About the Project
Molecular dynamics (MD) simulations of proteins are a popular method of studying aspects of biomolecular function and dynamics. They require input structure(s), which are preferably experimentally determined, usually by X-ray crystallography. However, as proteins are often highly flexible, they adopt multiple conformations which interconvert over a wide range of timescales, which can be predominantly longer than the feasible MD simulation length. This can also influence the binding of therapeutically relevant binding of ligands or drugs. Enhanced sampling methods have been developed to improve the sampling in MD simulations, but these do not offer a complete solution to the MD sampling problem. Machine learning (ML) offers an alternative or complementary approach and has been successfully applied to the analysis of the high-dimensional data produced by MD simulations and in structure prediction where an experimentally derived structure or homology model is not available. Another alternative to all-atom MD simulation is coarse grained modelling and simulation where pseudo atoms describe groups of atoms such as amino acid residues. These methods are less computationally demanding so allow larger and/or longer simulations to be performed.
In this project, extensive all-atom MD simulations of a range of dynamics and conformationally flexible proteins will be used to generate data that will be used as training and test data for ML. Different representations of conformational space will be explored and ML used to map between these representations and complete 3D geometry/structure. Coarse grained models of each protein library will be created and dynamics simulations performed. Different methods will be explored to map between the all-atom and coarse grained representation. Coarse grained simulations will be benchmarked against all-atom simulation data and then used to enhance (interpolate and extrapolate) the conformal space descriptors of each protein, ultimately allowing the prediction of new protein conformations and a more complete description of the conformational landscapes. These approaches will subsequently be tested for therapeutically relevant protein targets in the context of ligand/drug binding.
The project will make use of a broad range of computational chemistry, structural biology, and machine learning techniques and will be carried out at both the Manchester Institute of Biotechnology (www.mib.ac.uk) and the Bioinformatics Institute (A*STAR), Singapore (http://www.bii.a-star.edu.sg/). For further information, please contact Sam Hay ([Email Address Removed]) or Peter Bond ([Email Address Removed]).
Applicants must have obtained, or be about to obtain, at least an upper second class honours degree or the equivalent qualification gained outside the UK, in an appropriate area of science, engineering or technology.
UK applicants interested in this project should make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. International applicants (including EU nationals) must ensure they meet the academic eligibility criteria (including English Language) as outlined before contacting potential supervisors to express an interest in their project. Eligibility can be checked via the University Country Specific information page (https://www.manchester.ac.uk/study/international/country-specific-information/).
Some restrictions apply to applicants from certain Asian countries. In general, students from Europe, the Americas, Africa, Australia, New Zealand, Korea and Japan are eligible to apply for the programme. Unfortunately, we cannot accept applications from south-east Asian countries such as Singapore, China and Malaysia.
If your country is not listed you must contact the Doctoral Academy Admissions Team providing a detailed CV (to include academic qualifications – stating degree classification(s) and dates awarded) and relevant transcripts.
Following the review of your qualifications and with support from potential supervisor(s), you will be informed whether you can submit a formal online application.
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/
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3] Shivgan AT, Marzinek JK, Huber RG, Krah A, Henchman RH, Matsudaira P, Verma CS, Bond PJ. (2020) Extending the Martini Coarse-Grained Force Field to N-Glycans. J
Chem Inf Model. 60:3864-3883.
4] Zhang S, Heyes, DJ, Feng L, Sun W, Johannissen LO, Liu H, Levy CW, Li X, Yang J, Yu X, Lin M, Hardman SJO, Hoeven R, Sakuma M, Hay S, Leys D, Rao Z, Zhou A, Cheng
Q, Scrutton NS (2019) Structural basis for enzymatic photocatalysis in chlorophyll biosynthesis. Nature, 574, 722-725
5] Zuzic L, Marzinek JK, Warwicker J, Bond PJ. (2020) A Benzene-Mapping Approach for Uncovering Cryptic Pockets in Membrane-Bound Proteins. J Chem Theory
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