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ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins
BACKGROUND: Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908730/ https://www.ncbi.nlm.nih.gov/pubmed/27301453 http://dx.doi.org/10.1186/s12967-016-0928-3 |
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author | Gupta, Sudheer Madhu, Midhun K. Sharma, Ashok K. Sharma, Vineet K. |
author_facet | Gupta, Sudheer Madhu, Midhun K. Sharma, Ashok K. Sharma, Vineet K. |
author_sort | Gupta, Sudheer |
collection | PubMed |
description | BACKGROUND: Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1β, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response. RESULTS: A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %. CONCLUSION: The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-016-0928-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4908730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49087302016-06-16 ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins Gupta, Sudheer Madhu, Midhun K. Sharma, Ashok K. Sharma, Vineet K. J Transl Med Research BACKGROUND: Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1β, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response. RESULTS: A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %. CONCLUSION: The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-016-0928-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-14 /pmc/articles/PMC4908730/ /pubmed/27301453 http://dx.doi.org/10.1186/s12967-016-0928-3 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Gupta, Sudheer Madhu, Midhun K. Sharma, Ashok K. Sharma, Vineet K. ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins |
title | ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins |
title_full | ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins |
title_fullStr | ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins |
title_full_unstemmed | ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins |
title_short | ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins |
title_sort | proinflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908730/ https://www.ncbi.nlm.nih.gov/pubmed/27301453 http://dx.doi.org/10.1186/s12967-016-0928-3 |
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