The proteome social network: characterizing cellular protein interaction networks using advanced mass spectrometry and machine learning

• Funding – BBSRC funded 4 year studentship, providing tuition fees, training/research costs and an annual stipend at the UKRI rate, currently £21,805
• Start Date – September 2027
• 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. Comparing how proteins behave in healthy versus diseased cells is vital for discovering new drug therapies.

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 and cellular senescence.

Aim 1. The first two years of 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). Aging is associated with cellular senescence, a pro-inflammatory phenotype characterised by cell cycle withdrawal. 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 and in senescence.

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 PhD student 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.