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Extreme computing for total modelling of biological catalysts

Project TotalEnz
Research Area Computational enzymology
Principal Investigator(s) Dr. Adrian J. Mulholland (AJM)
  • School of Chemistry, University of Bristol, UK
  • Wroclaw University of Technology, Poland


Enzymes are outstandingly efficient natural catalysts. Better understanding of the mechanisms by which they achieve these catalytic properties promises technological spin-offs such as routes to new drugs (many drugs are enzyme inhibitors, which bind to enzymes and prevent them from functioning); analysis of the effects of genetic variation and mutation (for example in predicting individual metabolism of pharmaceuticals); and in the design of new catalysts (for example biomimetic catalysts or engineered enzymes)1. There is great interest in developing protein catalysts for practical applications (for example in the pharmaceutical, chemical and biotechnology industries). Enzymes catalyse biochemical reactions with great precision and power, and provide the foundation for the richness and diversity of biochemistry. Harnessing this catalytic power for non-biological reactions is a major goal of modern chemistry, and would lead to new materials, more environmentally friendly industrial processes and improved medicines. However, to harness this power, and to design new enzymes, we must first gain a complete understanding of how natural enzymes work, and how each part of them functions. computational enzymology is increasingly recognised as essential for understanding these fascinating biological catalysts1,2. Recent developments now allow the accurate, first principles calculation of free energy barriers of enzyme catalysed reactions.3-5 However, the calculation of such an energy profile presents a significant computational challenge. The combination of modern algorithms and software with the extreme computing resources offered via DEISA now has the potential to compute hundreds of these catalytic profiles. The ability to generate so many profiles now allows, for the first time, the creation of complete enzyme catalysis maps. These maps will chart the source of an enzyme’s catalytic power, and will provide the detailed information necessary to rationalise the function of each part of the enzyme, explain its evolutionary history, and, most importantly, to propose new, designed enhancements and modifications. The construction of these catalytic maps will provide an unprecedented insight into exactly why an enzyme is as it is. In this project, we propose to develop an approach to calculate ‘catalysis maps’ for a key model enzyme (chorismate mutase), from organisms adapted to different physical conditions. These maps will provide the detailed information on the evolution of these natural catalysts and their adaptation, which will assist in catalyst design. This project will demonstrate the utility and potential power of this novel computationa approach. It aims to provide a springboard for future mapping of entire classes of enzymes and designed catalysts (e.g engineeered enzymes and bioimimetic catalysts) using extreme computing.


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