About the Project
Optimisation of enzyme catalysis for cost-effective biocatalysis in industrial settings presents challenges; enzymes must be stable to process conditions, possess sufficient catalytic activity to produce enough turnover to be cost-effective, and be able to accept appropriate substrates – whether these are a diverse range of substrates, or sufficient specificity for a particular novel substrate. Rational improvement of enzyme function relies on either generating or accessing enzyme variants that have improved properties, and this can be heavily facilitated by taking a holistic approach for entire families that employs AI/Data Driven approaches. In particular, computational approaches can significantly increase the rate at which new variations can be screened, maximising the effective use of expensive experimental resources.
The aim of this project is to use a combination of statistical and machine learning pattern analysis to develop a structure-based methodology underpinned by comprehensive large-scale homology-modelled data-sets. The focus will be on a superfamily of particularly challenging enzymes: the Radical SAM (rSAM) enzymes, and how these enzymes can be rationally improved to meet industrial requirements. Many rSAM enzymes show poor industrial parameters, yet isolated and characterised natural rSAMs have been shown to overcome these limitations – thus engineering to improve properties is feasible.
The student will receive training in computational chemistry, statistical modelling, including Bayesian methods, and in specific techniques such as molecular dynamics simulations, quantum mechanical calculations, and mathematical data classification. As a member of the BBSRC Doctoral Training Programme, students will access a diverse range of training opportunities, including specialist workshops, lectures and seminars, as well as participation in the University of Nottingham’s yearly BBSRC DTP Spring School event.
The project strongly benefits from the industrial input of VideraBio (viderabio.com), leaders in disruptive bioprocess development, and includes an integrated three-month placement. As biotechnology matures, there is an ever-greater need for scientists able to work effectively at the interface between life sciences, mathematics, and physical sciences, and the discipline-bridging combination of training provided will afford an excellent basis for a future career in biotechnology, including the pharmaceutical and fine chemicals industries.
Qualifications and Eligibility
Applicants must have or be expected to obtain at least a 2:1 degree in a relevant subject. This studentship is available to UK or EU citizens meeting BBSRC residency requirements and is fully funded at the Standard Research Council stipend rates for 4 years (currently fees plus a stipend of around £14,777 p/a). UK/EU students not meeting the residency requirements will be eligible for a fees-only studentship. Candidates will be expected to start as soon as possible before December 31st 2018.
How to apply
Applications should be in the form of a detailed CV and a covering letter. The CV should contain the names and contacts (including email addresses) of two referees, and the type, class and grade (or that predicted) of your degree. Please send applications and enquiries to Dr Anna Croft [Email Address Removed], quoting the studentship reference.
Applications will be considered up until 1st December.
Continue with Facebook
