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

2 Citations (Scopus)

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.

Original languageEnglish
Article number602
Number of pages12
JournalBMC Bioinformatics
Volume20
Issue number1
DOIs
Publication statusPublished - 21 Nov 2019

Keywords

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

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