RRM data integration and analysis

Ph.D. job offer: Collect, integrate and analyse data on RNA recognition motifs (RRMs).

ESR 3: RRM data integration and analysis

This PhD project focuses on two main axes:
1. The creation of a complete and comprehensive database of available RRM information from the many available RRM data covering a broad range of behaviours (with initial help of the other PhD students in the project). This includes their sequence, structure, dynamics, RNA specificity and other data (binding affinity, biological function…). This database will be regularly extended with internal and external data as it becomes available, will be released at the end of the project, and is key to the development of computational approaches in the RNAct project. You will analyse these different RRM data, and will liaise with the other PhD students to enrich the data with results from in silico methodologies.
2. The computation of protein-RNA binding energies by molecular dynamics simulations of RRM-RNA models obtained by your fellow- PhD students. For example, he/she will investigate why the Drosophilia Sex-lethal (Sxl) protein, and its putative human homologue HuR, bind more specific Py-tracts than the U2AF65 protein and cannot accommodate cytosine.

You will spend 3 month of secondments at the VUB (Brussel, Belgium) to learn about sequence-based methods for protein design and analysis.

The project is highly interdisciplinary: the day-to-day work involves a lot of programming on atomic representations of proteins and nucleic acids. Good programming skills (preferentially Python and/or C++) are essential. Knowledge of structural biology is very desirable, skills in discrete mathematics and statistics would be appreciated. Most importantly, candidates must be motivated to learn about all disciplines relevant to the project.

Candidates must be fluent either in French or in English.

More detailed information available here.


H2020-MSCA-ITN-2018 813239 (Jan. 1, 19-Dec. 31, 23)
RNAct - Enabling proteins with RNA recognition motifs for synthetic biology and bio-analytics