252
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

Molecular dynamics simulations of aqueous systems of inhibitor candidates for adenosine-5’-phosphosufate reductase

ORCID Icon, , , &
Pages 2466-2477 | Received 26 Aug 2021, Accepted 19 Jan 2022, Published online: 01 Feb 2022

References

  • Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B., & Lindahl, E. (2015, September). GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001
  • Albuquerque, S. O., Barros, T. G., Dias, L. R. S., Lima, C. H. D. S., Azevedo, P. H. R D. A., Flores-Junior, L. A. P., dos Santos, E. G., Loponte, H. F., Pinheiro, S., Dias, W. B., Muri, E. M. F., & Todeschini, A. R. (2020, November). Biological evaluation and molecular modeling of peptidomimetic compounds as inhibitors for O-GlcNAc transferase (OGT). European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences, 154, 105510. https://doi.org/10.1016/j.ejps.2020.105510
  • Anantharaman, K., Hausmann, B., Jungbluth, S. P., Kantor, R. S., Lavy, A., Warren, L. A., Rappé, M. S., Pester, M., Loy, A., Thomas, B. C., & Banfield, J. F. (2018). Expanded diversity of microbial groups that shape the dissimilatory sulfur cycle. The ISME Journal, 12(7), 1715–1728. https://doi.org/10.1038/s41396-018-0078-0
  • Baker, N. A., Sept, D., Joseph, S., Holst, M. J., & McCammon, J. A. (2001). Electrostatics of nanosystems: Application to microtubules and the ribosome. Proceedings of the National Academy of Sciences, 98(18), 10037–10041. https://doi.org/10.1073/pnas.181342398
  • Becke, A. D. (1993). Density‐functional thermochemistry. III. The role of exact exchange. The Journal of Chemical Physics, 98(7), 5648–5652. https://doi.org/10.1063/1.464913
  • Berendsen, H. J. C., van der Spoel, D., & van Drunen, R. (1995). GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communications, 91(1–3), 43–56. https://doi.org/10.1016/0010-4655(95)00042-E
  • Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., & Bourne, P. E. (2000). The protein data bank. Nucleic Acids Research, 28(1), 235–242. https://doi.org/10.1093/nar/28.1.235
  • Chiang, Y.-L., Hsieh, Y.-C., Fang, J.-Y., Liu, E.-H., Huang, Y.-C., Chuankhayan, P., Jeyakanthan, J., Liu, M.-Y., Chan, S. I., & Chen, C.-J. (2009). Crystal structure of adenylylsulfate reductase from desulfovibrio gigas suggests a potential self-regulation mechanism involving the C terminus of the β-subunit. Journal of Bacteriology, 191(24), 7597–7608. https://doi.org/10.1128/JB.00583-09
  • d’Hardemare, A. D. M., Torelli, S., Serratrice, G., & Pierre, J.-L. (2006). Design of iron chelators: Syntheses and iron (III) complexing abilities of tripodal tris-bidentate ligands. BioMetals, 19(4), 349–366. https://doi.org/10.1007/s10534-005-2997-2
  • Da Silva, T. U., Pougy, K. D. C., Albuquerque, M. G., da Silva Lima, C. H., & Machado, S. D. P. (2020, November). Development of parameters compatible with the CHARMM36 force field for [Fe 4 S 4] 2+ clusters and molecular dynamics simulations of adenosine-5’-phosphosulfate reductase in GROMACS 2019. Journal of Biomolecular Structure and Dynamics, 1–11. https://doi.org/10.1080/07391102.2020.1847687
  • de Jesus, E. B., de Andrade Lima, L. R. P., Bernardez, L. A., & Almeida, P. F. (2015). Inhibition of microbial sulfate reduction by molybdate. Brazilian Journal of Petroleum and Gas, 9(3), 95–106. https://doi.org/10.5419/bjpg2015-0010
  • dos Santos, E. S., de Souza, L. C. V., de Assis, P. N., Almeida, P. F., & Ramos-de-Souza, E. (2014). Novel potential inhibitors for adenylylsulfate reductase to control souring of water in oil industries. Journal of Biomolecular Structure & Dynamics, 32(11), 1780–1792. https://doi.org/10.1080/07391102.2013.834850
  • dos Santos, E. S., de Souza, L. C. V., de Assis, P. N., de Almeida, P. F., & Ramos-de-Souza, E. (2015). Souring control in fluid samples of oil industry using a multiple ligand simultaneous docking (MLSD) strategy. Journal of Biomolecular Structure & Dynamics, 33(6), 1176–1184. https://doi.org/10.1080/07391102.2014.937461
