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      Effect of mutations on binding of ligands to guanine riboswitch probed by free energy perturbation and molecular dynamics simulations

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          Abstract

          Riboswitches can regulate gene expression by direct and specific interactions with ligands and have recently attracted interest as potential drug targets for antibacterial. In this work, molecular dynamics (MD) simulations, free energy perturbation (FEP) and molecular mechanics generalized Born surface area (MM-GBSA) methods were integrated to probe the effect of mutations on the binding of ligands to guanine riboswitch (GR). The results not only show that binding free energies predicted by FEP and MM-GBSA obtain an excellent correlation, but also indicate that mutations involved in the current study can strengthen the binding affinity of ligands GR. Residue-based free energy decomposition was applied to compute ligand-nucleotide interactions and the results suggest that mutations highly affect interactions of ligands with key nucleotides U22, U51 and C74. Dynamics analyses based on MD trajectories indicate that mutations not only regulate the structural flexibility but also change the internal motion modes of GR, especially for the structures J12, J23 and J31, which implies that the aptamer domain activity of GR is extremely plastic and thus readily tunable by nucleotide mutations. This study is expected to provide useful molecular basis and dynamics information for the understanding of the function of GR and possibility as potential drug targets for antibacterial.

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          MMPBSA.py: An Efficient Program for End-State Free Energy Calculations.

          MM-PBSA is a post-processing end-state method to calculate free energies of molecules in solution. MMPBSA.py is a program written in Python for streamlining end-state free energy calculations using ensembles derived from molecular dynamics (MD) or Monte Carlo (MC) simulations. Several implicit solvation models are available with MMPBSA.py, including the Poisson-Boltzmann Model, the Generalized Born Model, and the Reference Interaction Site Model. Vibrational frequencies may be calculated using normal mode or quasi-harmonic analysis to approximate the solute entropy. Specific interactions can also be dissected using free energy decomposition or alanine scanning. A parallel implementation significantly speeds up the calculation by dividing frames evenly across available processors. MMPBSA.py is an efficient, user-friendly program with the flexibility to accommodate the needs of users performing end-state free energy calculations. The source code can be downloaded at http://ambermd.org/ with AmberTools, released under the GNU General Public License.
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            Langevin stabilization of molecular dynamics

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              Gene regulation by riboswitches.

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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                26 July 2019
                07 June 2019
                07 June 2019
                : 47
                : 13
                : 6618-6631
                Affiliations
                [1 ]School of Science, Shandong Jiaotong University, Jinan 250357 China
                [2 ]NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
                [3 ]Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China
                Author notes
                To whom correspondence should be addressed. Email: tongzhu.work@ 123456gmail.com tzhu@ 123456lps.ecun.cn
                Correspondence may also be addressed to Jianzhong Chen. Email: jzchen@ 123456sdjtu.edu.cn chenjianzhong1970@ 123456163.com
                Author information
                http://orcid.org/0000-0003-1558-4398
                http://orcid.org/0000-0001-7472-3736
                Article
                gkz499
                10.1093/nar/gkz499
                6649850
                31173143
                866a09c4-56e3-49a2-b875-a7c043e996ab
                © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 29 May 2019
                : 22 May 2019
                : 26 January 2019
                Page count
                Pages: 14
                Funding
                Funded by: Ministry of Science and Technology of China
                Award ID: 2016YFA0501700
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 91641116
                Award ID: 11574184
                Funded by: Shanghai Municipal Education Commission 10.13039/501100003395
                Award ID: 201701070005E00020
                Funded by: Shandong Province Higher Educational Science and Technology Program
                Award ID: J18KA040
                Award ID: J17KA045
                Categories
                Computational Biology

                Genetics
                Genetics

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