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Mini-Reviews in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1389-5575
ISSN (Online): 1875-5607

Mini-Review Article

Hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) Simulation: A Tool for Structure-Based Drug Design and Discovery

Author(s): Prajakta U. Kulkarni, Harshil Shah and Vivek K. Vyas*

Volume 22, Issue 8, 2022

Published on: 11 January, 2022

Page: [1096 - 1107] Pages: 12

DOI: 10.2174/1389557521666211007115250

Price: $65

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Abstract

Quantum Mechanics (QM) is the physics-based theory that explains the physical properties of nature at the level of atoms and sub-atoms. Molecular mechanics (MM) construct molecular systems through the use of classical mechanics. So, when combined, hybrid quantum mechanics and molecular mechanics (QM/MM) can act as computer-based methods that can be used to calculate the structure and property data of molecular structures. Hybrid QM/MM combines the strengths of QM with accuracy and MM with speed. QM/MM simulation can also be applied for the study of chemical processes in solutions, as well as in the proteins, and has a great scope in structure-based drug design (SBDD) and discovery. Hybrid QM/MM can also be applied to HTS to derive QSAR models. Due to the availability of many protein crystal structures, it has a great role in computational chemistry, especially in structure- and fragment-based drug design. Fused QM/MM simulations have been developed as a widespread method to explore chemical reactions in condensed phases. In QM/MM simulations, the quantum chemistry theory is used to treat the space in which the chemical reactions occur; however, the rest is defined through the molecular mechanics force field (MMFF). In this review, we have extensively reviewed recent literature pertaining to the use and applications of hybrid QM/MM simulations for ligand and structure-based computational methods for the design and discovery of therapeutic agents.

Keywords: Quantum mechanics (QM), molecular mechanics (MM), hybrid QM/MM, structure-based drug design, MD simulations, CADD.

Graphical Abstract

[1]
Loco, D.; Protti, S.; Mennucci, B.; Mezzetti, A. Critical assessment of solvent effects on absorption and fluorescence of 3HF in acetonitrile in the QM/PCM framework: A Synergic Computational and Experimental Study. J. Mol. Struct., 2019, 1182, 283-291.
[http://dx.doi.org/10.1016/j.molstruc.2018.12.085]
[2]
Rathore, R.S.; Sumakanth, M.; Reddy, M.S.; Reddanna, P.; Rao, A.A.; Erion, M.D.; Reddy, M.R. Advances in binding free energies calculations: QM/MM-based free energy perturbation method for drug design. Curr. Pharm. Des., 2013, 19(26), 4674-4686.
[http://dx.doi.org/10.2174/1381612811319260002] [PMID: 23260025]
[3]
Kotev, M.; Sarrat, L.; Gonzalez, C.D. User-friendly quantum mechanics: Applications for Drug Discovery. Quantum Mechanics in Drug Discovery; Springer, 2020, pp. 231-255.
[http://dx.doi.org/10.1007/978-1-0716-0282-9_15]
[4]
Bowen, J.P.; Güner, O.F. A perspective on quantum mechanics calculations in ADMET predictions. Curr. Top. Med. Chem., 2013, 13(11), 1257-1272.
[http://dx.doi.org/10.2174/15680266113139990032] [PMID: 23675934]
[5]
Venkatesan, P.; Cerón, M.; Pérez-Gutiérrez, E.; Thamotharan, S.; Robles, F.; Ceballos, P.; Percino, M.J. Insights from QM/MM-ONIOM, PIXEL, NBO and DFT Calculations: The Molecular Conformational Origins for Optical Properties on (Z)-2-Phenyl-3-(4-(Pyridin-2-Yl)-Phenyl) Acrylonitrile Polymorphs. J. Mol. Struct., 2020, 1210, 128016.
[http://dx.doi.org/10.1016/j.molstruc.2020.128016]
[6]
Pang, J.; Gao, S.; Sun, Z.; Yang, G. Discovery of small molecule PLpro inhibitor against COVID-19 using structure-based virtual screening, molecular dynamics simulation, and molecular mechanics/Generalized Born surface area (MM/GBSA) calculation. Struct. Chem., 2020, 1-8.
[http://dx.doi.org/10.1007/s11224-020-01665-y] [PMID: 33106741]
[7]
Monticelli, L.; Salonen, E. Biomolecular Simulations: Methods and Protocols; Springer, 2013, Vol. 924, .
[http://dx.doi.org/10.1007/978-1-62703-017-5_8]
[8]
Senn, H.M.; Thiel, W. QM/MM methods for biomolecular systems. Angew. Chem. Int. Ed. Engl., 2009, 48(7), 1198-1229.
