes.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Fast, Accurate, and System-Specific Variable-Resolution Modeling of Proteins.Journal of chemical information and modeling · 2023Article
- Lowered sensitivity of bitter taste receptors to β-glucosides in bamboo lemurs: an instance of parallel and adaptive functional decline in TAS2R16?Proceedings. Biological sciences · 2021Article
- Comparative modelling unravels the structural features of eukaryotic TCTP implicated in its multifunctional properties: an in silico approach.Journal of molecular modeling · 2021Article
- From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules.Frontiers in molecular biosciences · 2021Review
- Hybrid MM/CG Webserver: Automatic Set Up of Molecular Mechanics/Coarse-Grained Simulations for Human G Protein-Coupled Receptor/Ligand Complexes.Frontiers in molecular biosciences · 2020Article
- Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins.The Journal of membrane biology · 2019Review
- Dual binding mode of "bitter sugars" to their human bitter taste receptor target.Scientific reports · 2019Article
- Open-Boundary Molecular Mechanics/Coarse-Grained Framework for Simulations of Low-Resolution G-Protein-Coupled Receptor-Ligand Complexes.Journal of chemical theory and computation · 2019Article
- Computational studies of protein-protein dissociation by statistical potential and coarse-grained simulations: a case study on interactions between colicin E9 endonuclease and immunity proteins.Physical chemistry chemical physics : PCCP · 2019Article
- Understanding Ligand Binding to G-Protein Coupled Receptors Using Multiscale Simulations.Frontiers in molecular biosciences · 2019Review
- Toward the Identification of Extra-Oral TAS2R Agonists as Drug Agents for Muscle Relaxation Therapies via Bioinformatics-Aided Screening of Bitter Compounds in Traditional Chinese Medicine.Frontiers in physiology · 2019Article
- Allosteric sodium binding cavity in GPR3: a novel player in modulation of Aβ production.Scientific reports · 2018Article
- The Model Structures of the Complement Component 5a Receptor (C5aR) Bound to the Native and EngineeredScientific reports · 2018Article
- General principles of binding between cell surface receptors and multi-specific ligands: A computational study.PLoS computational biology · 2017Article
- Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis.Frontiers in molecular biosciences · 2017Article
- A multi-scale-multi-stable model for the rhodopsin photocycle.Molecules (Basel, Switzerland) · 2014Article
- In silico study of the structurally similar ORL1 receptor agonist and antagonist pairs reveal possible mechanism of receptor activation.The protein journal · 2014Article
- Coarse-grained model for colloidal protein interactions, B(22), and protein cluster formation.The journal of physical chemistry. B · 2013Article
- Genome-wide Membrane Protein Structure Prediction.Current genomics · 2013Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding how ligands bind to G-protein coupled receptors (GPCRs) provides insights into a myriad of cell processes and is crucial for drug development. Here we extend a hybrid molecular mechanics/coarse-grained (MM/CG) approach applied previously to enzymes to GPCR/ligand complexes. The accuracy of this method for structural predictions is established by comparison with recent atomistic molecular dynamics simulations on the human β2 adrenergic receptor, a member of the GPCRs superfamily. The results obtained with the MM/CG methodology show a good agreement with previous all-atom classical dynamics simulations, in particular in the structural description of the ligand binding site. This approach could be used for high-throughput predictions of ligand poses in a variety of GPCRs.
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