Oral Presentation The 47th Lorne Conference on Protein Structure and Function 2022

Multiscale simulation of enzyme catalytic mechanisms: dynamics, evolution and design (#36)

Adrian Mulholland 1
  1. University of Bristol, Bristol, ACT, Australia

Biomolecular simulations are revealing mechanisms of enzyme catalysis and inhibition, dynamics and thermodynamics relevant to function, and are contributing to catalyst and inhibitor design. Simulations can be used as computational ‘assays’ of biological activity, e.g. to predict drug resistance or the effects of mutation [1]. Different types of application require different levels of treatment, which can be combined in multiscale models to tackle a range of time- and length-scales [2,3]. Molecular simulation methods of various types are now capable of modelling processes ranging from biochemical reactions to membrane dynamics, and can offer increasing predictive power. Recently, this has included identifying important features of SARS-CoV-2 proteins, such as the effects of linoleate on the viral Spike protein [4,5].

Dynamical-nonequilibrium molecular dynamics (D-NEMD) simulations [6] reveal allosteric coupling of the fatty acid binding site to distant functional regions in the Spike, such as the furin cleavage site [5]. D-NEMD simulations also show coupling between allosteric sites and the active site in beta-lactamase enzymes; the pathways identified contain positions that differ between clinically relevant variants of these enzymes, indicating that allosteric effects modulate the spectrum of activity of these antibiotic resistance enzymes [7].

Combined quantum mechanics/molecular mechanics (QM/MM) methods allow modelling of reactions in proteins: they can identify mechanisms of reaction (e.g. for targeted covalent inhibitors such as ibrutinib [8]) and determinants of catalytic activity [9] and drug resistance [10]; and predict the activity of bacterial enzymes against antibiotics.

Increasingly, simulations are contributing to the engineering of natural enzymes, and to design and development of de novo biocatalysts [11]. Simulations are also contributing to the emerging evidence that activation heat capacity is an important factor in enzyme evolution and thermoadaptation [12,13]. Directed evolution of the catalytic activity of a designed Kemp eliminase unexpectedly introduced curvature into the temperature dependence of catalysis, a signature of the appearance of an activation heat capacity [14]. Simulations identify the dynamical networks involved, which may provide useful targets for mutation [15].

Virtual reality offers new ways interact with simulations, and new ways to collaborate [16,17]. Interactive MD simulation in virtual reality (iMD-VR) allows direct manipulation of biological macromolecules, going beyond mere visualization to allow e.g. fully flexible docking of drugs into protein targets [18,19]. The COVID-19 pandemic has highlighted the need for effective tools for virtual collaboration in VR. Groups of researchers can work together in the same virtual environment, using iMD-VR for molecular problems such as catalyst and structure-based drug design. These simulation methods, including iMD-VR, with collaborative sharing of models and data, have been brought together to develop peptide inhibitors of the SARS-CoV-2 main protease [20].

