SIMLIN: a bioinformatics tool for prediction of S-sulphenylation in the human proteome based on multi-stage ensemble-learning models

Xiaochuan Wang, Chen Li, Fuyi Li, Varun S. Sharma, Jiangning Song, Geoffrey I. Webb

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)


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 for academic purposes.

Original languageEnglish
Article number602
Number of pages12
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 21 Nov 2019


  • Bioinformatics software
  • Ensemble learning
  • Machine learning
  • Protein post-translational modification
  • S-sulphenylation

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