[http://dx.doi.org/10.1002/anie.200802019] [PMID: 19173328]
[9]
Alici, H.; Demir, K. Investigation of the Stability and the Helix-Tail Interaction of SCT and Its Various Charged Mutants Based on Comparative Molecular Dynamics Simulations. Chem. Phys., 2020., 111057.
[10]
Ma, S.; Vogt, K.A.; Petrillo, N.; Ruhoff, A.J. Characterizing the protonation states of the catalytic residues in apo and substrate-bound human T-cell leukemia virus type 1 protease. Comput. Biol. Chem., 2015, 56, 61-70.
[http://dx.doi.org/10.1016/j.compbiolchem.2015.04.002] [PMID: 25889320]
[11]
Feliciano, G.T.; da Silva, A.J.R. Unravelling the Reaction Mechanism of Matrix Metalloproteinase 3 Using QM/MM Calculations. J. Mol. Struct., 2015, 1091, 125-132.
[http://dx.doi.org/10.1016/j.molstruc.2015.02.079]
[12]
Chinnasamy, K.; Saravanan, M.; Poomani, K. Evaluation of binding and antagonism/downregulation of brilanestrant molecule in estrogen receptor-α via quantum mechanics/molecular mechanics, molecular dynamics and binding free energy calculations. J. Biomol. Struct. Dyn., 2020, 38(1), 219-235.
[http://dx.doi.org/10.1080/07391102.2019.1574605] [PMID: 31038398]
[13]
Al-Wahaibi, L.H.; Santhosh Kumar, N.; El-Emam, A.A.; Venkataramanan, N.S.; Ghabbour, H.A.; Al-Tamimi, A-M.S.; Percino, J.; Thamotharan, S. Investigation of Potential Anti-Malarial Lead Candidate 2-(4-Fluorobenzylthio)-5-(5-Bromothiophen-2-Yl)-1,3,4-Oxadiazole: Insights from Crystal Structure, DFT, QTAIM and Hybrid QM/MM Binding Energy Analysis. J. Mol. Struct., 2019, 1175, 230-240.
[http://dx.doi.org/10.1016/j.molstruc.2018.07.102]
[14]
Liu, J.; Zhai, Y.; Liang, L.; Zhu, D.; Zhao, Q.; Qiu, Y. Molecular modeling evaluation of the binding effect of five protease inhibitors to COVID-19 main protease. Chem. Phys., 2021, 542, 111080.
[http://dx.doi.org/10.1016/j.chemphys.2020.111080] [PMID: 33519023]
[15]
Zhou, T.; Huang, D.; Caflisch, A. Quantum mechanical methods for drug design. Curr. Top. Med. Chem., 2010, 10(1), 33-45.
[http://dx.doi.org/10.2174/156802610790232242] [PMID: 19929831]
[16]
Menikarachchi, L.C.; Gascón, J.A. QM/MM approaches in medicinal chemistry research. Curr. Top. Med. Chem., 2010, 10(1), 46-54.
[http://dx.doi.org/10.2174/156802610790232297] [PMID: 19929827]
[17]
Arodola, O.A.; Soliman, M.E.S. Quantum mechanics implementation in drug-design workflows: does it really help? Drug Des. Devel. Ther., 2017, 11, 2551-2564.
[http://dx.doi.org/10.2147/DDDT.S126344] [PMID: 28919707]
[18]
Dong, C.; Montes, M.; Al-Sawai, W.M. Xanthine Oxidoreductase Inhibition–A Review of Computational Aspect. J. Theor. Comput. Chem., 2020, 19, 2040008.
[http://dx.doi.org/10.1142/S0219633620400088]
[19]
De Luca, G.; Tocci, E.; Drioli, E. Quantum and Molecular Mechanics Calculations on Modified Silica Nano Ring. J. Mol. Struct., 2005, 739, 163-172.
[http://dx.doi.org/10.1016/j.molstruc.2004.05.042]
[20]
Yang, Z.; Mehmood, R.; Wang, M.; Qi, H.W.; Steeves, A.H.; Kulik, H.J. Revealing quantum mechanical effects in enzyme catalysis with large-scale electronic structure simulation. React. Chem. Eng., 2019, 4(2), 298-315.
[http://dx.doi.org/10.1039/C8RE00213D] [PMID: 31572618]
[21]
Zhang, Y-J.; Khorshidi, A.; Kastlunger, G.; Peterson, A.A. The potential for machine learning in hybrid QM/MM calculations. J. Chem. Phys., 2018, 148(24), 241740.
[http://dx.doi.org/10.1063/1.5029879] [PMID: 29960374]
[22]
Morawietz, T.; Artrith, N. Machine Learning-Accelerated Quantum Mechanics-Based Atomistic Simulations for Industrial Applications. J. Comput. Aided Mol. Des., 2020, 1-30.
[PMID: 33034008]
[23]
Shen, L.; Yang, W. Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks. J. Chem. Theory Comput., 2018, 14(3), 1442-1455.
