Ileukin10pred: A computational approach for predicting il-10-inducing immunosuppressive peptides using combinations of amino acid global features

Onkar Singh, Wen Lian Hsu, Emily Chia Yu Su

Research output: Contribution to journalArticlepeer-review

Abstract

Interleukin (IL)-10 is a homodimer cytokine that plays a crucial role in suppressing inflammatory responses and regulating the growth or differentiation of various immune cells. However, the molecular mechanism of IL-10 regulation is only partially understood because its regulation is environment or cell type-specific. In this study, we developed a computational approach, ILeukin10Pred (interleukin-10 prediction), by employing amino acid sequence-based features to predict and identify potential immunosuppressive IL-10-inducing peptides. The dataset comprises 394 experimentally validated IL-10-inducing and 848 non-inducing peptides. Furthermore, we split the dataset into a training set (80%) and a test set (20%). To train and validate the model, we applied a stratified five-fold cross-validation method. The final model was later evaluated using the holdout set. An extra tree classifier (ETC)-based model achieved an accuracy of 87.5% and Matthew’s correlation coefficient (MCC) of 0.755 on the hybrid feature types. It outperformed an existing state-of-the-art method based on dipeptide compositions that achieved an accuracy of 81.24% and an MCC value of 0.59. Our experimental results showed that the combination of various features achieved better predictive performance..

Original languageEnglish
Article number5
JournalBiology
Volume11
Issue number1
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Anti-inflammatory
  • Cytokines
  • Extra tree classifier
  • Immunosuppressive peptides
  • Interleukin-10
  • Machine learning

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)

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