Computational Characterization of the Substrate Activation in the Active Site of SARS-CoV-2 Main Protease

Authors

  • Maria G. Khrenova Lomonosov Moscow State University
  • Vladimir G. Tsirelson
  • Alexander V. Nemukhin Lomonosov Moscow State University

DOI:

https://doi.org/10.14529/jsfi200304

Abstract

Molecular dynamics simulations with the QM(DFT)/MM potentials are utilized to discriminate between reactive and nonreactive complexes of the SARS-CoV-2 main protease and its substrates. Classification of frames along the molecular dynamic trajectories is utilized by analysis of the 2D maps of the Laplacian of electron density. Those are calculated in the plane formed by the carbonyl group of the substrate and a nucleophilic sulfur atom of the cysteine residue that initiates enzymatic reaction. Utilization of the GPU-based DFT code allows fast and accurate simulations with the hybrid functional PBE0 and double-zeta basis set. Exclusion of the polarization functions accelerates the calculations 2-fold, however this does not describe the substrate activation. Larger basis set with d-functions on heavy atoms and p-functions on hydrogen atoms enables to disclose equilibrium between the reactive and nonreactive species along the MD trajectory. The suggested approach can be utilized to choose covalent inhibitors that will readily interact with the catalytic residue of the selected enzyme.

References

Adamo, C., Barone, V.: Toward reliable density functional methods without adjustable parameters: The PBE0 model. The Journal of Chemical Physics 110(13), 6158 (1999), DOI: 10.1063/1.478522

Anisimov, V.M., Lamoureux, G., Vorobyov, I.V., et al.: Determination of electrostatic parameters for a polarizable force field based on the classical drude oscillator. Journal of Chemical Theory and Computation 1(1), 153–168 (2005), DOI: 10.1021/ct049930p

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, and side-chain 1 and 2 dihedral angles. Journal of Chemical Theory and Computation 8(9), 3257–3273 (2012), DOI: 10.1021/ct300400x

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), DOI: 10.1063/1.3382344

Jorgensen, W.L., Chandrasekhar, J., Madura, J.D., et al.: Comparison of simple potential functions for simulating liquid water. The Journal of Chemical Physics 79(2), 926–935 (1983), DOI: 10.1063/1.445869

Khrenova, M.G., Tsirelson, V.G., Nemukhin, A.V.: Dynamical properties of enzymesubstrate complexes disclose substrate specificity of the SARS-CoV-2 main protease as characterized by the electron density descriptors. Physical Chemistry Chemical Physics 22(34), 19069–19079 (2020), DOI: 10.1039/D0CP03560B

Lu, T., Chen, F.: Multiwfn: A multifunctional wavefunction analyzer. Journal of Computational Chemistry 33(5), 580–592 (2012), DOI: 10.1002/jcc.22885

Luehr, N., Ufimtsev, I.S., Martínez, T.J.: Dynamic precision for electron repulsion integral evaluation on graphical processing units (GPUs). Journal of Chemical Theory and Computation 7(4), 949–954 (2011), DOI: 10.1021/ct100701w

Manathunga, M., Miao, Y., Mu, D., et al.: Parallel implementation of density functional theory methods in the quantum interaction computational kernel program. Journal of Chemical Theory and Computation 16(6), 4315–4326 (2020), DOI: 10.1021/acs.jctc.0c00290

Melo, M.C.R., Bernardi, R.C., Rudack, T., et al.: NAMD goes quantum: An integrative suite for QM/MM simulations. Nature methods 15(5), 351 (2018), DOI: 10.1038/nmeth.4638

Miao, Y., Merz, K.M.: Acceleration of electron repulsion integral evaluation on graphics processing units via use of recurrence relations. Journal of Chemical Theory and Computation 9(2), 965–976 (2013), DOI: 10.1021/ct300754n

Miao, Y., Merz, K.M.: Acceleration of high angular momentum electron repulsion integrals and integral derivatives on graphics processing units. Journal of Chemical Theory and Computation 11(4), 1449–1462 (2015), DOI: 10.1021/ct500984t

Otto, H.H.: Cysteine proteases and their inhibitors. Chemical Reviews 97(1), 133–172 (1997), DOI: 10.1021/cr950025u

Perdew, J.P., Burke, K., Ernzerhof, M.: Generalized gradient approximation made simple. Physical Review Letters 77(18), 3865–3868 (1996), DOI: 10.1103/PhysRevLett.77.3865

Perdew, J.P., Burke, K., Ernzerhof, M.: Generalized gradient approximation made simple [Phys. Rev. Lett. 77, 3865 (1996)]. Physical Review Letters 78(7), 1396–1396 (1997), DOI: 10.1103/PhysRevLett.78.1396

Phillips, J.C., Braun, R., Wang, W., et al.: Scalable molecular dynamics with NAMD. Journal of Computational Chemistry 26(16), 1781–1802 (2005), DOI: 10.1002/jcc.20289

TeraChem v 1.9, PetaChem, LLC, www.petachem.com

Ufimtsev, I.S., Martínez, T.J.: Quantum chemistry on graphical processing units. 1. Strategies for two-electron integral evaluation. Journal of Chemical Theory and Computation 4(2), 222–231 (2008), DOI: 10.1021/ct700268q

Ufimtsev, I.S., Martínez, T.J.: Quantum chemistry on graphical processing units. 2. Direct self-consistent-field implementation. Journal of Chemical Theory and Computation 5(4), 1004–1015 (2009), DOI: 10.1021/ct800526s

Vanommeslaeghe, K., Hatcher, E., Acharya, C., et al.: CHARMM general force field (CGenFF): 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 (2010), DOI: 10.1002/jcc.21367

Vanommeslaeghe, K., MacKerell, A.D.: Automation of the CHARMM general force field (CGenFF) I: Bond perception and atom typing. Journal of Chemical Information and Modeling 52(12), 3144–3154 (2012), DOI: 10.1021/ci300363c

Vanommeslaeghe, K., Raman, E.P., MacKerell, A.D.: Automation of the CHARMM general force field (CGenFF) II: Assignment of bonded parameters and partial atomic charges. Journal of Chemical Information and Modeling 52(12), 3155–3168 (2012), DOI: 10.1021/ci3003649

Vasilevskaya, T., Khrenova, M.G., Nemukhin, A.V., Thiel, W.: Mechanism of proteolysis in matrix metalloproteinase-2 revealed by QM/MM modeling. Journal of Computational Chemistry 36(21), 1621–1630 (2015), DOI: 10.1002/jcc.23977

Vasilevskaya, T., Khrenova, M.G., Nemukhin, A.V., Thiel, W.: Methodological aspects of QM/MM calculations: A case study on matrix metalloproteinase-2. Journal of Computational Chemistry 37(19), 1801–1809 (2016), DOI: 10.1002/jcc.24395

Vasilevskaya, T., Khrenova, M.G., Nemukhin, A.V., Thiel, W.: Reaction mechanism of matrix metalloproteinases with a catalytically active zinc ion studied by the QM(DFTB)/MM simulations. Mendeleev Communications 26(3), 209–211 (2016), DOI: 10.1016/j.mencom.2016.05.010

Voevodin, 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), DOI: 10.14529/jsfi190201

Downloads

Published

2020-11-07

How to Cite

Khrenova, M. G., Tsirelson, V. G., & Nemukhin, A. V. (2020). Computational Characterization of the Substrate Activation in the Active Site of SARS-CoV-2 Main Protease. Supercomputing Frontiers and Innovations, 7(3). https://doi.org/10.14529/jsfi200304