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      The full activation mechanism of the adenosine A 1 receptor revealed by GaMD and Su-GaMD simulations

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          Significance

          Clarifying the recognition pathways of agonist and G protein to G protein–coupled receptor (GPCR) is essential to understand the signal transduction mechanism of GPCR. However, it is still challenging to simulate the full activation process of GPCR on a reasonable simulation timescale with conventional molecular dynamics (MD) methods. Here, we developed an MD simulation approach named supervised Gaussian accelerated MD (Su-GaMD) and revealed the full activation mechanism of adenosine (Ado) A 1 receptor (A 1R) (including adenosine Ado−A 1R recognition, preactivation of A 1R, and A 1R−G protein recognition) in hundreds of nanoseconds simulations. The whole activation process and the metastable intermediate states revealed in this study could provide complementary structural characterizations to expand our perspectives on A 1R drug discovery.

          Abstract

          The full activation process of G protein–coupled receptor (GPCR) plays an important role in cellular signal transduction. However, it remains challenging to simulate the whole process in which the GPCR is recognized and activated by a ligand and then couples to the G protein on a reasonable simulation timescale. Here, we developed a molecular dynamics (MD) approach named supervised (Su) Gaussian accelerated MD (GaMD) by incorporating a tabu-like supervision algorithm into a standard GaMD simulation. By using this Su-GaMD method, from the active and inactive structure of adenosine A 1 receptor (A 1R), we successfully revealed the full activation mechanism of A 1R, including adenosine (Ado)–A 1R recognition, preactivation of A 1R, and A 1R–G protein recognition, in hundreds of nanoseconds of simulations. The binding of Ado to the extracellular side of A 1R initiates conformational changes and the preactivation of A 1R. In turn, the binding of G i2 to the intracellular side of A 1R causes a decrease in the volume of the extracellular orthosteric site and stabilizes the binding of Ado to A 1R. Su-GaMD could be a useful tool to reconstruct or even predict ligand–protein and protein–protein recognition pathways on a short timescale. The intermediate states revealed in this study could provide more detailed complementary structural characterizations to facilitate the drug design of A 1R in the future.

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          Most cited references55

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          Development and testing of a general amber force field.

          We describe here a general Amber force field (GAFF) for organic molecules. GAFF is designed to be compatible with existing Amber force fields for proteins and nucleic acids, and has parameters for most organic and pharmaceutical molecules that are composed of H, C, N, O, S, P, and halogens. It uses a simple functional form and a limited number of atom types, but incorporates both empirical and heuristic models to estimate force constants and partial atomic charges. The performance of GAFF in test cases is encouraging. In test I, 74 crystallographic structures were compared to GAFF minimized structures, with a root-mean-square displacement of 0.26 A, which is comparable to that of the Tripos 5.2 force field (0.25 A) and better than those of MMFF 94 and CHARMm (0.47 and 0.44 A, respectively). In test II, gas phase minimizations were performed on 22 nucleic acid base pairs, and the minimized structures and intermolecular energies were compared to MP2/6-31G* results. The RMS of displacements and relative energies were 0.25 A and 1.2 kcal/mol, respectively. These data are comparable to results from Parm99/RESP (0.16 A and 1.18 kcal/mol, respectively), which were parameterized to these base pairs. Test III looked at the relative energies of 71 conformational pairs that were used in development of the Parm99 force field. The RMS error in relative energies (compared to experiment) is about 0.5 kcal/mol. GAFF can be applied to wide range of molecules in an automatic fashion, making it suitable for rational drug design and database searching. Copyright 2004 Wiley Periodicals, Inc.
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            ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB.

