RNA–ligand molecular docking: Advances and challenges

Abstract

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating in vivo drug efficacy. More recently, the rise of deep-learning approaches has led to new tools for predicting RNA-small molecule binding. In this review, we present an overview of the recently developed computational methods for RNA-ligand docking and their advantages and disadvantages.

In vitro Analytical Techniques as Screening Tools to investigate the Metal chelate‐DNA interactions

“…It is seen that the type of interaction is intermolecular hydrogen bonding or electrostatic, between the oxygen atom of the phosphate group of DNA and the outside edge of the complexes. From the data, it is easy to conclude that the complexes tend to interact with the DNA via suitable binding sites that are available on the minor groove,as shown in article [143] …”

“…There are nearly 60 different docking tools and programs available to predict the mode of binding established between the mtal complexes and the DNA.Tools such as DOCK, AutoDock 4.0, [129] FlexX, [130] Surflex, [131] GOLD, [132] ICM [132] Glide, [133] LigandFit, [134] MCDock, [135] MOE‐Dock, [136] LeDock, [137] AutoDock Vina, [138] rDock, [139] UCSF Dock, [140] QuickVina, [141] RiboDock, [142] MORDOR [143] and many others that are being developed for both academic and commercial purposes over the past two decades. Among these programs, AutoDock Vina, GOLD, [132] and MOE‐Dock [136] predicted top‐ranking poses with the best scores.…”

Development of Nucleic Acid Targeting Molecules: Molecular Docking Approaches and Recent Advances

“…Unfortunately, the RLDOCK approach is difficult to apply to big target and ligand sets. The time-consuming selection of the complex formation produces prohibitively small processing effectiveness of the approach in complexes with a big RNA such as ribosomal RNA or ligands with the more than 12 rotatable bonds [107,108].…”

Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions

“…Taken together, these results suggest that the model is both sensitive and accurate in identifying the native or near-native 3D folds of an RNA. Combined with SHAPE data, the model would provide a powerful tool for RNA nanoparticle design based on the desired RNA native structures [122] , [123] , [124] , [125] , [126] , [127] , [128] , RNA conformational changes [129] , [130] , [131] , [132] , and RNA-ligand interactions [133] , [134] , [135] .

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