Molecular Dynamics and MM-PBSA Studies for Deciphering Molecular Interactions of Valproic Acid with CYP2C9 Mutants F114L and I207T

Year 2021, , 64 - 73, 20.04.2021

Ahmet Can Timuçin

https://doi.org/10.19113/sdufenbed.801973

Abstract

Valproic Acid (VPA) is a widely used drug, particularly in neuropsychiatric disorders, while showing promise in other types of diseases such as cancer. VPA metabolism via cytochrome P450 (CYP) pathway is responsible from only ~10% of the total drug dose. However, due to high risk of severe adverse reactions in liver and pancreas, interaction of VPA with CYP2C9 remains to be delineated chiefly in CYP2C9 mutants. Hence, here we implemented a molecular dynamics study, followed by MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method based relative binding energy estimation to understand how F114L and I207T CYP2C9 mutants changed their binding mode towards VPA in comparison to wild type (WT) CYP2C9. Results indicated that while F114L and I207T mutants have showed significant decrease in total relative binding energy, compared with WT, there were a clear shift of occupied amino acids responsible for VPA interaction in mutants vs WT. Overall, here for the first time in literature, this novel shift of VPA interacting amino acids in F114L and I207T mutants were reported. Limitations and future perspective of the data were also discussed.

Keywords

CYP2C9, MD Simulations, MM-PBSA, Valproic Acid

Thanks

Molecular dynamics and MM-PBSA calculations described in this study were fully implemented at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources).

Valproik Asit ile CYP2C9 Mutantları F114L ve I207T Moleküler Etkileşimlerinin Çözümlenmesi için Moleküler Dinamik ve MM-PBSA Çalışmaları

Abstract

Valproik asit (VPA), özellikle nöropsikiyatrik bozukluklarda kullanılan bir ilaç olup, kanser gibi diğer tip hastalıklarda da umut vaat etmektedir. Sitokrom P450 (CYP) yolağı tarafından gerçekleştirilen VPA metabolizması toplam ilaç dozunun sadece %10 kadarıdır. Ancak, karaciğer ve pankreasta şiddetli yan etki reaksiyonları gösterme riski yüksek olduğu için, VPA`nın CYP2C9 ile ve özellikle CYP2C9 mutantları ile nasıl etkileştiği açıklanmayı beklemektedir. Bu nedenle, biz burada moleküler dinamik çalışmaları ve devamında yapılan MM-PBSA (Moleküler Mekanik Poisson-Boltzmann Yüzey Alanı) metodu tabanlı bağıl bağlanma enerjisi tahmini ile CYP2C9 mutantları F114L ve I207T`nin VPA ile bağlanma modunun doğal fenotipli (WT) CYP2C9`a göre nasıl değiştiğini anlamayı amaçladık. Sonuçlar, F114L ve I207T mutantlarının toplam bağıl bağlanma enerjilerinin doğal fenotipe göre önemli miktarda düştüğünü göstermiş olup, ayrıca mutantların VPA etkileşimi için kullandıkları amino asitlerde de, doğal fenotipe göre net bir değişim olduğunu ortaya koymuştur. Tümde, literatürde bir ilk olarak, VPA ile etkileşen amino asitlerdeki bu özgün değişim raporlanmıştır. Verilerin kısıtlamaları ve gelecek perspektifi de ayrıca tartışılmıştır.