[http://dx.doi.org/10.1021/acs.jctc.7b01195] [PMID: 29438614]
[24]
Haranczyk, M.; Gutowski, M. Combinatorial-computational-chemoinformatics (C3) approach to finding and analyzing low-energy tautomers. J. Comput. Aided Mol. Des., 2010, 24(6-7), 627-638.
[http://dx.doi.org/10.1007/s10822-010-9344-6] [PMID: 20361238]
[25]
Atkins, P.W.; Friedman, R.S. Molecular Quantum Mechanics; Oxford university press, 2011.
[26]
Aminpour, M.; Montemagno, C.; Tuszynski, J.A. An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications. Molecules, 2019, 24(9), 1693.
[http://dx.doi.org/10.3390/molecules24091693] [PMID: 31052253]
[27]
Szczypka, W.; Koleżyński, A. Molecular Mechanics Modelling of Amorphous Silicon Oxycarbide Clusters by Bottom-up Approach. J. Mol. Struct., 2020, 1208, 127930.
[http://dx.doi.org/10.1016/j.molstruc.2020.127930]
[28]
MacKerell, A.D., Jr; Wiorkiewicz-Kuczera, J.; Karplus, M. An All-Atom Empirical Energy Function for the Simulation of Nucleic Acids. J. Am. Chem. Soc., 1995, 117, 11946-11975.
[http://dx.doi.org/10.1021/ja00153a017]
[29]
Kaminski, G.; Jorgensen, W.L. Performance of the AMBER94, MMFF94, and OPLS-AA Force Fields for Modeling Organic Liquids. J. Phys. Chem., 1996, 100, 18010-18013.
[http://dx.doi.org/10.1021/jp9624257]
[30]
Halgren, T.A. Merck Molecular Force Field. I. Basis, Form, Scope, Parameterization, and Performance of MMFF94. J. Comput. Chem., 1996, 17, 490-519.
[http://dx.doi.org/10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P]
[31]
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? J. Comput. Chem., 2000, 21, 1049-1074.
[http://dx.doi.org/10.1002/1096-987X(200009)21:12<1049::AID-JCC3>3.0.CO;2-F]
[32]
Lifson, S.; Warshel, A. Consistent Force Field for Calculations of Conformations, Vibrational Spectra, and Enthalpies of Cycloalkane and N‐alkane Molecules. J. Chem. Phys., 1968, 49, 5116-5129.
[http://dx.doi.org/10.1063/1.1670007]
[33]
Herbert, J.M.; Head-Gordon, M. Accelerated, energy-conserving Born-Oppenheimer molecular dynamics via Fock matrix extrapolation. Phys. Chem. Chem. Phys., 2005, 7(18), 3269-3275.
[http://dx.doi.org/10.1039/b509494a] [PMID: 16240040]
[34]
Dunning, T.H. Jr Gaussian Basis Sets for Use in Correlated Molecular Calculations. I. The Atoms Boron through Neon and Hydrogen. J. Chem. Phys., 1989, 90, 1007-1023.
[http://dx.doi.org/10.1063/1.456153]
[35]
Coley, C.W.; Jin, W.; Rogers, L.; Jamison, T.F.; Jaakkola, T.S.; Green, W.H.; Barzilay, R.; Jensen, K.F. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. (Camb.), 2018, 10(2), 370-377.
[http://dx.doi.org/10.1039/C8SC04228D] [PMID: 30746086]
[36]
Gao, W.; Mahajan, S.P.; Sulam, J.; Gray, J.J. Deep Learning in Protein Structural Modeling and Design; Patterns, 2020, p. 100142.
[37]
Clements, R.J.; Womack, J.C.; Skylaris, C.-K. Electron Localisation Descriptors in ONETEP: A Tool for Interpreting Localisation and Bonding in Large-Scale DFT Calculations. Electron. Struct., 2020.
[38]
Prentice, J.C.A.; Aarons, J.; Womack, J.C.; Allen, A.E.A.; Andrinopoulos, L.; Anton, L.; Bell, R.A.; Bhandari, A.; Bramley, G.A.; Charlton, R.J.; Clements, R.J.; Cole, D.J.; Constantinescu, G.; Corsetti, F.; Dubois, S.M.; Duff, K.K.B.; Escartín, J.M.; Greco, A.; Hill, Q.; Lee, L.P.; Linscott, E.; O’Regan, D.D.; Phipps, M.J.S.; Ratcliff, L.E.; Serrano, Á.R.; Tait, E.W.; Teobaldi, G.; Vitale, V.; Yeung, N.; Zuehlsdorff, T.J.; Dziedzic, J.; Haynes, P.D.; Hine, N.D.M.; Mostofi, A.A.; Payne, M.C.; Skylaris, C.K. The ONETEP linear-scaling density functional theory program. J. Chem. Phys., 2020, 152(17), 174111.