            Molecular mechanics is powerful for its speed in atomistic simulations, but an accurate force field is required. The Amber ff99SB force field improved protein secondary structure balance and dynamics from earlier force fields like ff99, but weaknesses in side chain rotamer and backbone secondary structure preferences have been identified. Here, we performed a complete refit of all amino acid side chain dihedral parameters, which had been carried over from ff94. The training set of conformations included multidimensional dihedral scans designed to improve transferability of the parameters. Improvement in all amino acids was obtained as compared to ff99SB. Parameters were also generated for alternate protonation states of ionizable side chains. Average errors in relative energies of pairs of conformations were under 1.0 kcal/mol as compared to QM, reduced 35% from ff99SB. We also took the opportunity to make empirical adjustments to the protein backbone dihedral parameters as compared to ff99SB. Multiple small adjustments of φ and ψ parameters were tested against NMR scalar coupling data and secondary structure content for short peptides. The best results were obtained from a physically motivated adjustment to the φ rotational profile that compensates for lack of ff99SB QM training data in the β-ppII transition region. Together, these backbone and side chain modifications (hereafter called ff14SB) not only better reproduced their benchmarks, but also improved secondary structure content in small peptides and reproduction of NMR χ1 scalar coupling measurements for proteins in solution. We also discuss the Amber ff12SB parameter set, a preliminary version of ff14SB that includes most of its improvements.
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              Lipid14: The Amber Lipid Force Field

              The AMBER lipid force field has been updated to create Lipid14, allowing tensionless simulation of a number of lipid types with the AMBER MD package. The modular nature of this force field allows numerous combinations of head and tail groups to create different lipid types, enabling the easy insertion of new lipid species. The Lennard-Jones and torsion parameters of both the head and tail groups have been revised and updated partial charges calculated. The force field has been validated by simulating bilayers of six different lipid types for a total of 0.5 μs each without applying a surface tension; with favorable comparison to experiment for properties such as area per lipid, volume per lipid, bilayer thickness, NMR order parameters, scattering data, and lipid lateral diffusion. As the derivation of this force field is consistent with the AMBER development philosophy, Lipid14 is compatible with the AMBER protein, nucleic acid, carbohydrate, and small molecule force fields.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                10 October 2022
                18 October 2022
                10 April 2023
                : 119
                : 42
                : e2203702119
                Affiliations
                [1] aState Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for Cell Responses, College of Pharmacy and Tianjin Key Laboratory of Molecular Drug Research, Nankai University , Tianjin 300350, China;
                [2] bCollege of Life Sciences, Nankai University , Tianjin 300350, China;
                [3] cBiodesign Center, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences , Tianjin 300308, China;
                [4] dPlatform of Pharmaceutical Intelligence, Tianjin International Joint Academy of Biomedicine , Tianjin 300457, China
                Author notes
                2To whom correspondence may be addressed. Email: dongmeili@ 123456nankai.edu.cn or jianpinglin@ 123456nankai.edu.cn .

                Edited by Yinglong Miao, The University of Kansas, Lawrence, KS; received March 2, 2022; accepted September 14, 2022 by Editorial Board Member J. A. McCammon

                Author contributions: Y.L., D.L., and J.L. designed research; Y.L. and J.S. performed research; Y.L., D.L., and J.L. analyzed data; and Y.L., D.L., and J.L. wrote the paper.

                1Y.L. and J.S. contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-0777-719X
                https://orcid.org/0000-0003-4599-7978
                https://orcid.org/0000-0002-6030-1062
                Article
                202203702
                10.1073/pnas.2203702119
                9586258
                36215480
                92e6bb90-e47a-4f9f-ad4f-25ce8dd1437a
                Copyright © 2022 the Author(s). Published by PNAS.

                This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                : 14 September 2022
                Page count
                Pages: 9
                Funding
                Funded by: China Postdoctoral Science Foundation | National Postdoctoral Program for Innovative Talents (Postdoctoral Innovation Talent Support Program of China) 501100012152
                Award ID: Grant No. BSMS69004
                Award Recipient : Yang Li
                Categories
                408
                Biological Sciences
                Biophysics and Computational Biology

                g protein–coupled receptor,molecular dynamics simulations,ligand‒protein recognition pathway,protein‒protein recognition pathway,enhanced sampling method

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