NPIF: Data driven enzyme design for bioprocess applications: New approaches to 3-D structure and property analysis
Supervisor 1: Dr Anna K Croft (Chemical and Environmental Engineering)
Supervisor 2: Dr Christof Jäger (Chemical and Environmental Engineering)
Supervisor 3: Drs Chris Fallaize, Chris Brignell and Yves van Gennip (Mathematics)
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. 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.
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. Applicants must have or be expected to obtain at least a 2:1 degree in a relevant subject.
This studentship is only 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 in October 2018, or as soon as possible thereafter, and before December 31st 2018. Please send applications to Dr Anna Croft firstname.lastname@example.org
, quoting the studentship reference.
Name: Dr Anna Croft
This PhD project is part of the National Productivity Investment Fund investment into the Nottingham BBSRC Doctoral Training Programme. Students will benefit from 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 University of Nottingham and Faculty of Engineering both hold Athena Swan Silver and are committed to ensuring a fair and healthy working culture.