Babault, N. | Search bioRxiv

bioRxiv2022

Targeting RNA:Protein Interactions with an Integrative Approach Leads tothe Identification of Potent YB-1 Inhibitors

RNA-binding proteins are promising targets for developing new molecules of therapeutic interest. Nevertheless, targeting RNA:Protein interfaces is hampered by the lack of methods able to detect these interactions in cells while being amenable to High Content Screening. Here, we adapt the microtubule bench assay to score small molecules targeting interactions of endogenous mRNA with a specific protein in cells and demonstrate its robustness by targeting YB-1 (YBX-1 gene), a mRNA-binding protein involved in cancer progression and resistance to chemotherapy. The implementation of an integrative approach led to the identification of 22 hits validated by NMR and MD simulations of which 11 were found to significantly interfere with the binding of mRNA to YB-1 in cells at low micromolar concentrations. One of our leads is P1, an FDA-approved PARP-1 inhibitor. This work shows the potential of our integrative approach and paves the way for the development of RNA:Protein Interaction inhibitors.

biophysics↗

bioRxiv2018

The Dynamic Conformational Landscapes of the Protein Methyltransferase SETD8

Elucidating conformational heterogeneity of proteins is essential for understanding protein functions and developing exogenous ligands for chemical perturbation. While structural biology methods can provide atomic details of static protein structures, these approaches cannot in general resolve less populated, functionally relevant conformations and uncover conformational kinetics. Here we demonstrate a new paradigm for illuminating dynamic conformational landscapes of target proteins. SETD8 (Pr-SET7/SET8/KMT5A) is a biologically relevant protein lysine methyltransferase for in vivo monomethylation of histone H4 lysine 20 and nonhistone targets. Utilizing covalent chemical inhibitors and depleting native ligands to trap hidden high-energy conformational states, we obtained diverse novel X-ray structures of SETD8. These structures were used to seed massively distributed molecular simulations that generated six milliseconds of trajectory data of SETD8 in the presence or absence of its cofactor. We used an automated machine learning approach to reveal slow conformational motions and thus distinct conformational states of SETD8, and validated the resulting dynamic conformational landscapes with multiple biophysical methods. The resulting models provide unprecedented mechanistic insight into how protein dynamics plays a role in SAM binding and thus catalysis, and how this function can be modulated by diverse cancer-associated mutants. These findings set up the foundation for revealing enzymatic mechanisms and developing inhibitors in the context of conformational landscapes of target proteins.

biophysics↗