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Applications are invited for two Postdoc positions available in spring 2016 in the Computational Pharmaceutical Chemistry & Molecular Bioinformatics group (Prof. Dr. Holger Gohlke; at the Heinrich-Heine-University, Düsseldorf, Germany.

Background: A quantitative description of allostery is fundamental to an understanding of processes in living systems and of practical relevance when developing allosteric modulators. The Gohlke group has developed Constraint Network Analysis (CNA) as a framework to analyze and predict allosteric sites in proteins.[1-3] The CNA approach analyzes the network coupling between putative binding sites by rigidity theory.
CNA applies concepts grounded in rigidity theory to analyze biomolecular flexibility [4] and how it might be perturbed by compound binding. The approach works on conformational ensembles [5] or ensembles of network topologies, [6] considering dynamics of molecules in an efficient way based on key intramolecular and intermolecular interactions (hydrophobic, H-bond, salt bridge constraints), without the need for very long molecular dynamics simulations. CNA is applied in a perturbation approach to gain structure-based insights into allosteric signaling and coupling in dynamic proteins. It is fast enough, for example, that one could perform an Alanine scan across the entire protein to identify which residues’ mutations might show the greatest perturbation at a given active site.
The Gohlke group already validated this approach against several targets. Thus, CNA is a useful tool for identifying novel allosteric sites and analyzing the effects of compound binding at these sites.

Objective: In collaboration with a major pharmaceutical company, we aim to extend and integrate the CNA approach into a virtual screening workflow to find novel chemical matter that would bind and appropriately perturb the identified allosteric sites to achieve desired pharmacological effects. In doing so, we will also fill a major gap in ligand-based virtual screening and enable automatic structure-based pharmacophore searches for targets.

Requirements: Ideal candidates will have a record of excellence (PhD plus publications in highly visible journals) and a high interest in working in an industrial collaboration. Postdoc 1 is required to have a strong chemoinformatics background and application-based skills; Postdoc 2 is required to have a strong structural bioinformatics / computational biochemistry-based background and programming skills in Python and C++.

Applicants should submit applications (a one-page letter of motivation why they are interested in the respective project and how they can contribute to the project’s success, a current CV, and contact data of three references) by email to Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein! . Please provide all documents as one PDF file and specify for which position you apply.

Detailed information about living and studying in Düsseldorf is provided here:

[1]    Pfleger, C., Rathi, P. C., Klein, D. L., Radestock, S. & Gohlke, H. Constraint Network Analysis (CNA): A Python Software Package for Efficiently Linking Biomacromolecular Structure, Flexibility, (Thermo-) Stability, and Function. J. Chem. Inf. Model. 53, 1007-1015 (2013).
[2]    Rathi, P. C., Mulnaes, D. & Gohlke, H. VisualCNA: A GUI for interactive Constraint Network Analysis and protein engineering for improving thermostability. Bioinformatics 31, 2394-2396 (2015).
[3]    Kruger, D. M., Rathi, P. C., Pfleger, C. & Gohlke, H. CNA web server: rigidity theory-based thermal unfolding simulations of proteins for linking structure, (thermo-)stability, and function. Nucleic Acids Res. 41, W340-348 (2013).
[4]    Pfleger, C., Radestock, S., Schmidt, E. & Gohlke, H. Global and local indices for characterizing biomolecular flexibility and rigidity. J. Comput. Chem. 34, 220-233 (2012).
[5]    Rathi, P. C., Radestock, S. & Gohlke, H. Thermostabilizing mutations preferentially occur at structural weak spots with a high mutation ratio. J. Biotechnol. 159, 135-144 (2012).
[6]    Pfleger, C. & Gohlke, H. Efficient and robust analysis of biomacromolecular flexibility using ensembles of network topologies based on fuzzy noncovalent constraints. Structure 21, 1725-1734 (2013).

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