Title: Computational biochemistry: predictive modelling for biology and medicine
Funder: Engineering and Physical Sciences Research Council

Abstract: All of biology - life itself - depends on enzymes. Enzymes are large, natural molecules that allow specific biochemical reactions to take place quickly. As yet we do not understand what it is that makes them such good natural catalysts. There are many reasons for studying enzymes and the reactions they catalyse: many drugs are enzyme inhibitors (they stop specific enzymes from working), so better understanding of enzymes will help in the design of new drugs. It should also help understand and predict the effects of genetic variation, for example in understanding why some people may benefit from a particular drug, or may be at risk from a disease. Enzymes are also very good and environmentally friendly catalysts - knowing how they function should help in the design and development of new 'green' catalysts for industrial applications. Enzymes also show great promise as 'molecular machines' in the emerging field of nanotechnology. We will develop and apply advanced computer modelling methods , in collaboration with experimental biochemistry, to analyse in detail how enzymes work. We will study enzymes that are targets for designing drugs for the treatment of pain and anxiety, and study how drugs are broken down by enzymes in the body. We will develop new modelling methods, capable of dealing accurately with these large and complex systems, and the chemical reactions they catalyse. We will bring together state-of-the-art computer software and hardware, and new theoretical methods, to achieve unprecedented accuracy for modelling enzymes. These modelling methods promise to add an extra dimension to studying enzyme reactions - e.g. making molecular 'movies' of how enzymes work. We will also use the methods we develop to predict how strongly potential drugs bind to their protein targets. The methods we will develop and use are based on fundamental quantum mechanics, so will be better than current approximate techniques. Current methods for predicting how strongly different drugs bind to proteins are efficient, but lack reliability because they fail to capture the essential physics. Quantum mechanics provides a physically accurate representation of the interactions, but until now these methods have been too computationally intensive for practical use. We will base our developments on methods that can accurately model chemical reactions of small molecules, combined with techniques for modelling protein structure and dynamics, and extend these to study enzymes and their reactions. We will make use of the great power provided by the latest 'multi-core' computer chips. Altogether, this will require several ground-breaking developments, which we are well placed to carry out. We will develop and apply new methods that can calculate how reactions happen in enzymes, describing the energies of breaking and forming chemical bonds accurately and analyse how reaction is affected by protein dynamics. This work will be carried out in collaboration with experiments, with project partners in academia and industry, in the UK and abroad. We will make predictions and compare with experiments on the same enzymes to test our theoretical methods. This will involve the transfer and exchange of methods, data, ideas and researchers between experimental and modelling groups, in new and existing collaborations. The methods we develop and the results we obtain will be made widely available (e.g. via the web), and should be very useful to biologists, biochemists, drug designers and other researchers working on enzymes. We will extend these high-level methods to new areas of biology, to provide new tools for studying protein structure. The results should provide new and exciting insight into how enzymes function, and promise to make a major contribution to the development of new drugs.


Research activity codes: 1.1 NORMAL BIOLOGICAL DEVELOPMENT AND FUNCTIONING;5.1 PHARMACEUTICALS
Health categories: MENTAL HEALTH;GENERIC HEALTH RELEVANCE;NEUROLOGICAL