Watch QM/MM Best Practice: Towards chemical accuracy in QM/MM modelling of enzyme catalytic mechanisms [.] Video Tutorial

Adrian Mulholland presents the fifth webinar in the BioExcel virtual workshop on Best Practices in QM/MM Simulation of Biomolecular Systems: https://bioexcel.eu/qmmm-best-practice-workshop Full title: "Towards chemical accuracy in QM/MM modelling of enzyme catalytic mechanisms and protein-ligand binding" Slides: http://doi.org/10.5281/zenodo.4382722 This presentation covers 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]. References: see https://bioexcel.eu/qmmm-best-practice-workshop-5