This project will build on our knowledge of how parasitic helminths manipulate the host immune response, in a collaboration between the groups of Dr Henry McSorley (University of Dundee) and Dr Hermelijn Smits (Leiden University Medical Centre, the Netherlands). The successful candidate for this 4 year PhD position will spend significant time in both Dundee and Leiden, to identify, characterise and translate their findings.
The School of Life Sciences at the University of Dundee is a world-class academic institution with a reputation for the excellence of its research, its high quality teaching and student experience, and the strong impact of its activities outside academia. With 900 staff from over 60 countries worldwide the School provides a dynamic, multi-national, collegiate and diverse environment with state-of-the-art laboratory, technology and teaching facilities.
Degrading proteins in a timely manner to dispose of misfolded and damaged proteins is essential for a healthy cell. In ageing cells and organisms, there is a deterioration in the ability of cells to clear proteins resulting in the accumulation of misfolded proteins. Deposition of misfolded protein aggregates is a hallmark of many neurodegenerative diseases. It is not understood why quality control systems and the degradation capacity of a cell decline with age.
Membranes and their protein organization are a frontier in our understanding of cell biology. We will focus on polarized trafficking and asymmetric cell division as a model to uncover fundamental mechanisms in biology. This project aims to answer mechanistic questions in 1) the regulation of protein structural mechanics in polarized trafficking, 2) and the consequences and fundamental differences in this pathway’s organization between distinct tissues in development. Our philosophy is to address big-picture questions of challenging biology in a hypothesis-driven research 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 has included work to characterise and predict protein-protein interactions from 3D structural information as well as from sequences and related data. Much of this experience is encapsulated in widely used software tools that include the Jalview (www.jalview.org) sequence analysis workbench which has over 70,000 regular users world-wide and JPred (
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
Receptor-like kinases are the principal means by which plants perceive their physical extracellular environment. As a result Receptor-like kinases regulate many aspects of development, pathogen perception, interaction with nodulating bacteria and cell wall remodelling and as a result are of particular interest for improving plant responses to environmental perturbations such as climate change and emerging pathogens or improving food yield.
Self-assembly of larger protein networks is a central feature of replicating systems from viral capsids to the cytoskeleton that gives cells structure and polarity. One important example is the nuclear lamina, a subset of the cytoskeleton responsible for nuclear structural integrity, controlling the demarcation between active and inactive chromatin and the developmental control of gene expression programs.
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 has included work to characterise and predict protein-protein interactions from 3D structural information (e.g. ) as well as from sequences and related data (e.g. [2, 3]).
This project should appeal to a student who will most likely have a background in computer science or other subject with strong experience in algorithm development. They will extend their skills to the challenging problem of visualising ‘big data’ in biology. They will also gain experience of communicating their achievements to a wide biological research audience through distribution of their software to a large user-base as well as conventional seminars and meetings.