Name
Title | ||
|---|---|---|
SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models. |
Abstract | ||
|---|---|---|
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. 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. 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12859-019-3178-6 | BMC Bioinformatics |
Keywords | DocType | Volume |
Protein post-translational modification, S-sulphenylation, Bioinformatics software, Machine learning, Ensemble learning | Journal | 20 |
Issue | ISSN | Citations |
1 | 1471-2105 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaochuan Wang | 1 | 0 | 0.34 |
| Chen Li | 2 | 68 | 6.46 |
| Fuyi Li | 3 | 97 | 11.25 |
| Varun S Sharma | 4 | 0 | 0.34 |
| Jiangning Song | 5 | 374 | 41.93 |
| Geoffrey I. Webb | 6 | 99 | 12.05 |