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
Date: Monday 14 December, 2020
Time: 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 . QM/MM barriers were found to correlate with experimental rate constants for alternative substrates in para-hydroxybenzoate hydroxylase  and phenol hydroxylase , 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  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 .
QM/MM simulations can be used as computational ‘assays’ of enzyme activity , e.g. distinguishing between beta-lactamases that can effectively hydrolyse carbapenem antibiotics from those that cannot . QM/MM simulations also reproduce their susceptibility to inhibitors such as clavulanate . 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 . Recent QM/MM applications include modelling mechanisms of covalent inhibition of the SARS-CoV-2 main protease and suggesting modifications to tune reversibility .
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 . The excellent agreement with experiment shows the applicability of transition state theory for enzyme-catalysed reactions .
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) . This removes uncertainty in reaction barriers and energies by removing dependence on the DFT functional , including for metalloenzymes . 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 , 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 . 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  and also to test the consistency of different QM and MM methods . 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 .
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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 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
 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
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 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
 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
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 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 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
 “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
 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
 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
 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
 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
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 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
 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
 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
 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
 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