The structural modelling of protein-ssRNA complexes poses a significant challenge due to the inherent flexibility of ssRNA. The state-of-the-art fragment-based docking method, ssRNA’TTRACT, is hindered by sampling and scoring problems, i.e. failure to generate correct models and inaccurate ranking, respectively. Both problems stem mainly from the protein-RNA parameters of the ATTRACT scoring function being not ssRNA-specific. This thesis aims to advance protein-ssRNA docking by addressing both problems.

A major accomplishment of this work is the development of the Histogram-Based Pseudo Potential (HIPPO), a novel protein-ssRNA coarse-grained scoring function that combines four scoring potentials. HIPPO effectively accounts for the various protein-ssRNA binding modes, mitigating the scoring problem in docking. The versatility of the workflow to derive HIPPO allows its application to other ligands, such as ssDNA and long peptides.

Many ssRNA binding proteins contain conserved RNA-Recognition Motifs (RRM). To tackle the sampling problem in RRM-ssRNA complexes, structural knowledge has been leveraged to create the data-driven docking pipeline ‘RRM-RNA dock.’ Beyond improving sampling, this pipeline serves as a user-friendly tool for RRM-ssRNA fragment-based data-driven docking. It holds the potential for expansion into a more generalised protein-ssRNA docking tool.

• Juan Cortes – LAAS-CNRS, Paris, France
• Pablo Chacon – Institute of Physical Chemistry, Spain
• Piotr Setny – University of Warsaw, Poland

• Martin Zacharias – Technical University of Munich, Germany
• Samuela Pasquali – Université Paris Cité, France

• Isaure Chauvot de Beauchêne – Université de Lorraine, France
• Malika Smaïl-Tabbone – Université de Lorraine, France