QM/MM Best Practice Workshop webinar: Towards chemical accuracy in QM/MM modelling of enzyme catalytic mechanisms and protein-ligand binding (2020-12-14)

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This is the fifth webinar in the BioExcel Virtual Workshop on Best Practices in QM/MM Simulation of Biomolecular Systems.

Title: Towards chemical accuracy in QM/MM modelling of enzyme catalytic mechanisms and protein-ligand binding

Speaker: Prof. Adrian Mulholland

Monday 14 December, 2020
15:00 CET



This presentation will cover practical aspects of combined quantum mechanics/molecular mechanics (QM/MM) calculations and their application to biomolecular systems [1,2,42,49,50].

QM/MM methods are now well established in computational biochemistry and enzymology [1,2]. Early applications included reactions in enzymes [3-10] and DNA [11]. QM/MM barriers were found to correlate with experimental rate constants for alternative substrates in para-hydroxybenzoate hydroxylase [6] and phenol hydroxylase [7], showing the QM/MM approach to be predictive for modelling enzyme catalysed reactions. QM/MM methods have demonstrated their value in revealing mechanisms of enzyme catalysis [1-10, 12-14], predicting reactivity of covalent inhibitors [15-17]; analysing effects of conformation [13, 13, 18-21], dynamics [22] and quantum tunnelling [23,24] in catalysis ; identifying novel catalytic interactions [6,7,25] analysing determinants of specificity in drug metabolism [26-29] and causes of drug resistance [30].

QM/MM simulations can be used as computational ‘assays’ of enzyme activity [31], e.g. distinguishing between beta-lactamases that can effectively hydrolyse carbapenem antibiotics from those that cannot [32]. QM/MM simulations also reproduce their susceptibility to inhibitors such as clavulanate [33]. QM/MM of Class D beta-lactamases reveal the molecular basis of differences in activity against cephalosporin antibiotics, showing that subtle changes in the active site account for experimentally observed differences in activity between OXA-48 and OXA-163 enzymes [34]. Recent QM/MM applications include modelling mechanisms of covalent inhibition of the SARS-CoV-2 main protease and suggesting modifications to tune reversibility [35].

High level ab initio quantum chemical methods can be applied in QM/MM calculations and can give barriers to enzyme-catalysed reactions with ‘chemical accuracy’ (~1kcal/mol, 4 kJ/mol) [36-39]. At this level of accuracy, reliable predictions can be made about the mechanisms of enzyme-catalysed reactions [40]. The excellent agreement with experiment shows the applicability of transition state theory for enzyme-catalysed reactions [40].

Projector-based embedding provides a practical approach to high level ab initio QM/MM calculations, rigorously embedding an ab initio region within a larger region treated by density functional theory (DFT) [41]. This removes uncertainty in reaction barriers and energies by removing dependence on the DFT functional [42], including for metalloenzymes [43]. Different types of application require different levels of treatment, which can be effectively combined in multiscale models to tackle a range of time- and length-scales, e.g. to study drug metabolism by cytochrome P450 enzymes [44], combining coarse-grained and atomistic molecular dynamics simulations, and QM/MM methods.

Multiscale simulation schemes also now allow QM/MM methods to be applied to free energy simulations to study e.g. protein-ligand binding [45]. This approach allows the difference between a QM and a MM description of a ligand to be quantified, e.g. to calculate the contribution of changes in electronic polarization to binding affinity [46] and also to test the consistency of different QM and MM methods [47]. Such QM/MM free energy calculations, combined with metadynamics simulations, reveal changes in electronic polarization of a ligand (ibrutinib) as it binds to/dissociates from its protein target, showing limitations of MM forcefields for predicting binding kinetics [48].


Prof. Adrian Mulholland
University of Bristol

Adrian Mulholland (AJM) is a Professor of Chemistry at the University of Bristol. His research focuses on the investigation of mechanisms of enzyme catalysis, and biomolecular structure and function more generally, by computational modelling and simulation. He has worked for over 25 years on the development and application of multiscale techniques for modelling enzyme catalytic mechanisms. He has interests in biomolecular simulation applied to problems in antimicrobial resistance, drug metabolism, biocatalysis and enzyme thermoadaptation and evolution. He also works on interactive simulation tools using virtual reality. He collaborates with experimental research groups worldwide. He has a strong interest in the application of high performance computing (HPC) for biomolecular simulations: e.g., he established and led the UK High End Computing Consortium for Biomolecular Simulation (HECBioSim.ac.uk). He has published over 200 papers, attracting over 5,000 citations. He was elected Chair of the 2016 Gordon Research Conference in Computational Chemistry and was the inaugural Lakshmi Raman Lecturer at the University of Pittsburgh (2019). He was awarded the 2020 John Meurig Thomas Medal ‘for outstanding and innovative work in catalytic science’.