Keywords

CYP2C9, MD Simulasyonları, MM-PBSA, Valproik Asit

References

• Chateauvieux, S., Morceau, F., Dicato, M., Diederich, M. 2010. Molecular and therapeutic potential and toxicity of valproic acid. J Biomed Biotechnol, 2010.
• Ghodke-Puranik, Y., Thorn, C. F., Lamba, J. K., Leeder, J. S., Song, W., Birnbaum, A. K., Altman, R. B., Klein, T. E. 2013. Valproic acid pathway: pharmacokinetics and pharmacodynamics. Pharmacogenet Genomics, 23(4), 236-241.
• Terbach, N., Williams, R. S. 2009. Structure-function studies for the panacea, valproic acid. Biochem Soc Trans, 37(Pt 5), 1126-1132.
• Leppik, I. E., Birnbaum, A. K. 2010. Epilepsy in the elderly. Ann N Y Acad Sci, 1184 208-224.
• Tan, L., Yu, J. T., Sun, Y. P., Ou, J. R., Song, J. H., Yu, Y. 2010. The influence of cytochrome oxidase CYP2A6, CYP2B6, and CYP2C9 polymorphisms on the plasma concentrations of valproic acid in epileptic patients. Clin Neurol Neurosurg, 112(4), 320-323.
• Ito, M., Ikeda, Y., Arnez, J. G., Finocchiaro, G., Tanaka, K. 1990. The enzymatic basis for the metabolism and inhibitory effects of valproic acid: dehydrogenation of valproyl-CoA by 2-methyl-branched-chain acyl-CoA dehydrogenase. Biochim Biophys Acta, 1034(2), 213-218.
• Argikar, U. A., Remmel, R. P. 2009. Effect of aging on glucuronidation of valproic acid in human liver microsomes and the role of UDP-glucuronosyltransferase UGT1A4, UGT1A8, and UGT1A10. Drug Metab Dispos, 37(1), 229-236.
• Sadeque, A. J., Fisher, M. B., Korzekwa, K. R., Gonzalez, F. J., Rettie, A. E. 1997. Human CYP2C9 and CYP2A6 mediate formation of the hepatotoxin 4-ene-valproic acid. J Pharmacol Exp Ther, 283(2), 698-703.
• Kiang, T. K., Ho, P. C., Anari, M. R., Tong, V., Abbott, F. S., Chang, T. K. 2006. Contribution of CYP2C9, CYP2A6, and CYP2B6 to valproic acid metabolism in hepatic microsomes from individuals with the CYP2C9*1/*1 genotype. Toxicol Sci, 94(2), 261-271.
• Ho, P. C., Abbott, F. S., Zanger, U. M., Chang, T. K. 2003. Influence of CYP2C9 genotypes on the formation of a hepatotoxic metabolite of valproic acid in human liver microsomes. Pharmacogenomics J, 3(6), 335-342.
• Bello, M., Mendieta-Wejebe, J. E., Correa-Basurto, J. 2014. Structural and energetic analysis to provide insight residues of CYP2C9, 2C11 and 2E1 involved in valproic acid dehydrogenation selectivity. Biochem Pharmacol, 90(2), 145-158.
• Daly, A. K., Rettie, A. E., Fowler, D. M., Miners, J. O. 2017. Pharmacogenomics of CYP2C9: Functional and Clinical Considerations. J Pers Med, 8(1).
• Isvoran, A., Louet, M., Vladoiu, D. L., Craciun, D., Loriot, M. A., Villoutreix, B. O., Miteva, M. A. 2017. Pharmacogenomics of the cytochrome P450 2C family: impacts of amino acid variations on drug metabolism. Drug Discov Today, 22(2), 366-376.
• Veronese, M. E., Miners, J. O., Rees, D. L., Birkett, D. J. 1993. Tolbutamide hydroxylation in humans: lack of bimodality in 106 healthy subjects. Pharmacogenetics, 3(2), 86-93.
• Zhang, H. F., Wang, H. H., Gao, N., Wei, J. Y., Tian, X., Zhao, Y., Fang, Y., Zhou, J., Wen, Q., Gao, J., Zhang, Y. J., Qian, X. H., Qiao, H. L. 2016. Physiological Content and Intrinsic Activities of 10 Cytochrome P450 Isoforms in Human Normal Liver Microsomes. J Pharmacol Exp Ther, 358(1), 83-93.
• Webb, B., Sali, A. 2016. Comparative Protein Structure Modeling Using MODELLER. Curr Protoc Bioinformatics, 54 5 6 1-5 6 37.
• Humphrey, W., Dalke, A., Schulten, K. 1996. VMD: visual molecular dynamics. J Mol Graph, 14(1), 33-38, 27-38.
• Trott, O., Olson, A. J. 2010. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem, 31(2), 455-461.
• Vanommeslaeghe, K., MacKerell, A. D., Jr. 2012. Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing. J Chem Inf Model, 52(12), 3144-3154.
• Vanommeslaeghe, K., Raman, E. P., MacKerell, A. D., Jr. 2012. Automation of the CHARMM General Force Field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Inf Model, 52(12), 3155-3168.
• Best, R. B., Zhu, X., Shim, J., Lopes, P. E., Mittal, J., Feig, M., Mackerell, A. D., Jr. 2012. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone phi, psi and side-chain chi(1) and chi(2) dihedral angles. J Chem Theory Comput, 8(9), 3257-3273.
• Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmuller, H., MacKerell, A. D., Jr. 2017. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods, 14(1), 71-73.
• MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., Joseph-McCarthy, D., Kuchnir, L., Kuczera, K., Lau, F. T., Mattos, C., Michnick, S., Ngo, T., Nguyen, D. T., Prodhom, B., Reiher, W. E., Roux, B., Schlenkrich, M., Smith, J. C., Stote, R., Straub, J., Watanabe, M., Wiorkiewicz-Kuczera, J., Yin, D., Karplus, M. 1998.
• Mackerell, A. D., Jr., Feig, M., Brooks, C. L., 3rd. 2004. Extending the treatment of backbone energetics in protein force fields: limitations of gas-phase quantum mechanics in reproducing protein conformational distributions in molecular dynamics simulations. J Comput Chem, 25(11), 1400-1415.
• Jorgensen, W. L., Madura, J. D. 1983. Quantum and statistical mechanical studies of liquids. 25. Solvation and conformation of methanol in water. Journal of the American Chemical Society, 105(6), 1407-1413.
• Martyna, G. J., Hughes, A., Tuckerman, M. E. 1999. Molecular dynamics algorithms for path integrals at constant pressure. Journal of Chemical Physics, 110(7), 3275-3290.
• Feller, S. E., Zhang, Y. H., Pastor, R. W., Brooks, B. R. 1995. Constant-Pressure Molecular-Dynamics Simulation - the Langevin Piston Method. Journal of Chemical Physics, 103(11), 4613-4621.
• Hoover, W. G. 1985. Canonical dynamics: Equilibrium phase-space distributions. Physical Review A, 31(3), 1695-1697.
• Wallace, A. C., Laskowski, R. A., Thornton, J. M. 1995. LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein Eng, 8(2), 127-134.
• Miller, B. R., 3rd, McGee, T. D., Jr., Swails, J. M., Homeyer, N., Gohlke, H., Roitberg, A. E. 2012. MMPBSA.py: An Efficient Program for End-State Free Energy Calculations. J Chem Theory Comput, 8(9), 3314-3321.
• 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 Res, 28(1), 235-242.
• Wester, M. R., Yano, J. K., Schoch, G. A., Yang, C., Griffin, K. J., Stout, C. D., Johnson, E. F. 2004. The structure of human cytochrome P450 2C9 complexed with flurbiprofen at 2.0-A resolution. J Biol Chem, 279(34), 35630-35637.
• Tracy, T. S., Hutzler, J. M., Haining, R. L., Rettie, A. E., Hummel, M. A., Dickmann, L. J. 2002. Polymorphic variants (CYP2C9*3 and CYP2C9*5) and the F114L active site mutation of CYP2C9: effect on atypical kinetic metabolism profiles. Drug Metab Dispos, 30(4), 385-390.
• Dai, D. P., Xu, R. A., Hu, L. M., Wang, S. H., Geng, P. W., Yang, J. F., Yang, L. P., Qian, J. C., Wang, Z. S., Zhu, G. H., Zhang, X. H., Ge, R. S., Hu, G. X., Cai, J. P. 2014. CYP2C9 polymorphism analysis in Han Chinese populations: building the largest allele frequency database. Pharmacogenomics J, 14(1), 85-92.
• Genheden, S., Ryde, U. 2015. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov, 10(5), 449-461.
• Spackova, N., Cheatham, T. E., 3rd, Ryjacek, F., Lankas, F., Van Meervelt, L., Hobza, P., Sponer, J. 2003. Molecular dynamics simulations and thermodynamics analysis of DNA-drug complexes. Minor groove binding between 4',6-diamidino-2-phenylindole and DNA duplexes in solution. J Am Chem Soc, 125(7), 1759-1769.
• Yang, T., Wu, J. C., Yan, C., Wang, Y., Luo, R., Gonzales, M. B., Dalby, K. N., Ren, P. 2011. Virtual screening using molecular simulations. Proteins, 79(6), 1940-1951.
• Foloppe, N., Hubbard, R. 2006. Towards predictive ligand design with free-energy based computational methods? Curr Med Chem, 13(29), 3583-3608.
• Wang, J. M., Hou, T. J., Xu, X. J. 2006. Recent Advances in Free Energy Calculations with a Combination of Molecular Mechanics and Continuum Models. Current Computer-Aided Drug Design, 2(3), 287-306.
• Homeyer, N., Gohlke, H. 2012. Free Energy Calculations by the Molecular Mechanics Poisson-Boltzmann Surface Area Method. Molecular Informatics, 31(2), 114-122.
• Atkins, W. M. 2005. Non-Michaelis-Menten kinetics in cytochrome P450-catalyzed reactions. Annu Rev Pharmacol Toxicol, 45 291-310.
• Hutzler, J. M., Hauer, M. J., Tracy, T. S. 2001. Dapsone activation of CYP2C9-mediated metabolism: evidence for activation of multiple substrates and a two-site model. Drug Metab Dispos, 29(7), 1029-1034.