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.
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.
| Original language | English |
|---|---|
| Article number | 602 |
| Number of pages | 12 |
| Journal | BMC Bioinformatics |
| DOIs | |
| Publication status | Accepted/In press - 21 Nov 2019 |
Keywords
- protein post-translational modification
- S-sulphenylation
- bioinformatics software
- machine learning
- ensemble learning
Cite this
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SIMLIN : a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models. / Wang, Xiaochuan; Li, Chen; Li, Fuyi; Sharma, Varun S.; Song, Jiangning; Webb, Geoffrey I.
In: BMC Bioinformatics, 21.11.2019.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - SIMLIN
T2 - a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models
AU - Wang, Xiaochuan
AU - Li, Chen
AU - Li, Fuyi
AU - Sharma, Varun S.
AU - Song, Jiangning
AU - Webb, Geoffrey I.
PY - 2019/11/21
Y1 - 2019/11/21
N2 - BackgroundS-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.ResultsIn 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.ConclusionsIn 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.
AB - BackgroundS-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.ResultsIn 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.ConclusionsIn 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.
KW - protein post-translational modification
KW - S-sulphenylation
KW - bioinformatics software
KW - machine learning
KW - ensemble learning
U2 - 10.1186/s12859-019-3178-6
DO - 10.1186/s12859-019-3178-6
M3 - Article
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
M1 - 602
ER -