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[1] Combined quantum mechanics/molecular mechanics (QM/MM) methods in computational enzymology van der Kamp MW, Mulholland AJ. Biochemistry (2013) 52 2708-28. https://doi.org/10.1021/bi400215w

[2] Multiscale methods in drug design bridge chemical and biological complexity in the search for cures R.E. Amaro & A.J. Mulholland Nature Reviews Chemistry 2, 0148 (2018) https://doi.org/10.1038/s41570-018-0148  

[3] Insights into chorismate mutase catalysis from a combined QM/MM simulation of the enzyme reaction Lyne PD, Mulholland AJ & Richards WG. J. Am. Chem. Soc. (1995) 117 11345-11350 http://dx.doi.org/10.1021/ja00150a037

[4] Acetyl-CoA enolization in citrate synthase: A quantum mechanical molecular mechanical (QM/MM) study Mulholland AJ & Richards WG. Proteins (1997) 27 9-25

[5] Ab initio QM/MM study of the citrate synthase mechanism. A low-barrier hydrogen bond is not involved Mulholland AJ, Lyne PD & Karplus M. J. Am. Chem. Soc. (2000) 122 534-535

[6] Correlation of calculated activation energies with experimental rate constants for an enzyme catalyzed aromatic hydroxylation Ridder L, Mulholland AJ, Vervoort J, Rietjens IMCM. J. Am. Chem. Soc. (1998) 120 7641-7642 http://dx.doi.org/10.1021/ja980639r

[7] A quantum mechanical/molecular mechanical study of the hydroxylation of phenol and halogenated derivatives by phenol hydroxylase Ridder L, Mulholland AJ, Rietjens IMCM, Vervoort J. J. Am. Chem. Soc. (2000) 122 8728-8738 http://dx.doi.org/10.1021/ja0007814

[8] Simulations of enzymic reactions Mulholland AJ & Karplus M Biochem. Soc. Trans. (1996) 24 247–254 https://doi.org/10.1042/bst0240247

[9] Electronic structure of compound I in human isoforms of cytochrome P450 from QM/MM modeling Bathelt CM, Zurek J, Mulholland AJ, Harvey JN. J. Am. Chem. Soc. (2005) 127 12900-12908 http://dx.doi.org/10.1021/ja0520924

[10] Mechanisms of antibiotic resistance: QM/MM modeling of the acylation reaction of a class A beta-lactamase with benzylpenicillin Hermann JC, Hensen C, Ridder L, Mulholland AJ, Holtje HD. J. Am. Chem. Soc. (2005) 127 4454-4465 http://dx.doi.org/10.1021/ja044210d

[11] Combined quantum and molecular mechanical study of DNA cross-linking by nitrous-acid Elcock AH, Lyne PD, Mulholland AJ, Nandra A, Richards WG. J. Am. Chem. Soc. (1995) 117 4706-4707 http://dx.doi.org/10.1021/ja00121a029

[12] Quantum Mechanics/Molecular Mechanics (QM/MM) Calculations Support a Concerted Reaction Mechanism for the Zika Virus NS2B/NS3 Serine Protease with Its Substrate Nutho B, Mulholland AJ & Rungrotmongkol T. (2019) J. Phys. Chem. B 123 2889-2903 DOI: https://doi.org/10.1021/acs.jpcb.9b02157

[13] Quantum Mechanics/Molecular Mechanics Simulations Show Saccharide Distortion is Required for Reaction in Hen Egg‐White Lysozyme Limb MAL, Suardiaz R, Grant IM & Mulholland AJ (2019) Chem. Eur. J. 25 764-768 https://doi.org/10.1002/chem.201805250

[14] Understanding complex mechanisms of enzyme reactivity: the case of Limonene-1,2-epoxide hydrolases Rinaldi S, van der Kamp MW, Ranaghan KE, Mulholland AJ & Colombo, G (2019) ACS Catalysis 8 5698–5707 https://doi.org/10.1021/acscatal.8b00863

[15] Mechanism of Covalent Binding of Ibrutinib to Bruton’s Tyrosine Kinase revealed by QM/MM Calculations Voice A, Tresadern G, Twidale R, van Vlijmen H & Mulholland AJ (2020) https://doi.org/10.26434/chemrxiv.13149893.v1

[16] Quantum mechanics/molecular mechanics modeling of fatty acid amide hydrolase reactivation distinguishes substrate from irreversible covalent inhibitors Lodola A, Capoferri L, Rivara S, Tarzia G, Piomelli D, Mulholland AJ & Mor M (2013) J. Med. Chem. 56 2500-12 http://dx.doi.org/10.1021/jm301867x

