Similarities and Differences in Ligand Binding to Protein and RNA Targets: The Case of Riboflavin.

It is nowadays clear that RNA molecules can play active roles in several biological processes. As a result, an increasing number of RNAs are gradually being identified as potentially druggable targets. In particular, noncoding RNAs can adopt highly organized conformations that are suitable for drug binding.

Probing allosteric communication with combined molecular dynamics simulations and network analysis.

Understanding the allosteric mechanisms within biomolecules involved in diseases is of paramount importance for drug discovery. Indeed, characterizing communication pathways and critical hotspots in signal transduction can guide a rational approach to leverage allosteric modulation for therapeutic purposes. While the atomistic signatures of allosteric processes are difficult to determine experimentally, computational methods can be a remarkable resource.

Computational drug discovery under RNA times.

RNA molecules play many functional and regulatory roles in cells, and hence, have gained considerable traction in recent times as therapeutic interventions. Within drug discovery, structure-based approaches have successfully identified potent and selective small-molecule modulators of pharmaceutically relevant protein targets. Here, we embrace the perspective of computational chemists who use these traditional approaches, and we discuss the challenges of extending these methods to target RNA molecules.

Molecular Dynamics Simulations with Grand-Canonical Reweighting Suggest Cooperativity Effects in RNA Structure Probing Experiments.

Chemical probing experiments such as SHAPE are routinely used to probe RNA molecules. In this work, we use atomistic molecular dynamics simulations to test the hypothesis that binding of RNA with SHAPE reagents is affected by cooperative effects leading to an observed reactivity that is dependent on the reagent concentration. We develop a general technique that enables the calculation of the affinity for arbitrary molecules as a function of their concentration in the grand-canonical ensemble.

Simultaneous refinement of molecular dynamics ensembles and forward models using experimental data.

A novel method combining the maximum entropy principle, the Bayesian-inference of ensembles approach, and the optimization of empirical forward models is presented. Here, we focus on the Karplus parameters for RNA systems, which relate the dihedral angles of γ, β, and the dihedrals in the sugar ring to the corresponding 3J-coupling signal between coupling protons. Extensive molecular simulations are performed on a set of RNA tetramers and hexamers and combined with available nucleic-magnetic-resonance data.