Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Gupta, Sudheer, Madhu, Midhun K., Sharma, Ashok K., Sharma, Vineet K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
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
_version_ 1782437733641224192
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
work_keys_str_mv AT guptasudheer proinflamawebserverforthepredictionofproinflammatoryantigenicityofpeptidesandproteins
AT madhumidhunk proinflamawebserverforthepredictionofproinflammatoryantigenicityofpeptidesandproteins
AT sharmaashokk proinflamawebserverforthepredictionofproinflammatoryantigenicityofpeptidesandproteins
AT sharmavineetk proinflamawebserverforthepredictionofproinflammatoryantigenicityofpeptidesandproteins