[http://dx.doi.org/10.1063/5.0004445] [PMID: 32384832]
[39]
Khaliullin, R.Z.; VandeVondele, J.; Hutter, J. Efficient Linear-Scaling Density Functional Theory for Molecular Systems. J. Chem. Theory Comput., 2013, 9(10), 4421-4427.
[http://dx.doi.org/10.1021/ct400595k] [PMID: 26589159]
[40]
Zhang, B.; Ma, Y.; Jin, X.; Wang, Y.; Suo, B.; He, X.; Jin, Z. GridMol2. 0: Implementation and Application of Linear‐scale Quantum Mechanics Methods and Molecular Visualization. Int. J. Quantum Chem., 2020., e26402.
[http://dx.doi.org/10.1002/qua.26402]
[41]
Dziedzic, J.; Bhandari, A.; Anton, L.; Peng, C.; Womack, J.C.; Famili, M.; Kramer, D.; Skylaris, C-K. Practical Approach to Large-Scale Electronic Structure Calculations in Electrolyte Solutions via Continuum-Embedded Linear-Scaling Density Functional Theory. J. Phys. Chem. C, 2020, 124, 7860-7872.
[http://dx.doi.org/10.1021/acs.jpcc.0c00762]
[42]
Kříž, K.; Řezáč, J. Benchmarking of Semiempirical Quantum-Mechanical Methods on Systems Relevant to Computer-Aided Drug Design. J. Chem. Inf. Model., 2020, 60(3), 1453-1460.
[http://dx.doi.org/10.1021/acs.jcim.9b01171] [PMID: 32062970]
[43]
van der Kamp, M.W.; Mulholland, A.J. Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology. Biochemistry, 2013, 52(16), 2708-2728.
[http://dx.doi.org/10.1021/bi400215w] [PMID: 23557014]
[44]
Munni, Y.A.; Ali, M.C.; Selsi, N.J.; Sultana, M.; Hossen, M.; Bipasha, T.H.; Rahman, M.; Uddin, M.N.; Hosen, S.M.Z.; Dash, R. Molecular simulation studies to reveal the binding mechanisms of shikonin derivatives inhibiting VEGFR-2 kinase. Comput. Biol. Chem., 2021, 90, 107414.
[http://dx.doi.org/10.1016/j.compbiolchem.2020.107414] [PMID: 33191109]
[45]
Arafet, K.; González, F.V.; Moliner, V. Quantum Mechanics/Molecular Mechanics Studies of the Mechanism of Cysteine Proteases Inhibition by Dipeptidyl Nitroalkenes. Chemistry, 2020, 26(9), 2002-2012.
[http://dx.doi.org/10.1002/chem.201904513] [PMID: 31692123]
[46]
Joel, I.Y.; Adigun, T.O.; Bankole, O.O.; Iduze, M.A. AbelJack-Soala, T.; ANI, O.G.; Olapade, E.O.; Dada, F.M.; Adetiwa, O.M.; Ofeniforo, B.E.; Akanni, F.O. Insights into Features and Lead Optimization of Novel Type 11/2 Inhibitors of P38α Mitogen-Activated Protein Kinase Using QSAR, Quantum Mechanics, Bioisostere Replacement and ADMET Studies. Results Chem., 2020, 2, 100044.
[http://dx.doi.org/10.1016/j.rechem.2020.100044]
[47]
Mitra, S.; Dash, R. Structural dynamics and quantum mechanical aspects of shikonin derivatives as CREBBP bromodomain inhibitors. J. Mol. Graph. Model., 2018, 83, 42-52.
[http://dx.doi.org/10.1016/j.jmgm.2018.04.014] [PMID: 29758466]
[48]
Hylsová, M.; Carbain, B.; Fanfrlík, J.; Musilová, L.; Haldar, S.; Köprülüoğlu, C.; Ajani, H.; Brahmkshatriya, P.S.; Jorda, R.; Kryštof, V.; Hobza, P.; Echalier, A.; Paruch, K.; Lepšík, M. Explicit treatment of active-site waters enhances quantum mechanical/implicit solvent scoring: Inhibition of CDK2 by new pyrazolo[1,5-a]pyrimidines. Eur. J. Med. Chem., 2017, 126, 1118-1128.
[http://dx.doi.org/10.1016/j.ejmech.2016.12.023] [PMID: 28039837]
[49]
Zou, Y.; Wang, F.; Wang, Y.; Guo, W.; Zhang, Y.; Xu, Q.; Lai, Y. Systematic study of imidazoles inhibiting IDO1 via the integration of molecular mechanics and quantum mechanics calculations. Eur. J. Med. Chem., 2017, 131, 152-170.
