The proteome social network: Characterizing cellular protein interaction networks using advanced mass spectrometry and deep learning

• Funding – self-funded/externally sponsored applicants   (PhD Fees can be found here)
• Applications are accepted year round
• Standard Entry dates – January and September
• Applicants are expected to have a degree (equivalent of Honours or Masters) in a relevant discipline.

Proteins are building blocks of the body and the engines of life. They can be structural, like hair and skin, or mechanical, like machines that perform chemical transformations and tasks within cells. For this reason, proteins are the target of most medicines used to treat disease. Understanding how they interact to perform their functions is an enormous research challenge. One cell can contain more than 10,000 different types of protein and they rarely work in isolation. Instead, proteins form complexes with each other to carry out their roles within the body. Deepening our understanding of how cells work in health and disease requires us to measure which proteins are present in cells, in what amounts, and how they interact. The importance and expected impact of the project is to discover key protein-protein interactions that are potential targets for future drug development to treat human disease.  

This PhD project aims to 1) develop a rapid and robust method to characterize protein interaction networks in cells, and 2) apply this novel approach to study protein interaction dynamics during cell cycle entry. 

Aim 1. In the first two years, the PhD student will combine native protein chromatography, including size exclusion and ion exchange chromatography, LC-MS/MS-based proteomics, and deep learning-driven structural modelling to establish a novel pipeline for rapid analysis of cellular protein interaction networks. The student will gain expertise in the latest Orbitrap Astral technology, which enables high throughput analysis of proteomes. 

Aim 2. The student will apply the pipeline to characterize the dynamic evolution of protein interaction networks during a regulated physiological process. The quiescence-to-proliferation state switch is crucial for tissue renewal and organismal homeostasis and frequently dysregulated in human disease (e.g., cancer). In years 3 and 4, the student will apply the novel proteomic technology to characterize how protein interaction networks dynamically evolve during cell cycle entry in human cells. The student will validate novel interactions using co-IP, bimolecular complementation and structural biology approaches and probe the cellular functions of protein-protein interactions using chemical genetics. 

The candidate will have opportunities to gain expertise in proteomics, ‘big data’ analysis and mechanisms of cell cycle control. Making use of cutting-edge mass spectrometry instrumentation available at the University of Dundee and the expertise of the host laboratories, the successful PhD candidate will be well positioned to advance the state-of-the-art in protein analysis. 

Our research community thrives on the diversity of students and staff which helps to make the University of Dundee a UK university of choice for postgraduate research.  We welcome applications from all talented individuals and are committed to widening access to those who have the ability and potential to benefit from higher education.