AstraZeneca Pharmaceuticals LP Post Doc Fellow - Detection of hidden allosteric pockets in Gothenburg, Sweden

Postdoctoral Long Title: Computational exploration of protein motions to detect and functionally characterize hidden allosteric pockets: develop, validate and apply efficient molecular modelling approaches.

We’re currently looking for talented scientists to join our innovative academic-style Postdoc. From our center in Gothenburg, SE, you’ll be in a global pharmaceutical environment, contributing to live projects right from the start. You’ll take part in a comprehensive training program, including a focus on drug discovery and development, given access to our existing Postdoctoral research, and encouraged to pursue your own independent research in cutting edge laboratories. It’s a newly expanding program spanning a range of therapeutic areas across a wide range of disciplines.

What’s more, you’ll have the support of a leading academic advisor, who’ll provide you with the guidance and knowledge you need to develop your career. This is an exciting area that hasn’t been explored to its full potential, making this an opportunity to make a real difference to the future of medical science.

AstraZeneca (AZ) is a global, innovation-driven biopharmaceutical business that focuses on the discovery, development and commercialisation of prescription medicines for some of the world’s most serious diseases. But we're more than one of the world's leading pharmaceutical companies. At AZ, we're proud to have a unique workplace culture that inspires innovation and collaboration. Here, employees are empowered to express diverse perspectives - and are made to feel valued, energised and rewarded for their ideas and creativity.

Postdoc project description:

Nowadays, there are more than 30 allosteric modulators in clinical development, which indicates a growing interest of Pharma industry in allosteric control. At AZ, we have many projects targeting allosteric binding sites covering all our disease areas and all major target classes. However, so far, in most cases the discovery of allosteric sites comes as a surprise, rather than a result of dedicated efforts. The aim of the Postdoc project is to change this situation and develop a predictive approach for identification of allosteric pockets using modern computational techniques. A combined approach bridging Monte Carlo and enhanced MD has the high potential to become a game changer in this field. You will have the opportunity to work closely with experts in all relevant fields: crystallographers, biophysicists, computational chemists, etc. and you will have access to relevant AZ internal experimental data not elsewhere available and modern super computing facilities. All in all, we are offering a challenging and very important scientific problem together with the right working environment to make this a success.

This project will be supported by experienced computational chemists and crystallographers from AZ, who drive novel and recognized research in the field (see e.g. Refs. [1,2,3]). Also, you will get input from key academic experts. You'll update and seek input from the group at regular project meetings, will present to the wider community of AZ computational chemists, postdoc groups, and the senior leadership team. You will be further responsible for planning and writing of scientific papers in high impact journals and presenting the work in international conferences.

[1] Cheng et al. “Structural insight into allosteric modulation of protease-activated receptor 2”, Nature2017, 545 (7652), 112-115

[2] Grebner et al. “Exploring Binding Mechanisms in Nuclear Hormone Receptors by Monte Carlo and X-ray-derived Motions”, Biophysical Journal2017, 112 (6), 1147-1156

[3] Grebner et al. " Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design”, Journal of Chemical Information and Modeling 2016, 56 (4), 774-787

Essential Education and Experience Required:

  • PhD or similar degree in physical chemistry, (bio)-physics, molecular biology, molecular modelling or similar field

  • Solid knowledge of statistical thermodynamics and theory behind the molecular simulation methods

  • Ability to write scripts in Python, Shell, etc. and/or code in any of the programming languages

  • Knowledge of Linux and High Performance Computing facilities


You have experience with some of the following:

  • molecular simulations (MD, Monte-Carlo, etc.) of biomolecules

  • exploring conformational space of biomolecules: peptide, proteins, etc.

  • enhanced sampling techniques: metadynamics, umbrella sampling, replica exchange, etc. including knowledge of appropriate collective variables to enhance biomolecule conformational search

  • different biomolecular force fields

  • searching cryptic pockets in biomolecules

  • coding interfaces between different simulation packages

  • writing scripts/tools for automated simulation setup and trajectory analysis

Skills and capabilities required:

  • Scientific independence, with excellent time management skills, forward planning and delivery focus

  • Strong written and oral communication skills and ability to work well in multidisciplinary teams

  • Demonstrated track record of good-quality scientific publications

This is a 3 year programme. 2 years will be a Fixed Term Contract, with a 1 year extension which will be merit based. The role will be based in Gothenburg, Sweden with a competitive salary on offer. To apply for this position, please click the apply link below.

Advert opening date – 5th March 2018 / Advert closing date – 13th May 2018

AZ welcomes applications from all sections of the community. AZ is an equal opportunity employer. AZ will consider all qualified applicants for employment without discrimination on grounds of disability, sex or sexual orientation, pregnancy or maternity leave status, race or national or ethnic origin, age, religion or belief, gender identity or re-assignment, marital or civil partnership status, protected veteran status (if applicable) or any other characteristic protected by law.

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