[http://dx.doi.org/10.1016/j.ejmech.2017.03.021] [PMID: 28319781]
[50]
Kaviani, S.; Izadyar, M.; Khavani, M.; Housaindokht, M.R. A Combined Molecular Dynamics and Quantum Mechanics Study on the Interaction of Fe3+ and Human Serum Albumin Relevant to Iron Overload Disease. J. Mol. Liq., 2020, 317, 113933.
[http://dx.doi.org/10.1016/j.molliq.2020.113933]
[51]
Arafet, K.; Ferrer, S.; Moliner, V. First quantum mechanics/molecular mechanics studies of the inhibition mechanism of cruzain by peptidyl halomethyl ketones. Biochemistry, 2015, 54(21), 3381-3391.
[http://dx.doi.org/10.1021/bi501551g] [PMID: 25965914]
[52]
Reddy, M.R.; Reddy, C.R.; Rathore, R.S.; Erion, M.D.; Aparoy, P.; Reddy, R.N.; Reddanna, P. Free energy calculations to estimate ligand-binding affinities in structure-based drug design. Curr. Pharm. Des., 2014, 20(20), 3323-3337.
[http://dx.doi.org/10.2174/13816128113199990604] [PMID: 23947646]
[53]
Zhu, K.; Lu, J.; Liang, Z.; Kong, X.; Ye, F.; Jin, L.; Geng, H.; Chen, Y.; Zheng, M.; Jiang, H.; Li, J.Q.; Luo, C. A quantum mechanics/molecular mechanics study on the hydrolysis mechanism of New Delhi metallo-β-lactamase-1. J. Comput. Aided Mol. Des., 2013, 27(3), 247-256.
[http://dx.doi.org/10.1007/s10822-012-9630-6] [PMID: 23456591]
[54]
Wichapong, K.; Rohe, A.; Platzer, C.; Slynko, I.; Erdmann, F.; Schmidt, M.; Sippl, W. Application of docking and QM/MM-GBSA rescoring to screen for novel Myt1 kinase inhibitors. J. Chem. Inf. Model., 2014, 54(3), 881-893.
[http://dx.doi.org/10.1021/ci4007326] [PMID: 24490903]
[55]
Burger, S.K.; Thompson, D.C.; Ayers, P.W. Quantum mechanics/molecular mechanics strategies for docking pose refinement: distinguishing between binders and decoys in cytochrome C peroxidase. J. Chem. Inf. Model., 2011, 51(1), 93-101.
[http://dx.doi.org/10.1021/ci100329z] [PMID: 21133348]
[56]
Pitarch, J.; Pascual-Ahuir, J-L. A Quantum Mechanics/Molecular Mechanics Study of the Acylation Reaction of TEM1 β-Lactamase and Penicillanate. J. Chem. Soc., Perkin Trans. 2, 2000, 761-767.
[http://dx.doi.org/10.1039/a908264f]
[57]
Lonsdale, R.; Fort, R.M.; Rydberg, P.; Harvey, J.N.; Mulholland, A.J. Quantum Mechanics/Molecular Mechanics Modeling of Drug Metabolism: Mexiletine N-Hydroxylation by Cytochrome P450 1A2. Chem. Res. Toxicol., 2016, 29(6), 963-971.
[http://dx.doi.org/10.1021/acs.chemrestox.5b00514] [PMID: 27064685]
[58]
Rabi, S.; Patel, A.H.G.; Burger, S.K.; Verstraelen, T.; Ayers, P.W. Exploring the Substrate Selectivity of Human SEH and M. Tuberculosis EHB Using QM/MM. Struct. Chem., 2017, 28, 1501-1511.
[http://dx.doi.org/10.1007/s11224-017-0982-3]
[59]
Jafari, S.; Ryde, U.; Fouda, A.E.A.; Alavi, F.S.; Dong, G.; Irani, M. Quantum Mechanics/Molecular Mechanics Study of the Reaction Mechanism of Glyoxalase I. Inorg. Chem., 2020, 59(4), 2594-2603.
[http://dx.doi.org/10.1021/acs.inorgchem.9b03621] [PMID: 32011880]
[60]
Elsässer, B.; Zauner, F.B.; Messner, J.; Soh, W.T.; Dall, E.; Brandstetter, H. Distinct Roles of Catalytic Cysteine and Histidine in the Protease and Ligase Mechanisms of Human Legumain As Revealed by DFT-Based QM/MM Simulations. ACS Catal., 2017, 7(9), 5585-5593.
[http://dx.doi.org/10.1021/acscatal.7b01505] [PMID: 28932620]
[61]
Jongkon, N.; Chotpatiwetchkul, W.; Gleeson, M.P. Probing the Catalytic Mechanism Involved in the Isocitrate Lyase Superfamily: Hybrid Quantum Mechanical/Molecular Mechanical Calculations on 2,3-Dimethylmalate Lyase. J. Phys. Chem. B, 2015, 119(35), 11473-11484.
