Computational Modeling of the Interaction of Molecular Oxygen with the Flavin-dependent Enzyme RutA




computational modeling, molecular dynamics, quantum mechanics/molecular mechanics, protein-oxygen interaction, flavin-dependent enzymes


Supercomputer molecular modeling methods are applied to characterize structure and dynamics of the flavin-dependent enzyme RutA in the complex with molecular oxygen. Following construction of a model protein system, molecular dynamics (MD) simulations were carried out using either classical force field interaction potentials or the quantum mechanics/molecular mechanics (QM/MM) potentials. Several oxygen-binding pockets in the protein cavities were located in these simulations. The QM/MM-based MD calculations rely on the interface between the quantum chemistry package TeraChem and the MD package NAMD. The results show a stable localization of the oxygen molecule in the enzyme active site. Static QM/MM calculations carried out with two different packages, NWChem and TURBOMOLE, allowed us to establish the structure of the RutA-O2 complex. Biochemical perspectives of the hallmark reaction of incorporating oxygen into organic compounds emerged from these simulations are formulated.


Adak, S., Begley, T.P.: RutA-Catalyzed Oxidative Cleavage of the Uracil Amide Involves Formation of a Flavin-N5-oxide. Biochemistry 56(29), 3708–3709 (2017). 10.1021/acs.biochem.7b00493

Adamo, C., Barone, V.: Toward reliable density functional methods without adjustable parameters: The PBE0 model. The Journal of Chemical Physics 110(13), 6158–6170 (1999).

Aleksandrov, A.: A molecular mechanics model for flavins. Journal of Computational Chemistry 40(32), 2834–2842 (2019).

Aprà, E., Bylaska, E.J., de Jong, W.A., et al.: NWChem: Past, present, and future. The Journal of Chemical Physics 152(18), 184102 (2020).

Auhim, H.S., Grigorenko, B.L., Harris, T.K., et al.: Stalling chromophore synthesis of the fluorescent protein venus reveals the molecular basis of the final oxidation step. Chem. Sci. 12, 7735–7745 (2021).

Balasubramani, S.G., Chen, G.P., Coriani, S., et al.: Turbomole: Modular program suite for ab initio quantum-chemical and condensed-matter simulations. The Journal of Chemical Physics 152(18), 184107 (2020).

Berman, H.M., Westbrook, J., Feng, Z., et al.: The Protein Data Bank. Nucleic Acids Research 28(1), 235–242 (2000).

Best, R.B., Zhu, X., Shim, J., et al.: Optimization of the additive charmm all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain ξ1 and ξ2 dihedral angles. Journal of Chemical Theory and Computation 8(9), 3257–3273 (2012).

Chai, J.D., Head-Gordon, M.: Long-range corrected hybrid density functionals with damped atomatom dispersion corrections. Phys. Chem. Chem. Phys. 10, 6615–6620 (2008). https: //

Colloc'h, N., Gabison, L., Monard, G., et al.: Oxygen Pressurized X-Ray Crystallography: Probing the Dioxygen Binding Site in Cofactorless Urate Oxidase and Implications for Its Catalytic Mechanism. Biophysical Journal 95(5), 2415–2422 (2008).

Giudetti, G., Polyakov, I., Grigorenko, B.L., et al.: How Reproducible are QM/MM Simulations? Lessons from Computational Studies of the Covalent Inhibition of the SARS-CoV-2 Main Protease by Carmofur. ChemRxiv (2022).

Grimme, S., Antony, J., Ehrlich, S., Krieg, H.: A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. The Journal of Chemical Physics 132(15), 154104 (2010).

Humphrey, W., Dalke, A., Schulten, K.: VMD: Visual molecular dynamics. Journal of Molecular Graphics 14(1), 33–38 (1996).

Khrenova, M.G., Polyakov, I.V., Nemukhin, A.V.: Molecular dynamics of enzyme-substrate complexes in guanosine-binding proteins. Khimicheskaya Fizika 41(6), 66–72 (2022).

Kstner, J., Carr, J.M., Keal, T.W., et al.: DL-FIND: An Open-Source Geometry Optimizer for Atomistic Simulations. The Journal of Physical Chemistry A 113(43), 11856–11865 (2009).

Loh, K.D., Gyaneshwar, P., Papadimitriou, E.M., et al.: A previously undescribed pathway for pyrimidine catabolism. Proceedings of the National Academy of Sciences 103(13), 5114–5119 (2006).

Massey, V.: Activation of molecular oxygen by flavins and flavoproteins. Journal of Biological Chemistry 269(36), 22459–22462 (1994). 31664-2

Matthews, A., Saleem-Batcha, R., Sanders, J.N., et. al: Aminoperoxide adducts expand the catalytic repertoire of flavin monooxygenases. Nature Chemical Biology 16(5), 556–563 (2020).

Melo, M.C.R., Bernardi, R.C., Rudack, T., et al.: NAMD goes quantum: an integrative suite for hybrid simulations. Nature Methods 15(5), 351–354 (2018).

Nemukhin, A.V., Grigorenko, B.L., Khrenova, M.G., Krylov, A.I.: Computational challenges in modeling of representative bioimaging proteins: GFP-like proteins, flavoproteins, and phytochromes. The Journal of Physical Chemistry B 123(29), 6133–6149 (2019).

Nemukhin, A.V., Grigorenko, B.L., Polyakov, I.V., Lushchekina, S.V.: Computational modeling of the SARS-CoV-2 main protease inhibition by the covalent binding of prospective drug molecules. Supercomputing Frontiers and Innovations 7(3), 25–32 (2020).

Phillips, J.C., Hardy, D.J., Maia, J.D.C., et al.: Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of Chemical Physics 153(4), 044130 (2020).

Seritan, S., Bannwarth, C., Fales, B.S., et al.: Terachem: A graphical processing unitaccelerated electronic structure package for large-scale ab initio molecular dynamics. WIREs Computational Molecular Science 11(2), e1494 (2021). 1494

Shabalin, I.G., Porebski, P.J., Minor, W.: Refining the macromolecular model – achieving the best agreement with the data from X-ray diffraction experiment. Crystallography Reviews 24(4), 236–262 (2018).

Sherwood, P., de Vries, A.H., Guest, M.F., et al.: QUASI: A general purpose implementation of the QM/MM approach and its application to problems in catalysis. Journal of Molecular Structure: THEOCHEM 632(1), 1–28 (2003).

Teufel, R.: Flavin-catalyzed redox tailoring reactions in natural product biosynthesis. Archives of Biochemistry and Biophysics 632, 20–27 (2017).

Voevodin, V.V., Antonov, A.S., Nikitenko, D.A., et al.: Supercomputer Lomonosov-2: Large scale, deep monitoring and fine analytics for the user community. Supercomputing Frontiers and Innovations 6(2), 4–11 (2019).

Wang, J., Cieplak, P., Kollman, P.A.: How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? Journal of Computational Chemistry 21(12), 1049–1074 (2000).<1049::AID-JCC3>3.0.CO;2-F

Wang, S., Hou, K., Heinz, H.: Accurate and compatible force fields for molecular oxygen, nitrogen, and hydrogen to simulate gases, electrolytes, and heterogeneous interfaces. Journal of Chemical Theory and Computation 17(8), 5198–5213 (2021).




How to Cite

Polyakov, I. V., Domratcheva, T. M., Kulakova, A. M., Nemukhin, A. V., & Grigorenko, B. L. (2022). Computational Modeling of the Interaction of Molecular Oxygen with the Flavin-dependent Enzyme RutA. Supercomputing Frontiers and Innovations, 9(2), 46–55.

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