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WIREs Comput Mol Sci
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RNA–ligand molecular docking: Advances and challenges

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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. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics
Three major applications of an RNA–ligand interaction model. Virtual screening involves docking against small molecules in a large library and scoring every docked pose. Top‐scored selections are treated as the most promising candidates for putative binders. For a given RNA–ligand pair, computational models for ligand binding pose identification and RNA–ligand binding affinity prediction rely on scoring the possible RNA–ligand complex structures. An ideal scoring function for ligand binding pose identification should have the ability to distinguish the native pose from a large pool of docked decoy poses, while achieving the maximum correlation between the predicted scores and the experimental affinities for different RNA–ligand pairs
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A simplified representation of the binding kinetics between the unbound receptor (R), unbound ligand (L) and the bound receptor–ligand complex (RL). (a) The binding kinetics of a system with only one transient state (TS) along the binding reaction coordinate. The figure shows a binding scenario where both receptor and ligand undergo conformational changes in the binding process. The kinetic residence time (i.e., the inverse of the RNA–ligand dissociation constant koff) depends on the free energy difference (ΔGoff) between the bound state and transient state, while the thermodynamic binding energy (ΔGbind) is determined by the free energy difference between the unbound state (R + L) and bound state (RL). (b) In practice, often the binding kinetic profile of a system contains multiple transient states (TS) and intermediate states (IS) with a much more complicated kinetic mechanism
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The typical workflow of a machine‐learning approach. Training and validation cycle usually needs to be performed many times before the performance on the validation set reaches an acceptable level. After the training‐validation cycle, the trained model is used to make predictions on the test set
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Different approaches to modeling RNA flexibility in RNA–ligand interactions illustrated using HIV‐TAR RNA (PDB: 1ANR145) as an example. The orange and blue regions correspond to rigid and flexible portions of RNA, respectively. From left to right, (a) bases from the active site are allowed to partially overlap with atoms from ligand through soft potential, (b) an ensemble of various RNA conformations is used to perform docking, and (c) RNA with full flexibility. Computational efficiency decreases from left to the right
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Illustration of conformational sampling methods used in RLDOCK, using the docking of 2′‐deoxyguanosine to 2′‐deoxyguanosine riboswitch (PDB code: 3SKL135) as an example. An ensemble of different conformers of the 2′‐deoxyguanosine (dG) is constructed for flexible docking. The sampling and scoring procedures are shown in order and labeled through a to e. (a) First, the regions of possible anchor sites within the riboswitch, colored in magenta, are determined by the geometric features of the target RNA. (b) Second, with exhaustive sampling of these prepared conformers through translation and rotation around the anchor sites, (c) binding sites (yellow dots) are selected according to Lennard‐Jones potential between RNA and ligand atoms. (d) Finally, the sampled ligand conformations associated with the selected binding sites are ranked (e) by a physics‐based scoring function
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RNA conformational changes and binding interactions mediated by water molecules and ions. (a) The local structure difference of preQ1 riboswitches between apo (ligand‐free) and holo (ligand‐bound) states. The structure in orange denotes the apo state (PDB code: 6VUH113) and the structure in blue denotes the holo state (PDB code: 3Q50114) with its bound small molecule (PRF) colored in magenta. Upon binding, the small molecule displaces residue A14 (colored in green for both apo and holo states) and causes the local structural transition. (b) Water molecules mediated RNA–ligand interactions. Water molecules form a bridge between small molecule Neomycin B (NEM, magenta) and 16S‐rRNA A‐site (PDB code: 2ET4115). The isolated red dots denote the oxygen atoms in water molecules. The black dashed lines show the water‐mediated hydrogen bonding contacts that promote NEM binding to the RNA receptor. (c) Metal ions in RNA‐small molecule interactions. The ligand benfotiamine (BTP, magenta) interacts with residues G60, C77, and G78 of the Thi‐box riboswitch through two magnesium ions (green) and the G42‐A43 base stack (PDB code: 2HOO116). The black solid lines represent the inner sphere metal ion coordination. The polyanionic RNA recognizes the positively charged metal ion complex made up of the monophosphorylated compound and cations
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The difference between local and blind docking. A complex of an aminoglycoside antibiotic, gentamicin (green) and the 16S‐rRNA A site of bacterial ribosome is used for illustration (PDB code: 2ET3115). In this example, both docking (local and blind) processes are carried out using the RLDOCK model.70,71 In local docking, the binding pocket is predefined and the sampling is contained within the red dashed box. The small magenta spheres denote candidate binding sites predicted by RLDOCK. In blind docking, the binding site detection is performed across the whole surface of the RNA. The small yellow and magenta spheres denote the predicted high‐ and low‐probability binding sites, respectively. Two cavities identified by RLDOCK (anchored by yellow spheres) are zoomed out separately
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