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A better model for structures of biological macromolecules determined by X-ray crystallography

  • Full or part time
  • Application Deadline
    Sunday, March 01, 2020
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

For over 50 years, X-ray crystallography has been the primary method to determine the structures of biological macromolecules. However, the atomic structure is not the direct outcome of the experiment: prior knowledge of macromolecular structures is used in most steps between measurement and the final molecular model. Consequently, our structures are only as good as our fundamental understanding of their nature - and our ability to express this in structural models and the methods employed.

The discrepancy between X-ray data and structural models is usually given as a percentage called the R-value. While small molecule structures routinely reach R-values of 5%, macromolecular structures typically are at 20%-25%. These relatively high discrepancies between data and model are the main reason why some relevant biological questions - for example, whether a ligand is bound - cannot be answered and why some structures cannot be solved at all, in particular membrane proteins and large complexes, where often only low-resolution data are available.

Improvements are hindered by the fact that we use model phases to generate maps: this introduces a strong model bias, and hence we cannot observe what we do not anticipate in the maps. In this project, model-bias free maps will be used to overcome this challenge and to find out what is missing from our models. This knowledge will then be used to improve how structures of biological macromolecules are modeled, which will give lower R-values and consequently allows us to see more detail in new and over 100 000 existing structures. These improvements could enable structural biologists worldwide to solve more structures and to adress more challenging biological problems.

This rewarding and challenging research project will allow you to acquire skills in applied structural biology and its theoretical foundations as well as data analysis including programming and machine learning. You will have access to state-of-the-art equipment and our group is well-connected; you will have the opportunity to visit several international collaboration partners. Through the Graduate School for Life Sciences you will have a personal thesis committee to guide you and benefit from their extensive transferable skill program. You will get access to national and public-service pension schemes (VBL), health care, and 30 days of holiday. The JMU Welcome Center supports international candidates with language classes and practical advice - and the University also offers support for researchers with children.

You should have a M.Sc. or equivalent in a relevant subject or be due to complete your studies within 2 months of applying. We are looking for someone with a good working knowledge of Linux and Python and a basic understanding of crystallography (irrespective of the field, e.g. mineralogy, structural physics, biology or chemistry). Skills in statistical or image analysis, C++ and previous experience with machine learning are a bonus. You should be able to develop your own ideas, and have the necessary skills to successfully drive and complete a research project. You should be a good communicator and a good command of English is a prerequisite. You will be expected to present your work both at in-house and international meetings, and to contribute on occasion to teaching and public outreach.

Your application should state why you are interested in this position specifically and the CV should detail any data analysis, programming and crystallographic qualifications, include diplomas/certificates and three addresses of referees.

If you have any further questions, please do not hesitate to contact us!

Funding Notes

We offer a 36-month 65%-position at the Rudolf-Virchow Center of the University Würzburg, with a very competitive TV-L salary, funded as part of the DFG project “Towards a better understanding of macromolecular X-ray structures”.
Female scientists are particularly encouraged to apply.
Disabled applicants will be preferentially considered in case of equivalent qualification.


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