Project description
Predicting enzyme dynamics
The field of industrial biotechnology is moving from chemistry-only-based catalysis to the use of enzymes to catalyse key reactions. Factors such as cost reduction, eco-friendliness, effectiveness, and specificity render biocatalysis an attractive alternative solution. The EU-funded PREDACTED project is interested to understand how enzymes function, adapt to different environmental conditions and evolve. Researchers will develop simulation models that can predict enzyme dynamics and the changes associated with catalysis. Results will help design improved artificial enzymes and address key challenges associated with the use of enzymes, such as antibiotic resistance.
Objective
Enzymes are superlative catalysts, honed by billions of years of evolution to achieve high specificity and efficiency. Understanding how enzymes function and are adapted to different environments will be essential in developing biocatalysts for the circular economy and in combating drug resistance. Basic principles of catalysis and mechanistic understanding have come from experiments and simulations, and significant progress has been made in designing and evolving de novo protein catalysts. Truly predictive understanding is limited, however. There is a need for models able to predict, e.g. whether an enzyme is able to break down a particular antibiotic and how to inhibit it; temperature dependence of catalysis; how mutations affect activity and temperature optima; to understand how catalytic power evolves and use those principles in the development of new biocatalysts.
Theoretical developments, and emerging multiscale methods, provide a route to the predictive understanding required. Fundamentally, protein dynamics are vital, not in ‘driving’ reaction but rather as a fundamental facet of natural enzymes on which evolution acts. PREDACTED will simulate enzyme dynamics and dynamical changes associated with catalysis. We will (1) Investigate the adaptation of enzyme activity using the emerging theoretical framework of macromolecular rate theory, and develop simulation approaches to predict enzyme temperature optima, with relevance e.g. for understanding ecosystem response to climate change, and for the development of biocatalysts for practical industrial applications. (2) Develop predictive simulation models for enzymes responsible for antibiotic resistance, analyse allosteric effects and predict spectrums of activity. (3) Model antibiotic breakdown and inhibition of beta-lactamases (4) Apply the understanding developed in redesigning and engineering natural and artificial enzymes to test the catalytic principles and demonstrate how they can be applied in practice.
Fields of science
- medical and health sciencesbasic medicinepharmacology and pharmacypharmaceutical drugsantibiotics
- natural scienceschemical sciencescatalysisbiocatalysis
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- medical and health sciencesbasic medicinepharmacology and pharmacydrug resistanceantibiotic resistance
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsenzymes
Programme(s)
Topic(s)
Funding Scheme
ERC-ADG - Advanced GrantHost institution
BS8 1QU Bristol
United Kingdom