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SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models.
- Source :
- BMC Bioinformatics; 11/21/2019, Vol. 20 Issue 1, pN.PAG-N.PAG, 1p, 1 Color Photograph, 2 Diagrams, 5 Charts, 2 Graphs
- Publication Year :
- 2019
-
Abstract
- Background: S-sulphenylation is a ubiquitous protein post-translational modification (PTM) where an S-hydroxyl (−SOH) bond is formed via the reversible oxidation on the Sulfhydryl group of cysteine (C). Recent experimental studies have revealed that S-sulphenylation plays critical roles in many biological functions, such as protein regulation and cell signaling. State-of-the-art bioinformatic advances have facilitated high-throughput in silico screening of protein S-sulphenylation sites, thereby significantly reducing the time and labour costs traditionally required for the experimental investigation of S-sulphenylation. Results: In this study, we have proposed a novel hybrid computational framework, termed SIMLIN, for accurate prediction of protein S-sulphenylation sites using a multi-stage neural-network based ensemble-learning model integrating both protein sequence derived and protein structural features. Benchmarking experiments against the current state-of-the-art predictors for S-sulphenylation demonstrated that SIMLIN delivered competitive prediction performance. The empirical studies on the independent testing dataset demonstrated that SIMLIN achieved 88.0% prediction accuracy and an AUC score of 0.82, which outperforms currently existing methods. Conclusions: In summary, SIMLIN predicts human S-sulphenylation sites with high accuracy thereby facilitating biological hypothesis generation and experimental validation. The web server, datasets, and online instructions are freely available at http://simlin.erc.monash.edu/ for academic purposes. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14712105
- Volume :
- 20
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- BMC Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 139790630
- Full Text :
- https://doi.org/10.1186/s12859-019-3178-6