dbSNO: a database of cysteine S-nitrosylation
Tzong-Yi Lee1, Yi-Ju Chen, Cheng-Tsung Lu
1Department of Computer Science and Engineering, Yuan Ze University, Taoyuan 320, Taiwan. francis@saturn.yu.edu.tw
Bioinformatics (Oxford, England)
|July 12, 2012
Summary
The dbSNO database integrates over 3000 experimentally verified S-nitrosylation sites, providing crucial structural and functional data. This resource aids research into protein post-translational modifications and cell signaling pathways.
Area of Science:
- Proteomics
- Molecular Biology
- Bioinformatics
Background:
- S-nitrosylation (SNO) is a critical post-translational modification regulating protein function, activity, and stability.
- Existing methods for identifying SNO sites have generated extensive data, but a centralized repository with structural and functional context was lacking.
Purpose of the Study:
- To create a comprehensive database, dbSNO, integrating experimentally verified S-nitrosylation sites.
- To provide structural and functional analyses of these SNO sites.
Main Methods:
- Manual accumulation of >3000 S-nitrosylated peptides from 219 research articles using text mining.
- Mapping peptide sequences to UniProtKB entries to resolve data heterogeneity.
- Performing structural and functional analyses including motif identification, solvent accessibility, and domain mapping.
Main Results:
- The dbSNO database successfully integrates over 3000 experimentally verified S-nitrosylated peptides.
- Data is mapped to UniProtKB entries for consistency and linked to structural and functional information.
- Analyses reveal substrate motifs, solvent accessibility, secondary/tertiary structures, protein domains, and gene ontology for SNO sites.
Conclusions:
- The dbSNO database serves as a valuable resource for researchers studying S-nitrosylation.
- It facilitates the understanding of structural and functional correlations of SNO sites.
- The database is freely accessible and regularly updated.
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