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Machine learning to evolve protein sequences beyond natural selection

Project Description

The overall aim of the proposal is to machine-learn how to realise proteins that are optimal for different environment by using the information that evolution has left behind in the form of protein sequences. Besides new biological understanding, the knowledge to design functional proteins that work in conditions different from those in which they evolved. Currently statistical methods (including advanced ones such as hidden Markov models) are used to detect evolutionarily related sequences through their alignment and extract frequency of occurrence of amino acids (or conservation profiles) and sometimes simultaneous occurrence of pairs (sometimes called coevolution profiles). Large alignments of protein sequences contain information that is usually disregarded, i.e., those complex features involving all the amino acids in a sequence, including the least conserved. Conservation profiles are useful to detect evolutionary relations and have also been shown to be of some use in designing novel functional profiles. However, they are of no use to determine completely novel sequences with a specific function (in general, but not necessarily, connected to their ability to assume a well-define three-dimensional conformation) and with additional desirable properties.
Candidates should have a BSc or MSc in Physics, Mathematics, Statistics, Chemistry or Biology with some programming skills and/or experience in data analysis.

Funding Notes

Project is eligible for funding under the BBSRC White Rose DTP: Doctoral Studentships in Artificial Intelligence, Machine Learning and Data Driven Economy.

Successful candidates will receive funding for 4 years, covering UK/EU fees and research council stipend (£14,777 for 2018-19).

Candidates should have, or be expecting, a 2.1 or above at undergraduate level in a relevant field. If English is not your first language, you will also be required to meet our language entry requirements. The PhD is to start in Oct 2018.
Apply online: View Website
Include project title and supervisor name, and upload a CV and transcripts.

How good is research at University of Leeds in Biological Sciences?

FTE Category A staff submitted: 60.90

Research output data provided by the Research Excellence Framework (REF)

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