MetalionRNA: computational predictor of metal-binding sites in RNA structures

Abstract

Motivation: Metal ions are essential for the folding of RNA molecules into stable tertiary structures and are often involved in the catalytic activity of ribozymes. However, the positions of metal ions in RNA 3D structures are difficult to determine experimentally. This motivated us to develop a computational predictor of metal ion sites for RNA structures.Results: We developed a statistical potential for predicting positions of metal ions (magnesium, sodium and potassium), based on the analysis of binding sites in experimentally solved RNA structures. The MetalionRNA program is available as a web server that predicts metal ions for RNA structures submitted by the user.Availability: The MetalionRNA web server is accessible at http://metalionrna.genesilico.pl/.Contact: iamb@genesilico.plSupplementary information: Supplementary data are available at Bioinformatics online.

MCTBI: a web server for predicting metal ion effects in RNA structures

“…In general, ions can bind to an RNA through site-specific and nonspecific associations (Cate and Doudna 1996;Draper et al 2005;Lipfert et al 2014;Petukh et al 2015). Site-specific association (binding) is often accompanied with full or partial dehydration of the ions, which are trapped at specific sites (Misra and Draper 2001;Philips et al 2012) such as specific pocket regions in the structure. The nonspecifically bound ions usually remain hydrated and form a mobile "ionic atmosphere" (Lipfert et al 2014) ("ionic cloud") (Kirmizialtin et al 2012) to cover the RNA to neutralize most charges in RNA (Bai et al 2007;Tan and Chen 2010).…”

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

“…For a larger grid width of the cubic grid, the statistics would be biased by the cell boundaries, negatively affecting the prediction quality. For smaller cells, the computational power and time increase significantly, without noticeable influence on the accuracy of the results (for more details, see MetalionRNA original article (Philips et al, 2012)). However, MetalionRNA server offers a grid width of 0.25 Å to choose for analysis of a special purpose, but we do not recommend this for common practice.…”

LigandRNA: computational predictor of RNA–ligand interactions

“…The potential is obtained using the inverse Boltzmann scheme, which presumes that only those ligand poses are favorable that exhibit interactions fitting the maxima of the statistical distribution of RNA-ligand atom contacts derived from experimentally determined structures of RNA-ligand complexes. We have used the same approach that we found successful in prediction of RNA-metal ion interactions with our method MetalionRNA (Philips et al 2012). First, we defined a list of RNA atom pairs [a, b] in nucleotides, of which b is an atom that may directly interact with a ligand, and a is covalently bound to b (Table 1).…”

“…The most important advantage of using a grid is that the discretization of space obviates the need to solve the potential function analytically and allows mapping of the statistical data into well-defined portions of space. A grid-based approach has been successfully applied in our previous studies on RNA-metal ion interactions (Philips et al 2012) and in small molecule docking, e.g., in the AutoDock program (Morris et al 2009). …”

“…Researchers interested in RNA-ligand docking and modeling of RNA-ligand structures would benefit from the availability of a scoring function that is software-independent and can rank and validate models of RNA-ligand complexes regardless of the procedure used to generate them. The lack of a user-friendly method available as a web server capable of comparing RNA-ligand complexes generated by different modeling/docking methods motivated us to develop LigandRNA, a method for computational prediction of RNA-ligand interactions, based on methodology similar to that used successfully in our methods for predicting RNA-cation complexes, MetalionRNA (Philips et al 2012), and RNA-protein complexes, DARS-RNP and QUASI-RNP (Tuszynska and Bujnicki 2011).…”