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New computational approaches to characterise and solve multi-crystal diffraction datasets of transmembrane proteins from the VMXm beamline

Project Description

Transmembrane proteins (TMPs) bring particular challenges to the structural biologist throughout structure determination. This joint University of Liverpool-Diamond project leverages recent advances, in TMP multi-crystal data collection using micron sized X-ray beams and in structural bioinformatics methods, aiming to increase the efficiency of TMP structure determination. The student will spend approximately equal time in each location.

At Liverpool, the student will work at the interface between structural bioinformatics and experimental structural biology, devising software solutions to help solve more TMP targets by Molecular Replacement (MR). These solutions will exploit the recent availability of evolutionary covariance-based predictions of contacting residue pairs. This additional information, readily obtained for targets in medium to large protein families, will drive three routes of unconventional MR. First, TMP-specific contact predictors will be used to build better ab initio models. Secondly, an innovative fragment-based approach to MR will be explored, using contact predictions to select promising structural fragments, containing multiple secondary structure elements, to act as MR search models. Thirdly, emerging software to align (predicted) contact maps will be used to match (regions of) targets to (regions of) PDB entries, identifying structural units, distantly related or unrelated, that may serve as search models.

At Diamond, the student will explore effective methods for the measurement and analysis of TMP diffraction data using both X-ray and electron diffraction approaches. This will encompass TMP sample mounting, data collection and data analysis. The student will devise and characterise novel metrics describing data quality, multiplicity and its distribution in reciprocal space, and test their relationship to solvability using the novel MR methods developed in Liverpool. The results will also feed into ongoing Machine Learning-based efforts to predict target tractability from characteristics of the crystal system itself and the data it yields.

The training element of the project is strong, from software development through to use of cutting edge infrastructure at Diamond - the VMXm beamline and Electron Diffraction facilities.

Funding Notes

This is a fully-funded four-year project, open to UK and EU students only, commencing October 2018. It pays an enhanced stipend of around £15,800 rising with inflation. Informal enquiries may be made to Dan Rigden () or Gwyndaf Evans (). Applications may be made below.


1: Simkovic F, Thomas JMH, Rigden DJ. ConKit: a python interface to contact predictions. Bioinformatics. 2017 33:2209-2211.

2: Simkovic F, Thomas JM, Keegan RM, Winn MD, Mayans O, Rigden DJ. Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds. IUCrJ. 2016

3: Mylona, A., Carr, S., Aller, P., Moraes, I., Treisman, R., Evans, G., and Foadi, J. A Novel Approach to Data Collection for Difficult Structures: Data Management for Large Numbers of Crystals with the BLEND Software. Crystals 2017 7:242.

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