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      Controlling opioid receptor functional selectivity by targeting distinct subpockets of the orthosteric site

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          Abstract

          Controlling receptor functional selectivity profiles for opioid receptors is a promising approach for discovering safer analgesics; however, the structural determinants conferring functional selectivity are not well understood. Here, we used crystal structures of opioid receptors, including the recently solved active state kappa opioid complex with MP1104, to rationally design novel mixed mu (MOR) and kappa (KOR) opioid receptor agonists with reduced arrestin signaling. Analysis of structure-activity relationships for new MP1104 analogs points to a region between transmembrane 5 (TM5) and extracellular loop (ECL2) as key for modulation of arrestin recruitment to both MOR and KOR. The lead compounds, MP1207 and MP1208, displayed MOR/KOR Gi-partial agonism with diminished arrestin signaling, showed efficient analgesia with attenuated liabilities, including respiratory depression and conditioned place preference and aversion in mice. The findings validate a novel structure-inspired paradigm for achieving beneficial in vivo profiles for analgesia through different mechanisms that include bias, partial agonism, and dual MOR/KOR agonism.

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          GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

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            CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field

            Proper treatment of nonbonded interactions is essential for the accuracy of molecular dynamics (MD) simulations, especially in studies of lipid bilayers. The use of the CHARMM36 force field (C36 FF) in different MD simulation programs can result in disagreements with published simulations performed with CHARMM due to differences in the protocols used to treat the long-range and 1-4 nonbonded interactions. In this study, we systematically test the use of the C36 lipid FF in NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM. A wide range of Lennard-Jones (LJ) cutoff schemes and integrator algorithms were tested to find the optimal simulation protocol to best match bilayer properties of six lipids with varying acyl chain saturation and head groups. MD simulations of a 1,2-dipalmitoyl-sn-phosphatidylcholine (DPPC) bilayer were used to obtain the optimal protocol for each program. MD simulations with all programs were found to reasonably match the DPPC bilayer properties (surface area per lipid, chain order parameters, and area compressibility modulus) obtained using the standard protocol used in CHARMM as well as from experiments. The optimal simulation protocol was then applied to the other five lipid simulations and resulted in excellent agreement between results from most simulation programs as well as with experimental data. AMBER compared least favorably with the expected membrane properties, which appears to be due to its use of the hard-truncation in the LJ potential versus a force-based switching function used to smooth the LJ potential as it approaches the cutoff distance. The optimal simulation protocol for each program has been implemented in CHARMM-GUI. This protocol is expected to be applicable to the remainder of the additive C36 FF including the proteins, nucleic acids, carbohydrates, and small molecules.
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              Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles.

              While the quality of the current CHARMM22/CMAP additive force field for proteins has been demonstrated in a large number of applications, limitations in the model with respect to the equilibrium between the sampling of helical and extended conformations in folding simulations have been noted. To overcome this, as well as make other improvements in the model, we present a combination of refinements that should result in enhanced accuracy in simulations of proteins. The common (non Gly, Pro) backbone CMAP potential has been refined against experimental solution NMR data for weakly structured peptides, resulting in a rebalancing of the energies of the α-helix and extended regions of the Ramachandran map, correcting the α-helical bias of CHARMM22/CMAP. The Gly and Pro CMAPs have been refitted to more accurate quantum-mechanical energy surfaces. Side-chain torsion parameters have been optimized by fitting to backbone-dependent quantum-mechanical energy surfaces, followed by additional empirical optimization targeting NMR scalar couplings for unfolded proteins. A comprehensive validation of the revised force field was then performed against data not used to guide parametrization: (i) comparison of simulations of eight proteins in their crystal environments with crystal structures; (ii) comparison with backbone scalar couplings for weakly structured peptides; (iii) comparison with NMR residual dipolar couplings and scalar couplings for both backbone and side-chains in folded proteins; (iv) equilibrium folding of mini-proteins. The results indicate that the revised CHARMM 36 parameters represent an improved model for the modeling and simulation studies of proteins, including studies of protein folding, assembly and functionally relevant conformational changes.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                08 February 2021
                2021
                : 10
                : e56519
                Affiliations
                [1 ] Department of Neurology and Molecular Pharmacology, Memorial Sloan Kettering Cancer Center New York United States
                [2 ] Department of Pharmacology, University of North Carolina Chapel Hill United States
                [3 ] Center for Clinical Pharmacology, St. Louis College of Pharmacy and Washington University School of Medicine St. Louis United States
                [4 ] Department of Anesthesiology, Washington University in St. Louis School of Medicine St. Louis United States
                [5 ] Department of Quantitative and Computational Biology, Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California Los Angeles United States
                [6 ] Division of Molecular Therapeutics, New York State Psychiatric Institute and Departments of Psychiatry, Pharmacology, Columbia University Vagelos College of Physicians & Surgeons New York United States
                [7 ] Department of Pharmacodynamics, University of Florida Gainesville United States
                [8 ] Department of Anesthesiology, Rutgers New Jersey Medical School, New Jersey Newark United States
                Weill Cornell Medicine United States
                Weill Cornell Medicine United States
                Weill Cornell Medicine United States
                Author notes
                [‡]

                Department of Anesthesiology,, Rutgers New Jersey Medical School, New Jersey, Newark, United States.

                [§]

                Deceased.

                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-7531-3587
                https://orcid.org/0000-0003-0986-9477
                http://orcid.org/0000-0002-9059-4791
                http://orcid.org/0000-0001-5591-377X
                http://orcid.org/0000-0001-8549-0413
                http://orcid.org/0000-0001-7395-2967
                https://orcid.org/0000-0003-3883-4505
                https://orcid.org/0000-0002-2931-3823
                Article
                56519
                10.7554/eLife.56519
                7909954
                33555255
                a945ffae-c390-4a23-b954-99182670ed22
                © 2021, Uprety et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 01 March 2020
                : 07 February 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA045884
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA046487
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000027, National Institute on Alcohol Abuse and Alcoholism;
                Award ID: AA026949
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA038858
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA035764
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA007242
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA042888
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: MH018870
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: MH112205
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA045657
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA006241
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: DA046714
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: W81XWH-17-1-0256
                Award Recipient :
                Funded by: St. Louis College of Pharmacy and Washington University;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000026, National Institute on Drug Abuse;
                Award ID: MH54137
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100006346, Hope for Depression Research Foundation;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award Recipient :
                Funded by: University of Florida Foundation;
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Biochemistry and Chemical Biology
                Custom metadata
                The structure-based design established a new approach to control pathway-selective activation of opioid receptors, resulting in new dual MOR/KOR G-protein biased agonist analgesics with attenuated liabilities.

                Life sciences
                safer opioids,gpcr bias,structure-based drug design,functional selectivity,boat/chair,mouse

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