The ongoing pandemic caused by SARS-CoV-2 has called for concerted efforts to generate new insights into the biology of betacoronaviruses to inform drug screening and development. Here, we establish a workflow to determine the RNA recognition and druggability of the nucleocapsid N-protein of SARS-CoV-2, a highly abundant protein crucial for the viral life cycle. We use a synergistic method that combines NMR spectroscopy and protein-RNA cross-linking coupled to mass spectrometry to quickly determine the RNA binding of two RNA recognition domains of the N-protein. Finally, we explore the druggability of these domains by performing an NMR fragment screening. This workflow identified small molecule chemotypes that bind to RNA binding interfaces and that have promising properties for further drug development.

This deposition contains the NMR data acquired to determine the structural features of RBDs- RNA recognition as well as selected relevant data regarding the characterization of promising molecular fragments to disrupt protein-RNA interaction.

Furthermore, we included the molecular docking files used to obtain the reported structural model.

The NMR folder contains raw data acquired in this study. In order to visualise this data, a processing software such as Topspin or similar is required. Please refer to the README file inside the folder to relate experiment name_number with data shown in the manuscript.

The Docking folder contains the output data obtained from Haddock for protein-RNA and protein-ligand docking. Please consult the Haddock manual for interpreting these files (https://www.bonvinlab.org/software/haddock2.4/manual/). Folders are named based on Figures reported in the manuscript. 

Representative models (lowest energy conformation of most representative clusters) are also included as pdb files in a separate folder (Representative models) to facilitate access.