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A bioinformatics approach to understanding protein solubility tags.


   Faculty of Biology, Medicine and Health

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The crowded environment of a cell cytoplasm reveals that proteins and other biological molecules co-exist at high macromolecular concentrations. Many proteins, though, are difficult to express, purify, and/or store at the high individual component concentrations required for biotechnological applications. Several methodologies are used to counter this problem, including the use of solubility tag fusions, expression systems modified to aid protein folding, protein engineering, and the inclusion of additives for storage. Some of these methods make use of our current knowledge of relevant factors, such as facilitating native disulphide bond formation. However, this knowledge is limited. For example, fusion tags to enhance solubility can have quite different effects, depending on the protein of interest, in turn limiting the scope for accurate prediction of expression efficiency. This project seeks to improve our understanding of the role played by protein fusion solubility tags in expression. Importantly, some of the computational groundwork is in place, with a physico-chemical model for protein solubility being developed. Additionally, there exists a wealth of expression data in the literature. Since the effects of fusion tags are protein-dependent, it is anticipated that interactions between fusion tag and protein of interest could be critical to an improved understanding. These interactions will be the focus for developing the model for protein solubility. Both protein targets and solubility tags can be modelled, based on structure, and this information used to provide a predictive scheme for the differential effects of solubility tags.

Entry Requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a biological, chemical, or physical sciences area.  Candidates with experience or interest in the application of computational methods to biotechnology are encouraged to apply.

How To Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select the appropriate subject title.

For international students, we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences.

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/”


Funding Notes

Applications are invited from self-funded students. This project has a Band 1 fee. Details of our different fee bands can be found on our website View Website

References

Hebditch M, Warwicker J. protein-sol pKa: prediction of electrostatic frustration, with application to coronaviruses. Bioinformatics. 2020 Jul 19:btaa646.
Kalayan J, Henchman RH, Warwicker J. Model for Counterion Binding and Charge Reversal on Protein Surfaces. Mol Pharm. 2020 Feb 3;17(2):595-603.
Singh P, Roche A, van der Walle CF, Uddin S, Du J, Warwicker J, Pluen A, Curtis R. Determination of Protein-Protein Interactions in a Mixture of Two Monoclonal Antibodies. Mol Pharm. 2019 Dec 2;16(12):4775-4786.
Austerberry JI, Thistlethwaite A, Fisher K, Golovanov AP, Pluen A, Esfandiary R, van der Walle CF, Warwicker J, Derrick JP, Curtis R. Arginine to Lysine Mutations Increase the Aggregation Stability of a Single-Chain Variable Fragment through Unfolded-State Interactions. Biochemistry. 2019 Aug 13;58(32):3413-3421.
Hebditch M, Roche A, Curtis RA, Warwicker J. Models for Antibody Behavior in Hydrophobic Interaction Chromatography and in Self-Association. J Pharm Sci. 2019 Apr;108(4):1434-1441.

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