LigandRNA: computational predictor of RNA–ligand interactions

Abstract

RNA molecules have recently become attractive as potential drug targets due to the increased awareness of their importance in key biological processes. The increase of the number of experimentally determined RNA 3D structures enabled structure-based searches for small molecules that can specifically bind to defined sites in RNA molecules, thereby blocking or otherwise modulating their function. However, as of yet, computational methods for structure-based docking of small molecule ligands to RNA molecules are not as well established as analogous methods for protein-ligand docking. This motivated us to create LigandRNA, a scoring function for the prediction of RNA-small molecule interactions. Our method employs a grid-based algorithm and a knowledge-based potential derived from ligand-binding sites in the experimentally solved RNA-ligand complexes. As an input, LigandRNA takes an RNA receptor file and a file with ligand poses. As an output, it returns a ranking of the poses according to their score. The predictive power of LigandRNA favorably compares to five other publicly available methods. We found that the combination of LigandRNA and Dock6 into a "meta-predictor" leads to further improvement in the identification of near-native ligand poses. The LigandRNA program is available free of charge as a web server at http:// ligandrna.genesilico.pl.

Structure-Based Discovery of Small Molecules Binding to RNA

“…MODOR generates first several orientations of the ligand in the binding site which are subsequently energy minimized whereas ligand and receptor are kept flexible to account for induced fit movements [73]. The resulting poses are scored with a force-field based scoring function which also considers the solvation energy [39] In some cases, scoring function was re-parameterized [55] or solvation and atom parameters were adopted [56] [ [58] [ 58] iMDLScore2 [58] [ 58] KScore [77] [ 77] LigandRNA [69] [ 69] based on an implicit solvent model. RiboDock treats the ligand as flexible and uses MC sampling to generate the binding modes [75].…”

“…Five standalone functions for scoring RNA-ligand complexes were published (Table 2). DrugScoreRNA, KScore, and LigandRNA are all knowledge-based scoring functions [60,69,77]. DrugScoreRNA uses distance-dependent pair potentials derived in a similar manner as the original DrugScore function [60].…”

“…With a different test set (42 complexes). LigandRNA and DOCK 6 found the best solution in 36% and DrugScoreRNA in 31% of the cases [69]. Moitessier et al were able to dock a set of 11 aminoglycosides with an average rmsd of 1.4 Å back into their receptors using an extended version of AutoDock accounting for structural water molecules and RNA flexibility [59].…”

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

“…The scoring scheme allows comparison between scores obtained for different ligands. For poses generated by the Dock6 program, there is an additional option to compute a LigandRNA-Dock6 combined score, which significantly increases the accuracy of both methods (Philips, Milanowska, Lach, & Bujnicki, 2013). The added value of the combination of LigandRNA and Dock6 results most likely from the very different, and hence complementary, character of both scoring functions.…”

Synthesis, antibacterial studies, and molecular modeling studies of 3,4-dihydropyrimidinone compounds

“…Subsequently, rRNA actively involved in wide range of biological process such as the storage and propagation of genetic information and protein synthesis [3]. In order to choose the novel targets towards RNA, rRNA is one of the good candidates, as well-studied drug targets for their active site [4]. Aminoglycosides are group of extensively studied antibiotics that specifically bind with A site of 16S rRNA in the bacterial ribosome [5].…”