[17] Identification of productive inhibitor binding orientation in fatty acid amide hydrolase (FAAH) by QM/MM mechanistic modelling Lodola A, Mor M, Rivara S, Christov C, Tarzia G, Piomelli D & Mulholland AJ. (2008) Chem. Commun. (2) 214-216 http://dx.doi.org/10.1039/b714136j

[18] QM/MM modelling of ketosteroid isomerase reactivity indicates that active site closure is integral to catalysis van der Kamp MW, Chaudret R, Mulholland AJ (2013) FEBS J. 280 3120-3131 http://dx.doi.org/10.1111/febs.12158

[19] Structural Fluctuations in Enzyme-Catalyzed Reactions: Determinants of Reactivity in Fatty Acid Amide Hydrolase from Multivariate Statistical Analysis of Quantum Mechanics/Molecular Mechanics Paths Lodola A, Sirirak J, Fey N, Rivara S, Mor M, Mulholland AJ (2010) J. Chem. Theor. Comput. 6 2948-2960 https://doi.org/10.1021/ct100264j

[20] Conformational Effects in Enzyme Catalysis: Reaction via a High Energy Conformation in Fatty Acid Amide Hydrolase Lodola A, Mor M, Zurek J, Tarzia G, Piomelli D, Harvey JN & Mulholland AJ (2007) Biophys J. 92 L20–L22 https://doi.org/10.1529/biophysj.106.098434

[21] Quantum Mechanics/Molecular Mechanics Modeling of Substrate-Assisted Catalysis in Family 18 Chitinases: Conformational Changes and the Role of Asp142 in Catalysis in ChiB. Jitonnom J, Lee VS, Nimmanpipug P, Rowlands HA & Mulholland AJ (2011) Biochemistry 50 pp. 4697-4711 http://dx.doi.org/10.1021/bi101362g

[22] Unraveling the role of protein dynamics in dihydrofolate reductase catalysis Luk LY et al.  (2013) Proc. Natl. Acad. Sci. USA. 11016344-16349 http://dx.doi.org/10.1073/pnas.1312437110

[23] Analysis of classical and quantum paths for deprotonation of methylamine by methylamine dehydrogenase Ranaghan KE, Masgrau L, Scrutton NS, Sutcliffe MJ, Mulholland AJ ChemPhysChem (2007) 8 1816-1835 http://dx.doi.org/10.1002/cphc.200700143

[24] Atomic description of an enzyme reaction dominated by proton tunneling Masgrau L et al. (2006) Science 312  237-241 http://dx.doi.org/10.1126/science.1126002

[25] A catalytic role for methionine revealed by a combination of computation and experiments on phosphite dehydrogenase Ranaghan KE et al. (2014) Chemical Science 5 2191-2199 https://doi.org/10.1039/C3SC53009D

[26] Quantum mechanics/molecular mechanics modelling of drug metabolism: Mexiletine N-hydroxylation by cytochrome P450 1A2 Lonsdale R, Fort R, Rydberg P, Harvey JN & Mulholland AJ (2016) Chemical Research in Toxicology 29 963–971 http://dx.doi.org/10.1021/acs.chemrestox.5b00514

[27] Determinants of Reactivity and Selectivity in Soluble Epoxide Hydrolase from QM/MM Modeling Lonsdale R, Hoyle S, Grey DT, Ridder L & Mulholland AJ (2012) Biochemistry 51 1774-1786 http://dx.doi.org/10.1021/bi201722j

[28] QM/MM Modeling of Regioselectivity of Drug Metabolism in Cytochrome P450 2C9 Lonsdale R, Houghton KT, Zurek J, Bathelt CM, Foloppe N, de Groot MJ, Harvey JN & Mulholland AJ (2013) J. Am. Chem. Soc. 135 8001–8015 http://dx.doi.org/10.1021/ja402016p

[29] QM/MM Modelling of Drug-Metabolizing Enzymes Lonsdale R & Mulholland AJ (2014) Curr. Top. Med. Chem. 14 1339-1347 https://doi.org/10.2174/1568026614666140506114859

[30] L718Q mutant EGFR escapes covalent inhibition by stabilizing a non-reactive conformation of the lung cancer drug Osimertinib Callegari D et al. (2018) Chemical Science 9 2740-2749. https://doi.org/10.1039/C7SC04761D

[31] Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity Huggins DJ et al. (2019) Wiley Interdisciplinary Reviews: Computational Molecular Science 9 e1393 https://doi.org/10.1002/wcms.1393

[32] An efficient computational assay for β-lactam antibiotic breakdown by class A β-lactamases Hirvonen VHA et al.(2019) Journal of Chemical Information and Modeling 59 3365-3369 https://doi.org/10.1021/acs.jcim.9b00442

