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WIREs Comput Mol Sci
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Modeling of ribonucleic acid–ligand interactions

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Computational methods play a pivotal role in the early stages of small molecule drug discovery and are widely applied in virtual screening, structure optimization, and compound activity profiling. Over the past half century in medicinal chemistry, almost all the attention has been directed to protein–ligand binding and computational tools were created with such targets in mind. However, with growing discoveries of functional RNAs and their possible applications, RNA macromolecules have gained considerable attention as possible drug targets. This flow of discovery was followed by adapting existing computational tools for RNA applications as well as active development of new RNA‐tailored methods. However, due to the different nature of RNA, especially its tendency to use morphological plasticity (conformational change in ligand binding) this remains a challenging task. The evolution of ‘protein‐based’ drug discovery and related computational methods offers some clues on possible future directions and developments in modeling RNA interactions with small molecule ligands. WIREs Comput Mol Sci 2015, 5:425–439. doi: 10.1002/wcms.1226

This article is categorized under:

  • Structure and Mechanism > Computational Biochemistry and Biophysics
  • Computer and Information Science > Chemoinformatics
  • Molecular and Statistical Mechanics > Molecular Interactions
Virtual screening of a small molecule library containing a fraction of active molecules (green) among inactive ones (red). The goal is to reduce the number of compounds to be analyzed in detail (e.g., by high‐resolution docking and experimental methods) from all the compounds in the initial library to only the most promising ones (highest scored). As currently used, screening tools are not perfect, among highest scored compounds are also inactive compounds (low affinity) while not all active compounds will receive a highly favorable score. The key to success of virtual screening is to enrich significantly the fraction of active compounds in the output subset of compounds, as compared with random selection.
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An example of computational docking of a small ligand to RNA structure. The structure of an RNA aptamer (shown in gray) bound to citrulline (in cyan) has been determined experimentally (PDB id: 1KOD) and the docked model has been taken from Ref . The best‐scoring ligand pose obtained in the course of computational docking (pose generation with DOCK6, scoring with LigandRNA) is shown in magenta.
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Various degrees of RNA flexibility in response to ligand‐binding (a) Nuclear Magnetic Resonance (NMR) structures of decoding region A‐Site, (b) HIV‐1 frameshift inducing element, and (c) HIV‐1 TAR RNA. RNA molecules are presented as a rainbow ribbon (5′ terminus in blue, 3′ terminus in red), small molecule ligands are presented as spheres (C—gray, O—red, N—blue)
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Schematic diagram illustrating the prediction of a macromolecule–small molecule complex structure by molecular docking of a small molecule ligand (blue) to a macromolecule receptor (brown). In the simplest variant of the docking procedure, the structures of the receptor and the ligand are kept rigid (like a key and a lock), but the ligand and the receptor may be also made partially or fully flexible. The matching of receptor and ligand structures results in a set of proposed binding modes with scores—numbers indicating the likelihood that the pose represents a favorable binding interaction. The highest ranked, most probable pose (with the best score) is bordered with a dashed line.
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Molecular and Statistical Mechanics > Molecular Interactions
Computer and Information Science > Chemoinformatics
Structure and Mechanism > Computational Biochemistry and Biophysics

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