Société Française de Bioinformatique

Mots-Clés

RBA, drug-design

Description

RNA is a highly flexible macromolecule whose structure is strongly influenced by its environment (ions, pH, etc.) and can adopt multiple conformations (polymorphism). Understanding and predicting this structural diversity requires statistical physics, molecular modeling, and computational simulations, making physics central to modern RNA-targeted drug discovery.
Our research group aims to develop structure-based approaches to explore RNA molecules and guide drug design by identifying potential small molecules capable of forming stable complexes. This involves characterizing ensembles of RNA structures and defining RNA-ligand interaction profiles using the growing body of experimental RNA structural data.
The internship project builds on our recently developed binding site predictor based on Statistical Molecular Interaction Fields (SMIFs), which identifies druggable RNA pockets from their physico-chemical interaction landscape. The student will use SMIFs to characterize the dynamic behavior of pockets across RNA conformational ensembles, obtained from existing simulations as well as from new simulations the student will run: how pockets appear, disappear, reshape, and change their interaction profile as the RNA explores different conformations and environmental conditions. The goal is to identify pockets whose interaction features are persistent or conformationally selected, and are therefore the most promising targets for small molecules.
In collaboration with Pr. Vladimir Reinharz (Université du Québec à Montréal), the project will also link SMIFs to RNA structural motifs at the 2D and 2.5D levels, i.e. secondary structure elements and the networks of non-canonical interactions that organize them. Relating interaction field signatures, and their dynamics, to the motifs that frame a pocket will help connect SMIFs to signals that can be inferred directly from sequence and predicted secondary structure, opening a route toward anticipating druggable regions without a 3D model. Part of the internship (1 to 2 months) can be carried out in Pr. Reinharz’s laboratory in Montréal, with the stay funded by the Université du Québec.
The project is highly interdisciplinary, combining computational physics, bioinformatics, and computational biology, and is led by Pr. Samuela Pasquali, a computational physicist. The student will collaborate with physicists, computational biologists, and bioinformaticians in Paris and Montréal, gaining hands-on experience in molecular simulations, RNA polymorphism, and environmental effects.
We are seeking candidates in computational biophysics, theoretical chemistry, or computational biology, with strong Python programming skills and some experience with molecular simulations. Interested candidates should send a CV and motivation letter to Pr. Samuela Pasquali (samuela.pasquali@u-paris.fr). One recommendation letter should be sent directly by a previous internship supervisor or head of the master program.