James W. Gauld
Department of Chemistry and Biochemistry,
University of Windsor,
Ontario, N9B 3P4
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Elucidating the properties and chemistry of enzymes has long been of significant importance. This is partly due to the fact that they are central to many physiological processes occurring in cells. Indeed, they are critical for ensuring that metabolically important reactions within cells and organisms occur at life-sustaining rates, efficiency, and accuracy. Impressively, 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 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.3,4 For those that target enzymes this requires detailed knowledge of the latter's active site structure, properties, and mechanisms. Unfortunately, this knowledge is often limited.
Computational enzymology is the application of computational chemistry methods to the study enzymes. One of its major goals is to elucidate enzyme's catalytic mechanisms, the role of active site residues, as well as the surrounding protein/solvent environment. Nowadays, there are a range of computational methods available to the researcher 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. Each method has its strengths and limitations, which themselves at times can teach us about some aspect of enzymology. As a result, the modern practitioner must increasingly be adept at multiple methodologies.
In this lecture we will discuss what is computational enzymology, as well as practicalities of such aspects as chemical model construction, commonly applied computational methods and their application as well as challenges. These will be illustrated using examples from the literature and research from the Gauld group.
(1) Bloksberg-Fireovid, R.; Hewes, J., Catalysis and Biocatalysis Technologies: Leveraging Resources and Targeting Performance. National Institute of Standards and Technology: NIST, 1998. (2) World Health Organization: Antibiotic Resistance. http://www.who.int/mediacentre/factsheets/antibiotic-resistance/en/ (accessed Jan. 4, 2018). (3) Mavromoustakos, T.; Durdagi, S.; Koukoulitsa, C.; Simcic, M.; Papadopoulos, M. G.; Hodoscek, M.; Golic Grdadolnik, S. Curr. Med. Chem. 2011, 18, 2517-2530. (4) Lounnas, V.; Ritschel, T.; Kelder, J.; McGuire, R.; Bywater, R. P.; Foloppe, N. Comput. Struct. Biotechnol. J. 2013, 5, e201302011.
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.