This project is offered as part of the University of Dundee 4-year MRC DTP Programme “Quantitative and Interdisciplinary approaches to biomedical science”. This PhD programme brings together leading experts from the School of Life Sciences (SLS), the School of Medicine (SoM) and the School of Science and Engineering (SSE) to train the next generation of scientists at the forefront of international science. The outstanding biomedical research at the University of Dundee was recognised by its very high rankings in REF 2014, with Dundee rated as the top University for Biological Sciences in the UK. A wide range of projects are available within this programme crossing exceptional strengths in four key areas: Infection and Disease; Responses to Cellular Stresses; Development, Stem Cells and Neurobiology; and Big Data and Translation. All students on this programme will receive training in computational biology, mathematical biology and statistics to equip with the quantitative skills in tackling complex biological questions. In the 1st year, students will carry out 3 rotation projects prior to selection of the final PhD project.
Our research has focused for more than 20 years on developing effective computational methods to predict the function, structure and specificity of proteins from the amino acid sequence. This experience is encapsulated in widely used software tools which include the Jalview (www.jalview.org)sequence analysis workbench that has over 70,000 regular users world-wide and JPred (www.compbio.dundee.ac.uk/jpred)which performs up to 500,000 predictions of secondary structure and other features from the amino acid sequence for scientists in laboratories in the UK and internationally. Together, Jalview and JPred have accumulated over 7,000 citations to the papers that describe them.The rapid advances in DNA sequencing technology over recent years have stimulated the large-scale sequencing of populations of single species. There is now publically available data on variation in over 200,000 human individuals, human cancers, bacterial strains, major food crops (e.g. wheat and barley) and animals (e.g. cow). While most effort to date has focused on exploiting these data to identify variants involved in genetic disease, the variation data provides a completely new resource to inform details of protein structure, function and interactions within a species. Recent work from our group (MacGowan et al, 2017) has demonstrated that variation data can identify key residues important in protein-ligand specificity and protein-protein interaction specificity in over 200 protein domain families. This Ph.D. project will build on these findings to characterise the identified sites by a variety of techniques including molecular dynamics simulations to identify which are most likely to affect molecular function. The project will focus initially on large protein repeat families (e.g. TPRs, Ankyrins, HEAT, Armadillo) which provide a strong signal for our new methods. Recent work in collaboration with co-supervisor Prof. Zachariae (Llabrés et al, 2019) have shown the power of this approach in conjunction with MD simulations and the project will build on this experience.
This project will train the student in software development and advanced bioinformatics research techniques including machine learning noSQL technology and statistics. On completion of the Ph.D. the student will be well prepared for a research career in bioinformatics, but also have excellent transferrable skills appropriate to careers in Big Data analytics or software engineering.
Stuart A MacGowan, Fábio Madeira, Thiago Britto Borges, Melanie S Schmittner, Christian Cole, and Geoffrey J Barton, (2017), “Human Missense Variation is Constrained by Domain Structure and Highlights Functional and Pathogenic Residues”, bioRxiv preprint, https://doi.org/10.1101/127050
Llabrés, Salomé, Tsenkov, M., I., MacGowan, S. A., Barton, G. J. and Zachariae, U. (2019), “Disease related single point mutations alter the global dynamics of a tetratricopeptide (TPR) alpha-solenoid domain”, Journal of Structural Biology, https://doi.org/10.1016/j.jsb.2019.107405