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Developing reliable ab-inito software for the interpretation of protein structure from BioSaxs data - EPSRC Centre for Doctoral Training (CDT) in Molecular Sciences for Medicine (MoSMed)

  • Full or part time
  • Application Deadline
    Friday, March 06, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Biological small-angle X-ray scattering (BioSaxs) is an important method for determining protein structure. Data interpretation in this field is challenging, requiring forward modelling of the protein’s shape to make predictions.

The project will build on theoretical techniques pioneered by the primary supervisor to develop consistent and accurate methods for identifying protein structures. It is highly interdisciplinary with cutting-edge mathematical and experimental components.

Research Project
The forward model, developed by Prior, utilises a parsimonious discrete curve description, with discrete differential-geometrical constraints to mimic the stereochemisty of amino acid chains. This will be paired with the pioneering scattering data analysis techniques of Rambo, to develop the first reliable and widely available ab-inito tertiary structure prediction software for BioSaxs.

Initial target molecules (Davies lab)
First, MAJIN-TERB2, which leads to conformational change from its solved crystal structure. Second, a region of Mei-P22, an unknown structure. Both play crucial roles in meiosis.

Research plan
The student will complete training courses in experimental SAXS techniques at the Diamond light source, and collect the data use to guide the theoretical modelling.

Theoretical work plans:
• Modelling experimental noise. This comes from experimental error and random protein motion/polymerisation. The student will develop statistical models for these sources (using Python, and C++).

• Developing improved search algorithms
The student will learn to apply Bayesian sampling techniques, so the hard to navigate the protein fold space is explored comprehensively and parsimoniously.

• Development of automated post-search structural assessment The student will develop methods to rate the quality of these predictions. First, by translating the model predictions into the Rosetta computational protein modelling suite to generate assessable protein models. Second, by applying topological metrics (from knot theory) for classifying and comparing predictions.
Training & Skills

Durham & Newcastle
• Training in the use of statistical techniques such as Bayesian learning.
• Knowledge of cutting edge Topological and global geometrical metrics used to classify and compare known and new protein structures.
• Optimization and mathematical programming (C++ and Python).

• World-class training in state-of-the-art synchrotron measurements using BioSaxs.
• Implementation of a web-accessible algorithms.
• Participation in BioSaxs training workshops.

Funding Notes

The award is available to UK/EU applicants only. Depending on how you meet the EPSRC’s eligibility criteria, you may be entitled to a full or a partial award.


Applicants should hold or expect to receive an excellent Masters level degree in a relevant subject. An excellent Bachelors degree with relevant experience will also be considered.

For more information regarding MoSMed and a full description of the project please visit the webpage: Please note that MoSMed is a joint venture between Durham and Newcastle Universities.

When making an application, please select the course code F1A201 and quote the project title and reference number MoSMed20-11.

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