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A cross-linking approach to map small molecule-RNA binding sites in cells.

ABSTRACT: Methods to identify RNAs bound by small molecules in cells are sparse. Herein, an advance to identify the direct RNA targets of small molecules in cells is described. The approach, dubbed Chemical Cross-Linking and Isolation by Pull-down to Map Small Molecule-RNA Binding Sites (Chem-CLIP-Map-Seq), appends a cross-linker and a purification tag onto a small molecule. In cells, the compound binds to RNA and undergoes a proximity-based reaction. The cross-linked RNA is purified and then amplified using a universal reverse transcription (RT) primer and gene-specific PCR primers. At nucleotides proximal to the binding site, RT "stops" are observed. This approach has broad utility in identifying and validating the RNA targets and binding sites of small molecules in the context of a complex cellular system.

SUBMITTER: Velagapudi SP

PROVIDER: S-EPMC6598432 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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A cross-linking approach to map small molecule-RNA binding sites in cells.

Velagapudi Sai Pradeep SP Li Yue Y Disney Matthew D MD

Bioorganic & medicinal chemistry letters 20190402 12

Methods to identify RNAs bound by small molecules in cells are sparse. Herein, an advance to identify the direct RNA targets of small molecules in cells is described. The approach, dubbed Chemical Cross-Linking and Isolation by Pull-down to Map Small Molecule-RNA Binding Sites (Chem-CLIP-Map-Seq), appends a cross-linker and a purification tag onto a small molecule. In cells, the compound binds to RNA and undergoes a proximity-based reaction. The cross-linked RNA is purified and then amplified us ...[more]

PMID: 30987892

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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.

Project description:BackgroundThe study of protein-small molecule interactions is vital for understanding protein function and for practical applications in drug discovery. To benefit from the rapidly increasing structural data, it is essential to improve the tools that enable large scale binding site prediction with greater emphasis on their biological validity.ResultsWe have developed a new method for the annotation of protein-small molecule binding sites, using inference by homology, which allows us to extend annotation onto protein sequences without experimental data available. To ensure biological relevance of binding sites, our method clusters similar binding sites found in homologous protein structures based on their sequence and structure conservation. Binding sites which appear evolutionarily conserved among non-redundant sets of homologous proteins are given higher priority. After binding sites are clustered, position specific score matrices (PSSMs) are constructed from the corresponding binding site alignments. Together with other measures, the PSSMs are subsequently used to rank binding sites to assess how well they match the query and to better gauge their biological relevance. The method also facilitates a succinct and informative representation of observed and inferred binding sites from homologs with known three-dimensional structures, thereby providing the means to analyze conservation and diversity of binding modes. Furthermore, the chemical properties of small molecules bound to the inferred binding sites can be used as a starting point in small molecule virtual screening. The method was validated by comparison to other binding site prediction methods and to a collection of manually curated binding site annotations. We show that our method achieves a sensitivity of 72% at predicting biologically relevant binding sites and can accurately discriminate those sites that bind biological small molecules from non-biological ones.ConclusionsA new algorithm has been developed to predict binding sites with high accuracy in terms of their biological validity. It also provides a common platform for function prediction, knowledge-based docking and for small molecule virtual screening. The method can be applied even for a query sequence without structure. The method is available at http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi.

Dataset Information

A cross-linking approach to map small molecule-RNA binding sites in cells.

Publications

A cross-linking approach to map small molecule-RNA binding sites in cells.

Similar Datasets

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