James W. Gauld, Professor
Department of Chemistry and Biochemistry,
University of Windsor,
Ontario, N9B 3P4
Elucidating the properties and chemistry of enzymes has long been of significant importance. This is due in part to the fact that they are central to many physiological processes that occur in cells. In particular, they are critical for ensuring that metabolically important reactions that occur within cells and organisms occur with life-sustaining rates, efficiency, and accuracy. Furthermore, they often achieve this under relatively mild conditions. Thus, in addition to the fundamental knowledge to be gained, they also present tremendous potential health and industrial benefits.
Indeed, it has been estimated that in the US more than 90% of chemical and pharmaceutical manufacturing requires catalysts.1 Meanwhile, due to their critical physiological roles, enzymes are often the desired target of therapeutic drugs. Recently, the World Health Organization declared "antibiotic resistance one of the biggest threats to global health, food security, and development".2 Rational design is a powerful tool for developing new drugs to combat this present and growing threat. For those that target enzymes this requires detailed knowledge of the latter's active site structures, properties, and mechanisms. Unfortunately, this knowledge is often at best limited.
3,4Computational enzymology, in its broadest sense, is the use of computers to study the properties and mechanisms of enzymes. However, one of its major goals is to elucidate the catalytic mechanism of an enzyme or enzymes, the role of their key active site residues, and/or surrounding protein and solvent environment. Nowadays there are a range of computational methods available to the researcher that can be brought to bear on such challenges including molecular dynamics, quantum mechanical (QM)-chemical cluster, quantum mechanical/molecular mechanic (QM/MM). Increasingly, it is common to complementarily apply several of these methods.
In this lecture we will discuss several key aspects of computational enzymology including chemical model construction, commonly applied computational methods and their complementary application and challenges. These will be illustrated using examples from the literature as well as our own research in the Gauld group.
1. Quesne, M. G.; Borowski, T.; de Visser, S. P. Quantum Mechanics/Molecular Mechanics Modeling of Enzymatic Processes: Caveats and Breakthroughs. Chem. Eur. J. 2016, 22, 2562-2581.
2. Sousa, S. F.; Fernandes, P. A.; Ramos, M. J. Computational Enzymatic Catalysis – Clarifying Enzymatic Mechanisms With The Help of Computers. Phys. Chem. Chem. Phys. 2012, 14, 12431-12441.
3. Himo, F. Recent Trends in Quantum Chemical Modeling of Enzymatic Reactions. J. Am. Chem. Soc. 2017, 139, 6780-6786.
4. Chung, L. W.; Sameera, W. M.; Ramozzi, R.; Page, A. J.; Hatanaka, M.; Petrova, G. P.; Harris, T. V.; Li, X.; Ke, Z.; Liu, F.; Li, H. B.; Ding, L.; Morokuma, K. The ONIOM Method and Its Applications. Chem. Rev. 2015, 115, 5678-6796.
5. Simulating Enzyme Reactivity: Computational Methods in Enzyme Catalysis, I. Tuñón, V. Moliner (Eds.). The Royal Society of Chemistry Publishing, UK (2017).
Some examples of enzymatic systems discussed are taken from our own studies which are linked to at http://www.uwindsor.ca/compchem. Particular examples that will be used include:
6. Aboelnga, M. M.; Hayward, J. J.; Gauld, J. W. Enzymatic Post-Transfer Editing Mechanism of E. coli Threonyl-tRNA Synthetase (ThrRS): A Molecular Dynamics (MD) and Quantum Mechanics/Molecular Mechanics (QM/MM) Investigation. ACS Catalysis, 2017, 7, 5180-5193.
7. Almasi, J. N.; Bushnell, E. A. C.; Gauld, J. W. A QM/MM-Based Computational Investigation on The Catalytic Mechanism of Saccharopine Reductase. Molecules 2011, 16, 8569-8589.
8. Wei, W.; Gauld, J. W.; Monard, G. Pretransfer Editing in Threonyl-tRNA Synthetase: Roles of Differential Solvent Accessibility and Intermediate Stabilization. ACS Catalysis 2017, 7, 3102-3112.