[http://dx.doi.org/10.1021/acs.jpcb.5b04732] [PMID: 26224328]
[62]
Sgrignani, J.; Grazioso, G.; De Amici, M.; Colombo, G. Inactivation of TEM-1 by avibactam (NXL-104): insights from quantum mechanics/molecular mechanics metadynamics simulations. Biochemistry, 2014, 53(31), 5174-5185.
[http://dx.doi.org/10.1021/bi500589x] [PMID: 25050826]
[63]
Lodola, A.; Capoferri, L.; Rivara, S.; Tarzia, G.; Piomelli, D.; Mulholland, A.; Mor, M. Quantum mechanics/molecular mechanics modeling of fatty acid amide hydrolase reactivation distinguishes substrate from irreversible covalent inhibitors. J. Med. Chem., 2013, 56(6), 2500-2512.
[http://dx.doi.org/10.1021/jm301867x] [PMID: 23425199]
[64]
Christov, C.Z.; Lodola, A.; Karabencheva-Christova, T.G.; Wan, S.; Coveney, P.V.; Mulholland, A.J. Conformational effects on the pro-S hydrogen abstraction reaction in cyclooxygenase-1: An integrated QM/MM and MD study. Biophys. J., 2013, 104(5), L5-L7.
[http://dx.doi.org/10.1016/j.bpj.2013.01.040] [PMID: 23473504]
[65]
Nutho, B.; Mulholland, A.J.; Rungrotmongkol, T. Quantum Mechanics/Molecular Mechanics (QM/MM) Calculations Support a Concerted Reaction Mechanism for the Zika Virus NS2B/NS3 Serine Protease with Its Substrate. J. Phys. Chem. B, 2019, 123(13), 2889-2903.
[http://dx.doi.org/10.1021/acs.jpcb.9b02157] [PMID: 30845796]
[66]
Jayasheela, K.; Nagabalasubramanian, P.B.; Periandy, S. Conformational & spectroscopic characterization, charge analysis and molecular docking profiles of chromone-3-carboxylic acid using a quantum hybrid computational method. Heliyon, 2020, 6(10), e04775.
[http://dx.doi.org/10.1016/j.heliyon.2020.e04775] [PMID: 33083580]
[67]
Pasala, C.; Katari, S.K.; Nalamolu, R.M.; Aparna, R.B.; Amineni, U. Integration of core hopping, quantum-mechanics, molecular mechanics coupled binding-energy estimations and dynamic simulations for fragment-based novel therapeutic scaffolds against Helicobacter pylori strains. Comput. Biol. Chem., 2019, 83, 107126.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.107126] [PMID: 31557645]
[68]
Devi, R.N.; Khrenova, M.G.; Israel, S.; Anzline, C.; Astakhov, A.A.; Tsirelson, V.G. Testing the ability of rhodanine and 2, 4-thiazolidinedione to interact with the human pancreatic alpha-amylase: electron-density descriptors complement molecular docking, QM, and QM/MM dynamics calculations. J. Mol. Model., 2017, 23(9), 252.
[http://dx.doi.org/10.1007/s00894-017-3418-5] [PMID: 28780749]
[69]
Al-Otaibi, J.S.; Almuqrin, A.H.; Mary, Y.S.; Thomas, R. Modeling the Conformational Preference, Spectroscopic Properties, UV Light Harvesting Efficiency, Biological Receptor Inhibitory Ability and Other Physico-Chemical Properties of Five Imidazole Derivatives Using Quantum Mechanical and Molecular Mechanics T. J. Mol. Liq., 2020., 112871.
[http://dx.doi.org/10.1016/j.molliq.2020.112871]
[70]
McClory, J.; Timson, D.J.; Singh, W.; Zhang, J.; Huang, M. Reaction Mechanism of Isopentenyl Phosphate Kinase: A QM/MM Study. J. Phys. Chem. B, 2017, 121(49), 11062-11071.
[http://dx.doi.org/10.1021/acs.jpcb.7b08770] [PMID: 29155589]
[71]
Mu, X.; Zhang, C.; Xu, D. QM/MM investigation of the catalytic mechanism of angiotensin-converting enzyme. J. Mol. Model., 2016, 22(6), 132.
[http://dx.doi.org/10.1007/s00894-016-3004-2] [PMID: 27184002]
[72]
Vepuri, S.B.; Devarajegowda, H.C.; Soliman, M.E. Synthesis, Characterization and Molecular Modelling of a Novel Dipyridamole Supramolecule - X-Ray Structure, Quantum Mechanics and Molecular Dynamics Study to Comprehend the Hydrogen Bond Structure-Activity Relationship. J. Mol. Struct., 2016, 1105, 194-204.
