Computational Methods for Prediction of RNA Interactions with Metal Ions and Small Organic Ligands

Philips, Anna; Łach, Grzegorz; Bujnicki, Janusz M.

doi:10.1016/bs.mie.2014.10.057

Cited by 9 publications

(9 citation statements)

References 43 publications

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“…Ligand‐binding sites in RNA molecules are often more flexible and ionizable than in proteins and they often contain metal ions. When comparing the correlation between the predicted‐ and experimental‐binding affinities, it tends to be slightly higher for RNA‐specific scoring functions than for those developed for scoring protein–binding poses . However, despite the wide range of approaches used, currently available scoring functions give correlation between predicted‐ and measured‐binding affinities often lower than R 2 = 0.5 (or 50%, depending on the method used) for small molecule inhibitor‐RNA complexes .…”

Section: Future Perspectivesmentioning

confidence: 99%

“…21 Although the modeling of RNA-protein and RNA-metal ion complexes represent important topics in understanding many cellular processes, they are outside of the main subject of this review and have been extensively described elsewhere. 22,23 minimization, ab initio based and other methods, [24][25][26] and they have been shown to improve the correlation between the calculated-and experimental-binding affinities. Among the available methods to predict, small molecule ligand-binding orientations are different types of docking and MD methods.…”

Section: Modeling Of Rna-protein and Rna-metal Interactionsmentioning

confidence: 99%

“…When comparing the correlation between the predicted-and experimental-binding affinities, it tends to be slightly higher for RNA-specific scoring functions than for those developed for scoring protein-binding poses. 23,28 However, despite the wide range of approaches used, currently available scoring functions give correlation between predicted-and measured-binding affinities often lower than R 2 = 0.5 (or 50%, depending on the method used) for small molecule inhibitor-RNA complexes. 49 Another difficulty results from the differing performance of currently available scoring functions when applied to rigid and flexible RNA receptors.…”

Section: Rna-ligand-binding Affinity Predictionmentioning

confidence: 99%

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Modeling of ribonucleic acid–ligand interactions

Stefaniak

Chudyk

Bodkin

et al. 2015

WIREs Comput Mol Sci

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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. The function of many RNAs is modulated by small ligand molecules. These can be naturally occurring molecules as well as fully synthetic compounds. One of the most-studied and well-validated targets for small molecules is ribosomal RNA (rRNA), which forms the active site of the ribosome. This large macromolecular complex consists of RNA and proteins, and is responsible for protein synthesis. Blockage of ribosome function disrupts protein synthesis and leads to cell death. That is why the bacterial ribosome is a good target for many groups of small molecule antibiotics, including macrolides, aminoglycosides, tetracyclines, or oxazolidines, among others.2 Riboswitches represent another class of RNA molecules that can be modulated upon small molecule binding. They typically occur within the protein-noncoding parts of messenger RNA (mRNA) and regulate the translation of the protein-coding parts. Riboswitches can directly form complexes with small molecules and, in this way, regulate gene function without the presence of protein cofactors. Among natural ligands recognized by riboswitches are metabolites, divalent cations, and second messengers. [3][4][5] As riboswitches are common in bacteria and rarely occur in eukaryotes, they are emerging as a potential target for new and selective antibacterial drugs. Viral RNAs can also be † The authors wish it to be known that F.S. and E.I.C. contributed equally and should be regarded as Joint First Authors. 425targets for small molecule inhibitors. Examples include HIV-1 trans-activation response (TAR) RNA, for which small molecules 6-8 and peptidomimetics 9,10 that disrupt its interactions with the Tat protein have been described. Among other druggable viral RNAs are the Hepatitis C virus (HCV) internal ribosome entry site [11][12][13] and the Hepatitis D virus (HDV) ribozyme.14 In recent years, considerable attention has centered on micro RNA (miRNA) as a possible drug target in many diseases including inflamma...

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Section: Future Perspectivesmentioning

confidence: 99%

Section: Modeling Of Rna-protein and Rna-metal Interactionsmentioning

confidence: 99%

Section: Rna-ligand-binding Affinity Predictionmentioning

confidence: 99%

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Modeling of ribonucleic acid–ligand interactions

Stefaniak

Chudyk

Bodkin

et al. 2015

WIREs Comput Mol Sci

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“…Clearly the mean-field approach of PB is inadequate to account for specific ion binding, and alternative approaches have been developed to predict binding sites (e.g. [63]; also see review [64]). This is by no means an exhaustive review on NA electrostatics; for that, the reader is referred to other reviews [42,44,45,56,[65][66][67][68][69][70].…”

Section: Beyond Poisson-boltzmann Theorymentioning

confidence: 99%

Nucleic acid polymeric properties and electrostatics: Directly comparing theory and simulation with experiment

Sim

2016

Advances in Colloid and Interface Science

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“…Inspired by the tremendous potential of RNA-targeted therapeutics (127), researchers have developed a numbers of docking/scoring models for RNA-ligand binding (116). Different from ion binding, where an ion can be approximately modeled as a sphere, ligand binding models involve sampling of different orientations of the (anisotropic) ligand.…”

Section: Ligand Bindingmentioning

confidence: 99%

Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules

Sun

Zhang²,

Chen³

2017

Annu. Rev. Biophys.

111

110

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In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. Here, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures, and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories for the cellular environment effects, such as molecular crowding and cotranscriptional folding, may ultimately lead to an all-encompassing RNA folding model.

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Computational Methods for Prediction of RNA Interactions with Metal Ions and Small Organic Ligands

Cited by 9 publications

References 43 publications

Modeling of ribonucleic acid–ligand interactions

Modeling of ribonucleic acid–ligand interactions

Nucleic acid polymeric properties and electrostatics: Directly comparing theory and simulation with experiment

Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules

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