[33] Multiscale simulations of clavulanate inhibition identify the reactive complex in class A β-lactamases and predict the efficiency of inhibition Fritz RA et al.(2018) Biochemistry 57 3560-3563 https://doi.org/10.1021/acs.biochem.8b00480

[34] Small changes in hydration determine cephalosporinase activity of OXA-48 β-lactamases Hirvonen VHA et al. (2020) ACS Catalysis 10 6188–6196 https://doi.org/10.1021/acscatal.0c00596

[35] Mechanism of Inhibition of SARS-CoV-2 M pro  by N3 Peptidyl Michael Acceptor Explained by QM/MM Simulations and Design of New Derivatives with Tunable Chemical Reactivity Arafet K et al. (2021) Chemical Science Accepted Manuscript https://doi.org/10.1039/D0SC06195F

[36] High accuracy computation of reaction barriers in enzymes Claeyssens F et al. (2006) Angew. Chem. Int. Ed. 45 6856-9 http://dx.doi.org/10.1002/anie.200602711

[37] Ab Initio QM/MM Modelling of the Rate-Limiting Proton Transfer Step in the Deamination of Tryptamine by Aromatic Amine Dehydrogenase Ranaghan KE et al. (2017) J. Phys. Chem. B 121 9785–9798 https://doi.org/10.1021/acs.jpcb.7b06892

[38] Testing High-Level QM/MM Methods for Modeling Enzyme Reactions: Acetyl-CoA Deprotonation in Citrate Synthase van der Kamp MW et al. (2010) J. Phys. Chem. B 114 11303-11314 https://doi.org/10.1021/jp104069t

[39] “Lethal synthesis” of fluorocitrate by citrate synthase explained through QM/MM modeling van der Kamp MW, McGeagh JD & Mulholland AJ (2011) Angew. Chem. Int. Ed. Engl. 50 10349-51 https://doi.org/10.1002/anie.201103260

[40] Chemical accuracy in QM/MM calculations on enzyme-catalysed reactions Mulholland AJ (2007) Chemistry Central J. 1 Article number: 19 https://doi.org/10.1186/1752-153X-1-19

[41] A projector embedding approach for multi scale coupled-cluster calculations applied to citrate synthase Bennie, S et al. (2016) J. Chem. Theor. Comput. 12 2689–2697 http://dx.doi.org/10.1021/acs.jctc.6b00285

[42] Projector-based embedding eliminates density functional dependence for QM/MM calculations of reactions in enzymes and solution Ranaghan KE et al. (2019) J. Chem. Inf. Model. 59 2063–2078 https://doi.org/10.1021/acs.jcim.8b00940

[43] Electronic Structure Benchmark Calculations of CO2 Fixing Elementary Chemical Steps in RuBisCO Using the Projector-Based Embedding Approach Douglas-Gallardo OA et al. (2020). J. Comput. Chem. 41 2151-2157 https://doi.org/10.1002/jcc.26380

[44] A Multiscale Approach to Modelling Drug Metabolism by Membrane-Bound Cytochrome P450 Enzymes Lonsdale R. et al. (2014) PLoS Comput. Biol. 10 e1003714. https://doi.org/10.1371/journal.pcbi.1003714

[45] An efficient method for the calculation of quantum mechanics/molecular mechanics free energies Woods CJ, Manby FR & Mulholland AJ. (2008) J. Chem. Phys. 128 014109. http://dx.doi.org/10.1063/1.2805379

[46] Combined Quantum Mechanics/Molecular Mechanics (QM/MM) Simulations for Protein-Ligand Complexes: Free Energies of Binding of Water Molecules in Influenza Neuraminidase Woods CJ, Shaw KE & Mulholland AJ (2015) J. Phys. Chem. B 119 997-1001. http://dx.doi.org/10.1021/jp506413j

[47] Compatibility of Quantum Chemical Methods and Empirical (MM) Water Models in Quantum Mechanics/Molecular Mechanics Liquid Water Simulations Shaw KE, Woods CJ, Mulholland AJ. (2010) J. Phys. Chem. Lett. 1 219-223 http://dx.doi.org/10.1021/jz900096p

[48] A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics Haldar S. et al. (2018) J. Chem Theory Comput. 14 6093-6101 https://doi.org/10.1021/acs.jctc.8b00687

[49] A practical guide to modelling enzyme-catalysed reactions Lonsdale R, Harvey JN & Mulholland AJ (2012) Chemical Society Reviews 41 3025-3038 DOI: 10.1039/C2CS15297E

[50] Combined Quantum Mechanics and Molecular Mechanics Studies of Enzymatic Reaction Mechanisms Ainsley J et al. (2018) Adv. Protein Chem. Struct. Biol. 113 1-32 https://doi.org/10.1016/bs.apcsb.2018.07.001