  1. 1. Huggins, D.J, et al. Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity. WIREs Comput Mol Sci. 2019; 9:e1393. https://doi.org/10.1002/wcms.1393
  2. 2. Amaro, R.,E. & Mulholland, A.J. Multiscale methods in drug design bridge chemical and biological complexity in the search for cures. Nature Reviews Chemistry 2, 0148 (2018). https://doi.org/10.1038/s41570-018-0148
  3. 3. Jagger B.R. et al. Multiscale simulation approaches to modeling drug–protein binding. Current Opinion in Structural Biology, 61, 213-221 (2020) https://doi.org/10.1016/j.sbi.2020.01.014.
  4. 4. Gupta, K. et al. Structural insights in cell-type specific evolution of intra-host diversity by SARS-CoV-2. Nat Commun 13, 222 (2022). https://doi.org/10.1038/s41467-021-27881-6
  5. 5. Oliveira A.SF. et al. The fatty acid site is coupled to functional motifs in the SARS-CoV-2 spike protein and modulates spike allosteric behaviour. Computational and Structural Biotechnology Journal, 20, 139-147 (2022) https://doi.org/10.1016/j.csbj.2021.12.011
  6. 6. Oliveira, A.S.F. et al. Dynamical nonequilibrium molecular dynamics reveals the structural basis for allostery and signal propagation in biomolecular systems. Eur. Phys. J. B 94, 144 (2021). https://doi.org/10.1140/epjb/s10051-021-00157-0
  7. 7. I. Galdadas et al. Allosteric communication in class A β-lactamases occurs via cooperative coupling of loop dynamics. eLife 10:e66567 DOI: 10.7554/eLife.66567 (2021)
  8. 8. A Voice et al. Mechanism of covalent binding of ibrutinib to Bruton’s tyrosine kinase revealed by QM/MM calculations’ Chem. Sci., 12, 5511-5516 (2021) https://doi.org/10.1039/D0SC06122K
  9. 9. Hirvonen, V. et al. Multiscale simulations identify origins of differential carbapenem hydrolysis by the OXA-48 β-lactamase. ChemRxiv. (2021) doi:10.26434/chemrxiv-2021-bv7tb
  10. 10. Callegari D. et al, L718Q mutant EGFR escapes covalent inhibition by stabilizing a non-reactive conformation of the lung cancer drug osimertinib. Chem. Sci., 9, 2740-2749 (2018)
  11. 11. Bunzel, H.A. et al. Designing better enzymes: Insights from directed evolution, Current Opinion in Structural Biology, 67, 212-218 (2021) https://doi.org/10.1016/j.sbi.2020.12.015.
  12. 12. van der Kamp et al. Dynamical origins of heat capacity changes in enzyme-catalysed reactions. Nat Commun 9, 1177 (2018). https://doi.org/10.1038/s41467-018-03597-y
  13. 13. Arcus V.L. & Mulholland A.J. Temperature, Dynamics, and Enzyme-Catalyzed Reaction Rates. Annual Review of Biophysics 49, 163-180 (2020) https://doi.org/10.1146/annurev-biophys-121219-081520
  14. 14. Bunzel H.A. et al. Emergence of a Negative Activation Heat Capacity during Evolution of a Designed Enzyme. J. Am. Chem. Soc. 141, 11745–11748 (2019) https://doi.org/10.1021/jacs.9b02731
  15. 15. Bunzel, H.A. et al. Evolution of dynamical networks enhances catalysis in a designer enzyme. Nat. Chem. 13, 1017–1022 (2021). https://doi.org/10.1038/s41557-021-00763-6
  16. 15. Bunzel, H.A. et al. Evolution of dynamical networks enhances catalysis in a designer enzyme. Nat. Chem. 13, 1017–1022 (2021). https://doi.org/10.1038/s41557-021-00763-6
  17. 16. O'Connor et al. Interactive molecular dynamics in virtual reality from quantum chemistry to drug binding: An open-source multi-person framework. J. Chem. Phys. 150, 220901 (2019); https://doi.org/10.1063/1.5092590
  18. 17. O'Connor et al. Sampling molecular conformations and dynamics in a multiuser virtual reality framework. Science Advances. 4, eaat2731 https://www.science.org/doi/10.1126/sciadv.aat2731
  19. 18. Deeks HM et al. Interactive molecular dynamics in virtual reality for accurate flexible protein-ligand docking. PLoS ONE 15(3): e0228461 (2020) https://doi.org/10.1371/journal.pone.0228461
  20. 19. Deeks H. et al. Interactive Molecular Dynamics in Virtual Reality Is an Effective Tool for Flexible Substrate and Inhibitor Docking to the SARS-CoV-2 Main Protease. J. Chem. Inf. Model. 60, 5803–5814 (2020) https://doi.org/10.1021/acs.jcim.0c01030
  21. 20. Chan H. et al. Discovery of SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding. Chem. Sci. 12, 13686-13703 (2021) https://doi.org/10.1039/D1SC03628A