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(BBSRC DTP) A computational model for improving catalytic rate in enzymes


Project Description

Directed evolution of enzyme activity is an established methodology, generating biocatalysts that are of benefit for biotechnology, for example in bioenergy production [Turner 2009]. Such work typically involves mutation of amino acids lining an active site, with random traversal of this restricted sequence space coupled to experimental screening for activity. We would like to understand better the relationships between sequence, structure, and enzyme activity. Considering the influence of mutations on both substrate specificity and catalytic rate, in very simple terms, steric and charge complementarity in the ground state map to specificity, whereas complementarity in the transition state influences rate. Relatively little work has been carried out to support models for rate variation as compared to those for modulation of specificity. An interesting feature is that rate (kcat) can be altered by changes at some distance from the active site [Currin et al 2015], laying down a challenge for the construction of predictive models. Since charge often plays a major role in transition state stabilisation [Singh et al 2014], it makes sense to ask whether measured activity data correlate with calculated charge interactions in a chemical model for transition and ground states. A precedent for this approach lies in the observation that redox potentials of heme groups correlate with computed charge interactions with the protein environment [Zheng & Gunner 2009]. Thus physicochemical modelling allows us to understand variation in key biological and biotechnological properties. This computational PhD project will combine the expertise of 3 groups within the MIB to develop a predictive model for enzyme rate change upon engineering and redesign. It will focus on charge interactions, but include other effects where appropriate, such as flexibility. Briefly, and noting expertise within the supervisory team, developing chemical models for ground and transition states is key for this work (Sam de Visser), informed by docking of substrates (Sam Hay), followed by calculations of enzyme – ground/transition state interactions over a wide range of engineered sequences and modelled structures (Jim Warwicker) [Ivanov et al 2017]. In practical terms there is cross-over in the expertise of the team, a central theme is that we aim to better understand enzyme action through the use of both focussed studies on well-characterised enzymes and more broadly across enzyme families. The project will make predictive models available at our http://www.protein-sol.manchester site. An early task, for web delivery, is to develop more simple visualisations of electric field than are available currently.

http://personalpages.manchester.ac.uk/staff/j.warwicker/
http://www.manchester.ac.uk/research/sam.devisser/research
https://www.research.manchester.ac.uk/portal/sam.hay.html

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the BBSRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BBSRC DTP website View Website

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

1. Currin A, Swainston N, Day PJ, Kell DB (2015) Chem Soc Rev 44:1172.
2. Ivanov SM, Cawley A, Huber RG, Bond PJ, Warwicker J (2017) PLoS One 12:e0185928.
3. Singh MK, Chu ZT, Warshel A (2014) J Phys Chem B 118:12146.
4. Turner NJ (2009) Nat Chem Biol 5:567.
5. Zheng Z, Gunner MR (2009) Proteins 75:719.

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