Int. J. Mol. Sci. 2014, 15(1), 401-422; doi:10.3390/ijms15010401

Multi-Scale Computational Enzymology: Enhancing Our Understanding of Enzymatic Catalysis

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Received: 11 November 2013; in revised form: 5 December 2013 / Accepted: 24 December 2013 / Published: 31 December 2013
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Elucidating the origin of enzymatic catalysis stands as one the great challenges of contemporary biochemistry and biophysics. The recent emergence of computational enzymology has enhanced our atomistic-level description of biocatalysis as well the kinetic and thermodynamic properties of their mechanisms. There exists a diversity of computational methods allowing the investigation of specific enzymatic properties. Small or large density functional theory models allow the comparison of a plethora of mechanistic reactive species and divergent catalytic pathways. Molecular docking can model different substrate conformations embedded within enzyme active sites and determine those with optimal binding affinities. Molecular dynamics simulations provide insights into the dynamics and roles of active site components as well as the interactions between substrate and enzymes. Hybrid quantum mechanical/molecular mechanical (QM/MM) can model reactions in active sites while considering steric and electrostatic contributions provided by the surrounding environment. Using previous studies done within our group, on OvoA, EgtB, ThrRS, LuxS and MsrA enzymatic systems, we will review how these methods can be used either independently or cooperatively to get insights into enzymatic catalysis.
Keywords: enzyme catalysis; density functional theory (DFT) cluster method; quantum mechanics/molecular mechanics (QM/MM); molecular dynamics (MD) simulations; molecular docking
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MDPI and ACS Style

Gherib, R.; Dokainish, H.M.; Gauld, J.W. Multi-Scale Computational Enzymology: Enhancing Our Understanding of Enzymatic Catalysis. Int. J. Mol. Sci. 2014, 15, 401-422.

AMA Style

Gherib R, Dokainish HM, Gauld JW. Multi-Scale Computational Enzymology: Enhancing Our Understanding of Enzymatic Catalysis. International Journal of Molecular Sciences. 2014; 15(1):401-422.

Chicago/Turabian Style

Gherib, Rami; Dokainish, Hisham M.; Gauld, James W. 2014. "Multi-Scale Computational Enzymology: Enhancing Our Understanding of Enzymatic Catalysis." Int. J. Mol. Sci. 15, no. 1: 401-422.

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