[http://dx.doi.org/10.1016/j.molstruc.2015.10.050]
[73]
Sellers, B.D.; James, N.C.; Gobbi, A. A Comparison of Quantum and Molecular Mechanical Methods to Estimate Strain Energy in Druglike Fragments. J. Chem. Inf. Model., 2017, 57(6), 1265-1275.
[http://dx.doi.org/10.1021/acs.jcim.6b00614] [PMID: 28485585]
[74]
Avgy-David, H.H.; Senderowitz, H. Toward Focusing Conformational Ensembles on Bioactive Conformations: A Molecular Mechanics/Quantum Mechanics Study. J. Chem. Inf. Model., 2015, 55(10), 2154-2167.
[http://dx.doi.org/10.1021/acs.jcim.5b00259] [PMID: 26406154]
[75]
Lu, J.; Zhang, Z.; Ni, Z.; Shen, H.; Tu, Z.; Liu, H.; Lu, R. QM/MM-PB/SA scoring of the interaction strength between Akt kinase and apigenin analogues. Comput. Biol. Chem., 2014, 52, 25-33.
[http://dx.doi.org/10.1016/j.compbiolchem.2014.07.002] [PMID: 25179857]
[76]
Kordzadeh, A.; Amjad-Iranagh, S.; Zarif, M.; Modarress, H. Adsorption and encapsulation of the drug doxorubicin on covalent functionalized carbon nanotubes: A scrutinized study by using molecular dynamics simulation and quantum mechanics calculation. J. Mol. Graph. Model., 2019, 88, 11-22.
[http://dx.doi.org/10.1016/j.jmgm.2018.12.009] [PMID: 30616088]
[77]
Liu, J.Q.; Li, X.F.; Gu, C.Y.; da Silva, J.C.S.; Barros, A.L.; Alves, S., Jr; Li, B.H.; Ren, F.; Batten, S.R.; Soares, T.A. A combined experimental and computational study of novel nanocage-based metal-organic frameworks for drug delivery. Dalton Trans., 2015, 44(44), 19370-19382.
[http://dx.doi.org/10.1039/C5DT02171E] [PMID: 26501793]
[78]
Li, F.; Li, B.; Wang, C.; Zeng, Y.; Liu, J.; Gu, C.Y.; Lu, P.; Mei, L. Encapsulation of Pharmaceutical Ingredient Linker in Metal-Organic Framework: Combined Experimental and Theoretical Insight into the Drug Delivery. RSC Advances, 2016, 6, 47959-47965.
[http://dx.doi.org/10.1039/C6RA06178H]
[79]
Ma, D.Y.; Li, Z.; Xiao, J.X.; Deng, R.; Lin, P.F.; Chen, R.Q.; Liang, Y.Q.; Guo, H.F.; Liu, B.; Liu, J.Q. Hydrostable and Nitryl/Methyl-Functionalized Metal-Organic Framework for Drug Delivery and Highly Selective CO2 Adsorption. Inorg. Chem., 2015, 54(14), 6719-6726.
[http://dx.doi.org/10.1021/acs.inorgchem.5b00335] [PMID: 26146847]
[80]
Li, J.; Wu, G.; Fu, Q.; Ge, H.; Liu, S.; Li, X.; Cheng, B. Exploring the influence of conserved lysine69 on the catalytic activity of the helicobacter pylori shikimate dehydrogenase: A combined QM/MM and MD simulations. Comput. Biol. Chem., 2019, 83, 107098.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.107098] [PMID: 31421413]
[81]
Rasool, N.; Iftikhar, S.; Amir, A.; Hussain, W. Structural and quantum mechanical computations to elucidate the altered binding mechanism of metal and drug with pyrazinamidase from Mycobacterium tuberculosis due to mutagenicity. J. Mol. Graph. Model., 2018, 80, 126-131.
[http://dx.doi.org/10.1016/j.jmgm.2017.12.011] [PMID: 29331879]
[82]
Don, C.G.; Smies, M. Deciphering Reaction Determinants of Altered-Activity CYP2D6 Variants by Well-Tempered Metadynamics Simulation and QM/MM Calculations ̌., 2020.
[83]
Ryazantsev, M.N.; Nikolaev, D.M.; Struts, A.V.; Brown, M.F. Quantum Mechanical and Molecular Mechanics Modeling of Membrane-Embedded Rhodopsins. J. Membr. Biol., 2019, 252(4-5), 425-449.
[http://dx.doi.org/10.1007/s00232-019-00095-0] [PMID: 31570961]
[84]
Ainsley, J.; Lodola, A.; Mulholland, A.J.; Christov, C.Z.; Karabencheva-Christova, T.G. Combined Quantum Mechanics and Molecular Mechanics Studies of Enzymatic Reaction Mechanisms.Advances in protein chemistry and structural biology; Elsevier, 2018, Vol. 113, pp. 1-32.