  • dos Santos, E. S., Gritta, D. S., Taft, C. A., Almeida, P. F., & Ramos-de-Souza, E. (2010). Molecular dynamics simulation of the adenylylsulphate reductase from hyperthermophilic Archaeoglobus fulgidus. Molecular Simulation, 36(3), 199–203. https://doi.org/10.1080/08927020903177658
  • Dou, W., Jia, R., Jin, P., Liu, J., Chen, S., & Gu, T. (2018, November). Investigation of the mechanism and characteristics of copper corrosion by sulfate reducing bacteria. Corrosion Science, 144, 237–248. https://doi.org/10.1016/j.corsci.2018.08.055
  • Dou, W., Pu, Y., Han, X., Song, Y., Chen, S., & Gu, T. (2020, June). Corrosion of Cu by a sulfate reducing bacterium in anaerobic vials with different headspace volumes. Bioelectrochemistry, 133, 107478. https://doi.org/10.1016/j.bioelechem.2020.107478
  • Duarte, A. G., Santos, A. A., & Pereira, I. A. C. (2016). Electron transfer between the QmoABC membrane complex and adenosine 5′-phosphosulfate reductase. Biochimica et Biophysica Acta , 1857(4), 380–386. https://doi.org/10.1016/j.bbabio.2016.01.001
  • Fang, J.-Y., Chiang, Y.-L., Hsieh, Y.-C., Wang, V. C.-C., Huang, Y.-C., Chuankhayan, P., Yang, M.-C., Liu, M.-Y., Chan, S. I., & Chen, C.-J. (2011). Crystallization of adenylylsulfate reductase from desulfovibrio gigas: A strategy based on controlled protein oligomerization. Crystal Growth & Design, 11(6), 2127–2134. https://doi.org/10.1021/cg1013818
  • Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., & Scalmani, G. (2009). Gaussian 09, Revision B.01-SMP.
  • Fritz, G., Roth, A., Schiffer, A., Buchert, T., Bourenkov, G., Bartunik, H. D., Huber, H., Stetter, K. O., Kroneck, P. M. H., & Ermler, U. (2002). Structure of adenylylsulfate reductase from the hyperthermophilic Archaeoglobus fulgidus at 1.6-A resolution. Proceedings of the National Academy of Sciences, 99(4), 1836–1841. https://doi.org/10.1073/pnas.042664399
  • Gomes, D. E. B., Silva, A. W., Lins, R. D., Pascutti, P. G., & Soares, T. A. (2009). HbMap2Grace. Software for mapping the hydrogen bond frequency.
  • Grein, F., Ramos, A. R., Venceslau, S. S., & Pereira, I. A. C. (2013). Unifying concepts in anaerobic respiration: insights from dissimilatory sulfur metabolism. Biochimica et Biophysica Acta (Bba) - Bioenergetics, 1827(2), 145–160. https://doi.org/10.1016/j.bbabio.2012.09.001
  • Hanwell, M. D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., & Hutchison, G. R. (2012). Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1), 17. https://doi.org/10.1186/1758-2946-4-17
  • Hess, B., Kutzner, C., van der Spoel, D., & Lindahl, E. (2008). GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of Chemical Theory and Computation, 4(3), 435–447. https://doi.org/10.1021/ct700301q
  • Huang, J., & MacKerell, A. D. (2013). CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. Journal of Computational Chemistry, 34(25), 2135–2145. https://doi.org/10.1002/jcc.23354
  • Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. https://doi.org/10.1016/0263-7855(96)00018-5
  • Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., Zaslavsky, L., Zhang, J., & Bolton, E. E. (2019). PubChem 2019 update: Improved access to chemical data. Nucleic Acids Research, 47(D1), D1102–9. https://doi.org/10.1093/nar/gky1033
  • Kumari, R., Kumar, R., & Lynn, A. (2014). g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54(7), 1951–1962. https://doi.org/10.1021/ci500020m
  • Lee, C., Yang, W., & Parr, R. G. (1988). Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Physical Review B, 37(2), 785–789. https://doi.org/10.1103/PhysRevB.37.785
  • Lindahl, E., Abraham, M. J., Hess, B., & van der Spoel, D. (2019). GROMACS 2019.1 source code. https://doi.org/10.5281/zenodo.2564764.
  • Lindahl, E., Hess, B., & van der Spoel, D. (2001). GROMACS 3.0: A package for molecular simulation and trajectory analysis. Journal of Molecular Modeling, 7(8), 306–317. https://doi.org/10.1007/s008940100045
  • Makeneni, S., Thieker, D. F., & Woods, R. J. (2018). Applying pose clustering and MD simulations to eliminate false positives in molecular docking. Journal of Chemical Information and Modeling, 58(3), 605–614. https://doi.org/10.1021/acs.jcim.7b00588
