The aim of this project is to develop a classifier for the interpretation of immune-related sequence changes. The NLRP3 gene, which encodes a key regulator of IL-1 production, will be investigated as a case study. Mutations at this locus have been associated with a wide spectrum of autoinflammatory conditions, which will provide an opportunity to correlate the structural impact of disease alleles with the severity of clinical phenotypes.
A multi-disciplinary approach will be adopted, whereby the student will be co-supervised by two experienced investigators with complementary expertise in genetics/functional genomics (Francesca Capon) and structural biology/machine learning (Franca Fraternali).
Thus, the objectives of the PhD will be pursued as follows:
1. Modelling the impact of NLRP3 sequence variants on the three-dimensional structure of protein-protein complexes.
The student will use state of the art computational approaches to model the interaction between NLRP3 and its binding partners.
He/she will then examine the impact of known NLRP3 mutations on these interactions, comparing the effects of changes that are associated with different clinical phenotypes.
While this work will be carried out in the Capon lab, the student will have regular meetings with Prof Fraternali and her team.
2. Experimentally validating selected computational predictions
The student will generate mutagenized NLRP3 constructs, which they will express in mammalian cell lines. They will then examine the interaction between mutant NLRP3 and its binding partners. They will also measure the effects of various mutations on the production of IL-1, correlating IL-1 upregulation with the severity of the phenotype associated with each allele.
This work will be carried out in the Capon lab, where the relevant laboratory protocols are well established. The student will also continue to interact with the Fraternali group and will present their findings at the Fraternali lab meetings.
3. Develop a machine learning classifier to predict the effects of amino acid changes on protein-protein interactions.
The student will use the data generated in the first part of the project to train a machine learning classifier. This will predict which protein-protein interactions are disrupted by a given variant.
This work will be carried out in the Fraternali group, where the student will be able to access the relevant computational expertise. He/she will also continue to have regular meetings with Prof Capon and her team.
These studies are expected to facilitate the interpretation of NLRP3 sequence variants and shed new light on the amino acid residues that are essential for NLRP3 immune function.
This work will enable the student to acquire important computational skills, while also mastering key laboratory techniques for the characterization of disease alleles.
Academic requirement:
Minimum Upper Second (above 60%) BSc degree in a Life Sciences or Computational subject, or an MSc in a relevant area.
English Language Requirements:
Band D
Application Process
It is highly recommended to informally approach Professor Francesca Capon before making an application. For administrative and application process enquiries please contact Helen Rudkin.
Please submit an application for the Basic and Medical Bioscience Research MPhil/PhD (Full-time) using the KCL online application form.
Before completing the application click here for information about the programme, requirements and details of what documentation and information needs to be included in your application.
Please include in your application:
· Details of previous employment where applicable
· A 500 word personal statement outlining your motivation for undertaking postgraduate research should be uploaded to the Supporting statement section.
· References: two supporting references are required with at least one academic. Professional references will be accepted if you have completed your qualifications over five years ago. Prospective supervisors should not provide a reference. Applicant must ensure that their chosen referees are made aware of the requirement to submit references before the application deadline.
Funding information: Please select option 5 ‘I am applying for a funding award or scholarship administered by King’s College London’ and under ‘Award Scheme Code or Name’ add 2023/BMBS/02. Failing to include this code might result in you not being considered for this funding.
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