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

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 investi...

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Autores principales: Gupta, Sudheer, Mittal, Parul, Madhu, Midhun K., Sharma, Vineet K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5671494/
https://www.ncbi.nlm.nih.gov/pubmed/29163505
http://dx.doi.org/10.3389/fimmu.2017.01430
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author Gupta, Sudheer
Mittal, Parul
Madhu, Midhun K.
Sharma, Vineet K.
author_facet Gupta, Sudheer
Mittal, Parul
Madhu, Midhun K.
Sharma, Vineet K.
author_sort Gupta, Sudheer
collection PubMed
description 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/.
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spelling pubmed-56714942017-11-21 IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response Gupta, Sudheer Mittal, Parul Madhu, Midhun K. Sharma, Vineet K. Front Immunol Immunology 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/. Frontiers Media S.A. 2017-10-31 /pmc/articles/PMC5671494/ /pubmed/29163505 http://dx.doi.org/10.3389/fimmu.2017.01430 Text en Copyright © 2017 Gupta, Mittal, Madhu and Sharma. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Gupta, Sudheer
Mittal, Parul
Madhu, Midhun K.
Sharma, Vineet K.
IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response
title IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response
title_full IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response
title_fullStr IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response
title_full_unstemmed IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response
title_short IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response
title_sort il17escan: a tool for the identification of peptides inducing il-17 response
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5671494/
https://www.ncbi.nlm.nih.gov/pubmed/29163505
http://dx.doi.org/10.3389/fimmu.2017.01430
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