Interrogating RNA–Small Molecule Interactions with Structure Probing and Artificial Intelligence-Augmented Molecular Simulations

Abstract

While there is increasing interest in the study of RNA as a therapeutic target, efforts to understand RNA-ligand recognition at the molecular level lag far behind our understanding of protein−ligand recognition. This problem is complicated due to the more than 10 orders of magnitude in time scales involved in RNA dynamics and ligand binding events, making it not straightforward to design experiments or simulations. Here, we make use of artificial intelligence (AI)-augmented molecular dynamics simulations to directly observe ligand dissociation for cognate and synthetic ligands from a riboswitch system. The sitespecific flexibility profiles from our simulations are compared with in vitro measurements of flexibility using selective 2′ hydroxyl acylation analyzed by primer extension and mutational profiling (SHAPE-MaP). Our simulations reproduce known relative binding affinity profiles for the cognate and synthetic ligands, and pinpoint how both ligands make use of different aspects of riboswitch flexibility. On the basis of our dissociation trajectories, we also make and validate predictions of pairs of mutations for both the ligand systems that would show differing binding affinities. These mutations are distal to the binding site and could not have been predicted solely on the basis of structure. The methodology demonstrated here shows how molecular dynamics simulations with allatom force-fields have now come of age in making predictions that complement existing experimental techniques and illuminate aspects of systems otherwise not trivial to understand.

Targeting Riboswitches with Beta-Axial-Substituted Cobalamins

“…48 Disagreement between the results of SHAPE and another method has been seen previously; the Tte-PreQ 1 riboswitch did not show an altered SHAPE signature for synthetic ligand binding despite a change in RNA flexibility suggested by molecular dynamics simulations. 49 The inconsistency between our results suggests that SHAPE may not be ideal for screening weakly binding compounds during initial phases of discovery of an RNA targeting compound. These observations highlight the utility of parallel approaches to assess binding in novel systems.…”

Metadynamics simulations for the investigation of drug loading on functionalized inorganic nanoparticles

“…A summary of the MetaD parameters of all the simulations is provided in Table S1 in the ESI. Similar parameters were used in protein-ligand or RNA-ligand MetaD simulations 42,[73][74][75][76] . The convergence of the simulations was assessed by estimating the free energy difference between the bound state (d between 2 Å and 18 Å) and the unbound state (d greater than 25 Å).…”

Targeting riboswitches with beta-axial substituted cobalamins

“…49 Disagreement between the results of SHAPE and another method has been seen previously; the Tte-PreQ1 riboswitch did not show an altered SHAPE signature for synthetic ligand binding despite a change in RNA flexibility suggested by molecular dynamics simulations. 50 The inconsistency between our results suggests that SHAPE may not be ideal for screening weakly binding compounds during initial phases of discovery of an RNA targeting compound. These observations highlight the utility of parallel approaches to assess binding in novel systems.…”