Insight into the molecular mechanism of yeast acetyl-coenzyme A carboxylase mutants F510I, N485G, I69E, E477R, and K73R resistant to soraphen A
Acetyl-coenzyme A carboxylases (ACCs) is the first committed enzyme of fatty acid synthesis pathway. The inhibition of ACC is thought to be beneficial not only for diseases related to metabolism, such as type-2 diabetes, but also for infectious disease like bacterial infection disease. Soraphen A, a potent allosteric inhibitor of BC domain of yeast ACC, exhibit lower binding affinities to several yeast ACC mutants and the corresponding drug resistance mechanisms are still unknown. We report here a theoretical study of binding of soraphen A to wild type and yeast ACC mutants (including F510I, N485G, I69E, E477R, and K73R) via molecular dynamic simulation and molecular mechanics/generalized Born surface area free energy calculations methods. The calculated binding free energies of soraphen A to yeast ACC mutants are weaker than to wild type, which is highly consistent with the experimental results. The mutant F510I weakens the binding affinity of soraphen A to yeast ACC mainly by decreasing the van der Waals contributions, while the weaker binding affinities of Soraphen A to other yeast ACC mutants including N485G, I69E, E477R, and K73R are largely attributed to the decreased net electrostatic (ΔEele + ΔGGB) interactions. Our simulation results could provide important insights for the development of more potent ACC inhibitors.
KeywordsAcetyl-coenzyme A carboxylases Biotin carboxylase Soraphen A Drug resistance Molecular dynamics simulation
This study was funded by National Natural Science Foundation of China (NSFC No. 21708033), China Postdoctoral Science Foundation funded project (Grants No. 2017M611916), Natural Science Foundation of Jiangsu Province (Grants No. BK20171184), Scientific Research Foundation for Talented Scholars of Xuzhou Medical College (Grant No. D2014008), and Science and Technology project of Xuzhou (Grant No. KC16SG249).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Corominasfaja B, Cuyàs E, Gumuzio J, Boschbarrera J, Leis O, Martin ÁG, Menendez JA (2014) Chemical inhibition of acetyl-CoA carboxylase suppresses self-renewal growth of cancer stem cells. Oncotarget 5(18):8306–8316Google Scholar
- 3.Harriman G, Greenwood J, Bhat S, Huang X, Wang R, Paul D, Tong L, Saha AK, Westlin WF, Kapeller R (2016) Acetyl-CoA carboxylase inhibition by ND-630 reduces hepatic steatosis, improves insulin sensitivity, and modulates dyslipidemia in rats. Proc Natl Acad Sci USA 113(13):E1796CrossRefGoogle Scholar
- 5.Saggerson D (2008) Malonyl-CoA, a key signaling molecule in mammalian cells. Annu Rev Nutr 28:253–272. https://doi.org/10.1146/annurev.nutr.28.061807.155434 CrossRefGoogle Scholar
- 9.Miller JR, Dunham S, Mochalkin I, Banotai C, Bowman M, Buist S, Dunkle B, Hanna D, Harwood HJ, Huband MD, Karnovsky A, Kuhn M, Limberakis C, Liu JY, Mehrens S, Mueller WT, Narasimhan L, Ogden A, Ohren J, Prasad JV, Shelly JA, Skerlos L, Sulavik M, Thomas VH, VanderRoest S, Wang L, Wang Z, Whitton A, Zhu T, Stover CK (2009) A class of selective antibacterials derived from a protein kinase inhibitor pharmacophore. Proc Natl Acad Sci USA 106(6):1737–1742. https://doi.org/10.1073/pnas.0811275106 CrossRefGoogle Scholar
- 10.Mochalkin I, Miller JR, Narasimhan L, Thanabal V, Erdman P, Cox PB, Prasad JV, Lightle S, Huband MD, Stover CK (2009) Discovery of antibacterial biotin carboxylase inhibitors by virtual screening and fragment-based approaches. ACS Chem Biol 4(6):473–483. https://doi.org/10.1021/cb9000102 CrossRefGoogle Scholar
- 18.Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, Scalmani G, Barone V, Petersson GA, Nakatsuji H, Li X, Caricato M, Marenich A, Bloino J, Janesko BG, Gomperts R, Mennucci B, Hratchian HP, Ortiz JV, Izmaylov AF, Sonnenberg JL, Williams-Young D, Ding F, Lipparini F, Egidi F, Goings J, Peng B, Petrone A, Henderson T, Ranasinghe D, Zakrzewski VG, Gao J, Rega N, Zheng G, Liang W, Hada M, Ehara M, Toyota K, Fukuda R, Hasegawa J, Ishida M, Nakajima T, Honda Y, Kitao O, Nakai H, Vreven T, Throssell K, Montgomery JA Jr, Peralta JE, Ogliaro F, Bearpark M, Heyd JJ, Brothers E, Kudin KN, Staroverov VN, Keith T, Kobayashi R, Normand J, Raghavachari K, Rendell A, Burant JC, Iyengar SS, Tomasi J, Cossi M, Millam JM, Klene M, Adamo C, Cammi R, Ochterski JW, Martin RL, Morokuma K, Farkas O, Foresman JB, Fox DJ (2016) Gaussian 09 RA. Gaussian, Inc., WallingfordGoogle Scholar
- 20.Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T, Caldwell J, Wang J, Kollman P (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24(16):1999–2012. https://doi.org/10.1002/jcc.10349 CrossRefGoogle Scholar
- 25.Wang J, Hou T, Xu X (2006) Recent advances in free energy calculations with a combination of molecular mechanics and continuum models. Curr Comput 2(3):287–306Google Scholar
- 26.Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Lee T, Duan Y, Wang W, Donini O, Cieplak P, Srinivasan J, Case DA, Cheatham TE (2000) Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 33(12):889–897CrossRefGoogle Scholar
- 33.Delano WL (2002) The PyMOL molecular graphics system. DeLano Scientific, San CarlosGoogle Scholar