Machine Learning and Molecular Modelling in Mass Spectrometry

   Faculty of Biological Sciences

  , ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

This PhD project will harness the power of computational modelling and machine learning (A.I.) to analyse data obtained by mass spectrometry experiments and predict structural characteristics of biomolecules and their interactions

Your research will combine experiment with theory in a highly interdisciplinary field comprising Analytical Biochemistry, Computational Modelling and A.I., with supervisors representing this spectrum at Leeds and University College London (UCL, Wang). You will explore how different types of mass spectrometry data of biomolecules, such as metabolites, proteins or their complexes, can be interpreted by state-of-the-art computational methods. You will gain systematic insights into structural characteristics of these molecules that govern their analytic behaviour, and develop machine learning approaches based on appropriate training sets. With this powerful approach, deeper molecular insights and automation of data analysis become a possibility. 

In this PhD project, you will be based in the Mass Spectrometry group (Sobott) in the School of Molecular and Cellular Biology and collaborate closely with the Computational Modelling group (Kalli) in the School of Medicine and an A.I. expert (Wang, now at UCL). The three supervisors have previously collaborated on analysis and predictions of lipid interactions with membrane proteins (, and their current research includes predictions of PIEZO ion channel structure and the development of mass spectrometry methods for the characterization of protein-lipid interactions and their structural effects. Another PhD project in the Sobott group in collaboration with industrial partners investigates the use of ion mobility-mass spectrometry and fragmentation for the comprehensive characterization of drug-like molecules and their metabolites, with the aim to develop machine learning models and accelerate data interpretation.

 The current project will target structural interpretation of biomolecules and their interactions, for a specific class of proteins (e.g. kinases) and their ligand interactions, but the precise area of research can be defined in discussion with the candidate. You will acquire multi-dimensional mass spectrometry data with particular focus on retention times (liquid chromatography), charge states, collision cross sections (ion mobility) and fragmentation behaviour. It is possible to also link other types of structural MS data (e.g. hydrogen-deuterium exchange) with structural properties of the molecular targets. You will have access to a range of cutting-edge mass spectrometers at Leeds including LC-MS/MS, native/ion mobility, HDX and covalent labelling (Fast Photochemical Oxidation of Proteins, FPOP), with the group focused on developing new MS methods and applying them to biomolecular structure. The Kalli group has expertise in molecular dynamics simulation and molecular modelling of proteins. These computational techniques allow us to follow the dynamics of proteins over time which provides details at the molecular level about their function. In the Kalli group the student will have the opportunity to use molecular simulations at both the atomistic and the coarse-grained resolution. Wang’s group focuses on machine learning and its applications in areas including computer graphics, computer vision, computation physics, etc. The student will have access to expertise in a wide range of machine/deep learning models and explore how they can be applied in combination with molecular dynamics.

You will also be a member of the Astbury Centre of Molecular Structural Biology which combines broad expertise on “Life in Molecular Detail” from more than 60 different research groups across the University campus. Leeds is the capital city of Yorkshire in the North of England, ca. 2h away from London, and well known for its cultural and party life. Nearby are the medieval City of York as well as three National Parks, the Peak District to the South and the Yorkshire Dales and the Yorkshire Moors with their beautiful coastline to the North.   


Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline.

Applicants whose first language is not English must provide evidence that their English language is sufficient to meet the specific demands of their study. The Faculty of Biological Sciences minimum requirements in IELTS and TOEFL tests are:

  • British Council IELTS - score of 6.0 overall, with no element less than 5.5
  • TOEFL iBT - overall score of 87 with the listening and reading element no less than 20, writing element no less than 21 and the speaking element no less than 22. 

How to apply

To apply for this project applicants should complete an online application form and attach the following documentation to support their application. 

  • a full academic CV
  • degree certificate and transcripts of marks
  • Evidence that you meet the University's minimum English language requirements (if applicable)
  • Evidence of funding

To help us identify that you are applying for this project please ensure you provide the following information on your application form;

  • Select PhD in Biological Sciences as your programme of study
  • Give the full project title and name the supervisors listed in this advert

For information about the application process please contact the Faculty Admissions Team:


Biological Sciences (4) Chemistry (6) Mathematics (25)

Funding Notes

This project is open to applicants who have the funding to support their own studies or who have a sponsor who will cover these costs.

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