  • Mark, P., & Nilsson, L. (2001). Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. The Journal of Physical Chemistry A, 105(43), 9954–9960. https://doi.org/10.1021/jp003020w
  • Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
  • Páll, S., Abraham, M. J., Kutzner, C., Hess, B., & Lindahl, E. (2015). Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In International Conference on Exascale Applications and Software. EASC 2014: Solving Software Challenges for Exascale (Vol. 8759, pp. 3–27).
  • Petersson, G. A., & Al‐Laham, M. A. (1991). A complete basis set model chemistry. II. Open‐shell systems and the total energies of the first‐row atoms. The Journal of Chemical Physics, 94(9), 6081–6090. https://doi.org/10.1063/1.460447
  • Pronk, S., Páll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D., Hess, B., & Lindahl, E. (2013). GROMACS 4.5: A high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29(7), 845–854. https://doi.org/10.1093/bioinformatics/btt055
  • Sargsyan, K., Grauffel, C., & Lim, C. (2017). How molecular size impacts RMSD applications in molecular dynamics simulations. Journal of Chemical Theory and Computation, 13(4), 1518–1524. https://doi.org/10.1021/acs.jctc.7b00028
  • Schiffer, A., Fritz, G., Kroneck, P. M. H., & Ermler, U. (2006). Reaction mechanism of the iron-sulfur flavoenzyme adenosine-5′- phosphosulfate reductase based on the structural characterization of different enzymatic states. Biochemistry, 45(9), 2960–2967. https://doi.org/10.1021/bi0521689
  • Sousa, K. A. D., Cammarota, M. C., & Sérvulo, E. F. C. (2010). Efeito da aplicação de nitrato na redução biogênica de sulfeto sob diferentes concentrações iniciais de bactérias redutoras de nitrato e sulfato. Química Nova, 33(2), 273–278. https://doi.org/10.1590/S0100-40422010000200008
  • Souza, P., Goulart, F., Marques, J., Bizzo, H., Blank, A., Groposo, C., Sousa, M., Vólaro, V., Alviano, C., Moreno, D., & Seldin, L. (2017). Growth inhibition of sulfate-reducing bacteria in produced water from the petroleum industry using essential oils. Molecules, 22(4), 648. https://doi.org/10.3390/molecules22040648
  • van der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26(16), 1701–1718. https://doi.org/10.1002/jcc.20291
  • Stoeva, M. K., & Coates, J. D. (2019). Specific inhibitors of respiratory sulfate reduction: Towards a mechanistic understanding. Microbiology (Reading, England), 165(3), 254–269. https://doi.org/10.1099/mic.0.000750
  • Systèmes, D. (2016). Biovia Discovery Studio ® 2016 Comprehensive Modeling and Simulations.
  • Trott, O., & Olson, A. J. (2009). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/10.1002/jcc.21334
  • Turner, P. J. (2005). XMGRACE, Version 5.1.19. Center for Coastal and Land-Margin Research, Oregon Graduate Institute of Science and Technology. http://plasma-gate.weizmann.ac.il/Grace/
  • Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., & Mackerell, A. D. (2009). CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of Computational Chemistry, 31(4), 671–690. https://doi.org/10.1002/jcc.21367
  • Vernis, L., el Banna, N., Baïlle, D., Hatem, E., Heneman, A., & Huang, M. E. (2017). Fe-S clusters emerging as targets of therapeutic drugs. Oxidative Medicine and Cellular Longevity, 2017, 1–12. https://doi.org/10.1155/2017/3647657
  • Wójcik-Augustyn, A., Johansson, A. J., & Borowski, T. (2021). Reaction mechanism catalyzed by the dissimilatory adenosine 5′-phosphosulfate reductase. Adenosine 5′-monophosphate inhibitor and key role of arginine 317 in switching the course of catalysis. Biochimica et Biophysica Acta (BBA) - Bioenergetics, 1862(1), 148333. https://doi.org/10.1016/j.bbabio.2020.148333
  • Yuan, S., Chan, H. S., & Hu, Z. (2017). Using PyMOL as a platform for computational drug design. Computational Molecular Science, 7(2), e1298. https://doi.org/10.1002/wcms.1298

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.