IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response

Abstract

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IL-17 cytokines are pro-inflammatory cytokines and are crucial in host defense against various microbes. Induction of these cytokines by microbial antigens has been investigated in the case of ischemic brain injury, gingivitis, candidiasis, autoimmune myocarditis, etc. In this study, we have investigated the ability of amino acid sequence of antigens to induce IL-17 response using machine-learning approaches. A total of 338 IL-17-inducing and 984 IL-17 non-inducing peptides were retrieved from Immune Epitope Database. 80% of the data were randomly selected as training dataset and rest 20% as validation dataset. To predict the IL-17-inducing ability of peptides/protein antigens, different sequence-based machine-learning models were developed. The performance of support vector machine (SVM) and random forest (RF) was compared with different parameters to predict IL-17-inducing epitopes (IIEs). The dipeptide composition-based SVM-model displayed an accuracy of 82.4% with Matthews correlation coefficient = 0.62 at polynomial (t = 1) kernel on 10-fold cross-validation and outperformed RF. Amino acid residues Leu, Ser, Arg, Asn, and Phe and dipeptides LL, SL, LK, IL, LI, NL, LR, FK, SF, and LE are abundant in IIEs. The present tool helps in the identification of IIEs using machine-learning approaches. The induction of IL-17 plays an important role in several inflammatory diseases, and identification of such epitopes would be of great help to the immunologists. It is freely available at http://metagenomics.iiserb.ac.in/IL17eScan/ and http://metabiosys.iiserb.ac.in/IL17eScan/.

Keywords

• interleukin-17
• machine learning
• support vector machine
• random forest
• pro-inflammatory cytokines