RNA-ligand interaction software tools | Drug discovery data analysis
RNA molecules have recently become attractive as potential drug targets. 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.
Assists users in analyzing non-coding RNA sequences. ARNhAck provides a software that focuses on the sole biochemical and evolutionary properties of the RNA. It gathers biochemical signal from structure probing experiments on RNA mutants with evolutionary information, collected in multiple sequence alignments (MSAs). The method intends to help in the identification, without prior knowledge of potential partners and hot-spots in RNA, involved in RNA–RNA, RNA–Protein, RNA–DNA and RNA–ligand interfaces.
Allows creation of binding site and docking. MORDOR is a software which contains all of the tools for virtual screening. The software can perform the main docking using its own energetic and minimization routines, but it is also interfaced with ANTECHAMBER for building ligand databases and CHARMM for some state-of-the-art molecular simulation such as calculating the solvation energy. It can also find the binding sites at the surface of the receptor to initially place the ligands.
Identifies stable or weak complexes by calculating Kd for formation of interfaces. SEQMOL is a protein data bank (PDB) structure analysis suite. It can be used to align multiple protein and DNA sequences, compute evolutionary attributes of multiple sequence alignments (such as sequence conservation, hydrophobicity conservation, conformational flexibility conservation, physical covariation, protein-protein interface, protein-RNA interface and protein-DNA interface propensity, and conservations thereof) and to map these features onto PDB files.
Helps for the prediction of RNA–small molecule interactions. LigandRNA is an independent grid-based program dedicated to scoring and ranking ligand poses in RNA 3D structures using a knowledge-based statistical potential. This application allows visualization of relative preferences of different regions of RNA surface to interact with different atoms of the ligand, as well as to reveal regions that are potential “hotspots” for binding of small molecules in general.
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