James W. Gauld, Professor

Department of Chemistry and Biochemistry,
University of Windsor,
Windsor,
Ontario, N9B 3P4
Canada

Abstract

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.

James W. Gauld

Department of Chemistry and Biochemistry,
University of Windsor,
Windsor,
Ontario, N9B 3P4
Canada

Video Recording

Video is available only for registered users.

Abstract

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.

Joakim Halldin Stenlid

Department of Physics, Stockholm University, Stockholm, Sweden

Video Recording

Video is available only for registered users.

Abstract

When interacting with electron-donors, neutral compounds of copper, silver and gold form bonds that are similar to hydrogen and halogen bonds. This type of bonding is referred to as regium bonding and it have been used to rationalize e.g. noble metal catalysis. Compounds donating regium, halogen and hydrogen bonds have in common local regions deficient in electron density, known as σ-holes. The chemistry of such regions can be characterized by local maxima in the electrostatic potential evaluated on contour surfaces of constant electron density (V_S,max); the position of the VS,max identify sites susceptible to interactions with nucleophiles, e.g. H₂O, H₂S, NH₃ and CO, while the magnitude of a V_S,max scales with the strength of the corresponding interaction. By this approach, information on the reaction and interaction tendencies of a compound can be readily accessed by standard DFT calculations. Regium bonds contains contributions from electrostatics, but also from polarization and charge-transfer. The latter are not directly captured by VS,max, but can be well-described by a newly introduced property called the local surface electron attachment energy. Minima in this property (E_S,min) provide a measure of the local electron affinity. E_S,min is used complementary to V_S,max to identity and rank interaction sites.
The use of molecular properties, such as the electrostatic potential and the electron attachment energies, is generally referred to as the Molecular Surface Property Approach (MSPA). These properties can be computed by e.g. DFT and provides estimate of reaction and/or interaction propensities of multiple sites of a compound from a single calculation. Historically MSPA has primarily been employed within the molecular science. The current presentation will exemplify how the MSPA can also be used as a guide to understand and predict the chemistry of materials and nanoparticles, opening up for a new realm of applications..