Dataset Information

Recognition of small molecule-RNA binding sites using RNA sequence and structure.

ABSTRACT:

Motivation

RNA molecules become attractive small-molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule-RNA binding sites.

Results

We developed a novel method RNAsite to predict small molecule-RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure's flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs.

Availability and implementation

http://yanglab.nankai.edu.cn/RNAsite.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Su H

PROVIDER: S-EPMC8034527 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Recognition of small molecule-RNA binding sites using RNA sequence and structure.

Su Hong H Peng Zhenling Z Yang Jianyi J

Bioinformatics (Oxford, England) 20210401 1

<h4>Motivation</h4>RNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule-RNA binding sites.<h4>Results</h4>We developed a novel method RNAsite to predict small molecule-RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be compet ...[more]

PMID: 33416863

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Project description:Background Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand. Results To encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome. Conclusions We present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites.Graphical abstract Mapping binding site space. Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0180-0) contains supplementary material, which is available to authorized users.

Dataset Information

Recognition of small molecule-RNA binding sites using RNA sequence and structure.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Recognition of small molecule-RNA binding sites using RNA sequence and structure.

Similar Datasets

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