The field of biocatalysis has advanced to the stage where it is now viewed as a key enabling technology for the development of a greener and more efficient chemical industry. Adaption of nature’s enzymes through laboratory evolution has delivered efficient biocatalysts with altered substrate scope, stability and selectivity profiles. However, directed evolution is costly and time consuming, and for many important chemical transformations there are no enzymes known that can serve as suitable starting templates for optimization. Within this program, we will bring together the very latest experimental and computational protein engineering technologies to accelerate the delivery of ‘industry-ready’ biocatalysts. The latest deep learning tools for protein design allow us to navigate areas of protein sequence-structure space remote from those sampled in Nature with unprecedented speed and accuracy. Integrating these methods with advanced active site engineering tools will bring about a step-change in the speed of biocatalyst development and greatly expand the range of biocatalysts available for sustainable pharmaceutical manufacturing.

The successful candidate will work with a team of computational biologists and enzyme engineers to establish streamlined protocols for developing biocatalysts for sustainable manufacturing of RNA therapeutics. Nucleic acid-based therapeutics, including antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), mRNA vaccines and immunotherapeutics etc., are emerging as powerful new drug modalities with the potential to prevent and treat a wide range of diseases. For example, mRNA vaccines were rapidly developed in response to the Covid19 pandemic, and mRNA therapeutics are now emerging for the treatment of cancers and genetic disorders. This growing number of RNA therapies and vaccines is placing increasing demands on our manufacturing capabilities, and more efficient, sustainable and cost-effective approaches are urgently needed. In this regard, the proposal aligns perfectly with the FSE research priority area ‘Digital and AI; AI for Science’ but also addresses ‘Health Inequalities’.

Before you apply: We strongly recommend that you contact the supervisor(s) for this project before you apply.

How to apply: To be considered for this project you’ll need to complete a formal application through our online application portal. This link should directly open an application for FSE Bicentenary PhD . Please select University of Manchester funding in the funding section of the form.

When applying, you’ll need to specify the full name of this project , the name of your proposed supervisor/s , details of your previous study, and names and contact details of two referees . You are also required to upload your CV and a Personal Statement describing your motivation for applying for the project.

Your application cannot be processed without all of the required documents, and we cannot accept responsibility for late or missed deadlines where applications are incomplete.

Equality, diversity and inclusion: Equality, diversity and inclusion are fundamental to the success of The University of Manchester, and are at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or equivalent) in a relevant science or engineering related discipline.

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