[85]
Tvaroška, I. Atomistic insight into the catalytic mechanism of glycosyltransferases by combined quantum mechanics/molecular mechanics (QM/MM) methods. Carbohydr. Res., 2015, 403, 38-47.
[http://dx.doi.org/10.1016/j.carres.2014.06.017] [PMID: 25060837]
[86]
Duarte, F.; Amrein, B.A.; Blaha-Nelson, D.; Kamerlin, S.C.L. Recent advances in QM/MM free energy calculations using reference potentials. Biochim. Biophys. Acta, 2015, 1850(5), 954-965.
[http://dx.doi.org/10.1016/j.bbagen.2014.07.008] [PMID: 25038480]
[87]
Rychkova, A.; Warshel, A. Exploring the nature of the translocon-assisted protein insertion. Proc. Natl. Acad. Sci. USA, 2013, 110(2), 495-500.
[http://dx.doi.org/10.1073/pnas.1220361110] [PMID: 23269832]
[88]
Mukherjee, S.; Warshel, A. Electrostatic origin of the mechanochemical rotary mechanism and the catalytic dwell of F1-ATPase. Proc. Natl. Acad. Sci. USA, 2011, 108(51), 20550-20555.
[http://dx.doi.org/10.1073/pnas.1117024108] [PMID: 22143769]
[89]
Czub, J.; Grubmüller, H. Torsional elasticity and energetics of F1-ATPase. Proc. Natl. Acad. Sci. USA, 2011, 108(18), 7408-7413.
[http://dx.doi.org/10.1073/pnas.1018686108] [PMID: 21502534]
[90]
Várnai, C.; Bernstein, N.; Mones, L.; Csányi, G. Tests of an adaptive QM/MM calculation on free energy profiles of chemical reactions in solution. J. Phys. Chem. B, 2013, 117(40), 12202-12211.
[http://dx.doi.org/10.1021/jp405974b] [PMID: 24033146]
[91]
Park, K.; Götz, A.W.; Walker, R.C.; Paesani, F. Application of Adaptive QM/MM Methods to Molecular Dynamics Simulations of Aqueous Systems. J. Chem. Theory Comput., 2012, 8(8), 2868-2877.
[http://dx.doi.org/10.1021/ct300331f] [PMID: 26592126]
[92]
Heyden, A.; Lin, H.; Truhlar, D.G. Adaptive partitioning in combined quantum mechanical and molecular mechanical calculations of potential energy functions for multiscale simulations. J. Phys. Chem. B, 2007, 111(9), 2231-2241.
[http://dx.doi.org/10.1021/jp0673617] [PMID: 17288477]
[93]
Bulo, R.E.; Ensing, B.; Sikkema, J.; Visscher, L. Toward a Practical Method for Adaptive QM/MM Simulations. J. Chem. Theory Comput., 2009, 5(9), 2212-2221.
[http://dx.doi.org/10.1021/ct900148e] [PMID: 26616607]
[94]
Pezeshki, S.; Lin, H. Adaptive-Partitioning Redistributed Charge and Dipole Schemes for QM/MM Dynamics Simulations: On-the-fly Relocation of Boundaries that Pass through Covalent Bonds. J. Chem. Theory Comput., 2011, 7(11), 3625-3634.
[http://dx.doi.org/10.1021/ct2005209] [PMID: 26598259]
[95]
Velmurugan, D.; Pachaiappan, R.; Ramakrishnan, C. Recent Trends in Drug Design and Discovery. Curr. Top. Med. Chem., 2020, 20(19), 1761-1770.
[http://dx.doi.org/10.2174/1568026620666200622150003] [PMID: 32568020]
[96]
Brogi, S.; Ramalho, T.C.; Kuca, K.; Medina-Franco, J.L.; Valko, M. Editorial: In silico Methods for Drug Design and Discovery. Front Chem., 2020, 8, 612.
[http://dx.doi.org/10.3389/fchem.2020.00612] [PMID: 32850641]
[97]
Aucar, M.G.; Cavasotto, C.N. Molecular Docking Using Quantum Mechanical-Based Methods.Quantum Mechanics in Drug Discovery; Springer, 2020, pp. 269-284.
[http://dx.doi.org/10.1007/978-1-0716-0282-9_17]
[98]
Palermo, G.; Spinello, A.; Saha, A.; Magistrato, A. Frontiers of Metal-Coordinating Drug Design. Expert Opin. Drug Discov., 2020, 1-15.
[PMID: 33874825]
[99]
Tkatchenko, A. Machine learning for chemical discovery. Nat. Commun., 2020, 11(1), 4125.
[http://dx.doi.org/10.1038/s41467-020-17844-8] [PMID: 32807794]

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