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Journal of Translational Medicine

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Open Access 01-12-2016 | Research

ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins

Authors: Sudheer Gupta, Midhun K. Madhu, Ashok K. Sharma, Vineet K. Sharma

Published in: Journal of Translational Medicine | Issue 1/2016

Abstract

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/.

Available only for authorised users

Fosgerau K, Hoffmann T. Peptide therapeutics: current status and future directions. Drug Discov Today. 2015;20:122–8.CrossRefPubMed

Shi J, Liu Y, Wang Y, Zhang J, Zhao S, Yang G. Biological and immunotoxicity evaluation of antimicrobial peptide-loaded coatings using a layer-by-layer process on titanium. Sci Rep. 2015;5:16336.CrossRefPubMedPubMedCentral

Gustafsson A, Sigel S, Ljunggren L. The antimicrobial peptide LL37 and its truncated derivatives potentiates proinflammatory cytokine induction by lipoteichoic acid in whole blood. Scand J Clin Lab Invest. 2010;70:512–8.CrossRefPubMed

Gordon YJ, Romanowski EG, McDermott AM. A review of antimicrobial peptides and their therapeutic potential as anti-infective drugs. Curr Eye Res. 2005;30:505–15.CrossRefPubMedPubMedCentral

Hsu HY, Chang MH, Ni YH, Huang SF. Cytomegalovirus infection and proinflammatory cytokine activation modulate the surface immune determinant expression and immunogenicity of cultured murine extrahepatic bile duct epithelial cells. Clin Exp Immunol. 2001;126:84–91.CrossRefPubMedPubMedCentral

Desmet VJ. Cholangiopathies: past, present, and future. Semin Liver Dis. 1987;7:67–76.CrossRefPubMed

Bylund J, Christophe T, Boulay F, Nystrom T, Karlsson A, Dahlgren C. Proinflammatory activity of a cecropin-like antibacterial peptide from Helicobacter pylori. Antimicrob Agents Chemother. 2001;45:1700–4.CrossRefPubMedPubMedCentral

Bellner L, Thoren F, Nygren E, Liljeqvist JA, Karlsson A, Eriksson K. A proinflammatory peptide from herpes simplex virus type 2 glycoprotein G affects neutrophil, monocyte, and NK cell functions. J Immunol. 2005;174:2235–41.CrossRefPubMed

Bacot SM, Lenz P, Frazier-Jessen MR, Feldman GM. Activation by prion peptide PrP106-126 induces a NF-kappaB-driven proinflammatory response in human monocyte-derived dendritic cells. J Leukoc Biol. 2003;74:118–25.CrossRefPubMed

10.

Kahlenberg JM, Kaplan MJ. Little peptide, big effects: the role of LL-37 in inflammation and autoimmune disease. J Immunol. 2013;191:4895–901.CrossRefPubMed

11.

Vasic D, Walcher D. Proinflammatory effects of C-Peptide in different tissues. Int J Inflamm. 2012;2012:932725.CrossRef

12.

Saha S, Raghava GP. Prediction methods for B-cell epitopes. Methods Mol Biol. 2007;409:387–94.CrossRefPubMed

13.

Singh H, Ansari HR, Raghava GP. Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PLoS One. 2013;8:e62216.CrossRefPubMedPubMedCentral

14.

Y EL-Manzalawy Y, Honavar V. Building classifier ensembles for B-cell epitope prediction. Methods Mol Biol. 2014;1184:285–94.CrossRef

15.

Desai DV, Kulkarni-Kale U. T-cell epitope prediction methods: an overview. Methods Mol Biol. 2014;1184:333–64.CrossRefPubMed

16.

Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 2003;12:1007–17.CrossRefPubMedPubMedCentral

17.

Bhasin M, Raghava GP. Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine. 2004;22:3195–204.CrossRefPubMed

18.

Bhasin M, Raghava GP. A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J Biosci. 2007;32:31–42.CrossRefPubMed

19.

Dhanda SK, Gupta S, Vir P, Raghava GP. Prediction of IL4 inducing peptides. Clin Dev Immunol. 2013;2013:263952.CrossRefPubMedPubMedCentral

20.

Dhanda SK, Vir P, Raghava GP. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct. 2013;8:30.CrossRefPubMedPubMedCentral

21.

Saha S, Raghava GP. AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. 2006;34:W202–9.CrossRefPubMedPubMedCentral

22.

Dimitrov I, Flower DR, Doytchinova I. AllerTOP—a server for in silico prediction of allergens. BMC Bioinform. 2013;14(Suppl 6):S4.CrossRef

23.

Wan M, Godson C, Guiry PJ, Agerberth B, Haeggstrom JZ. Leukotriene B4/antimicrobial peptide LL-37 proinflammatory circuits are mediated by BLT1 and FPR2/ALX and are counterregulated by lipoxin A4 and resolvin E1. FASEB J. 2011;25:1697–705.CrossRefPubMed

24.

Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GP. Peptide toxicity prediction. Methods Mol Biol. 2015;1268:143–57.CrossRefPubMed

25.

Dinarello CA. Historical insights into cytokines. Eur J Immunol. 2007;37(Suppl 1):S34–45.CrossRefPubMedPubMedCentral

26.

Vogt AB, Kropshofer H, Kalbacher H, Kalbus M, Rammensee HG, Coligan JE, Martin R. Ligand motifs of HLA-DRB5*0101 and DRB1*1501 molecules delineated from self-peptides. J Immunol. 1994;153:1665–73.PubMed

27.

Zhang Q, Wang P, Kim Y, Haste-Andersen P, Beaver J, Bourne PE, Bui HH, Buus S, Frankild S, Greenbaum J, et al. Immune epitope database analysis resource (IEDB-AR). Nucleic Acids Res. 2008;36:W513–8.CrossRefPubMedPubMedCentral

28.

Gupta A, Kapil R, Dhakan DB, Sharma VK. MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data. PLoS One. 2014;9:e93907.CrossRefPubMedPubMedCentral

29.

Sharma AK, Gupta A, Kumar S, Dhakan DB, Sharma VK. Woods: a fast and accurate functional annotator and classifier of genomic and metagenomic sequences. Genomics. 2015;106:1–6.CrossRefPubMed

30.

Zhang J, Zhao X, Sun P, Gao B, Ma Z. Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering. Biomed Res Int. 2014;2014:689219.PubMedPubMedCentral

31.

Gupta S, Ansari HR, Gautam A, Raghava GP. Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biol Direct. 2013;8:27.CrossRefPubMedPubMedCentral

32.

Melo MN, Ferre R, Feliu L, Bardaji E, Planas M, Castanho MA. Prediction of antibacterial activity from physicochemical properties of antimicrobial peptides. PLoS One. 2011;6:e28549.CrossRefPubMedPubMedCentral

33.

Sharma A, Singla D, Rashid M, Raghava GP. Designing of peptides with desired half-life in intestine-like environment. BMC Bioinform. 2014;15:282.CrossRef

34.

Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, Raghava GP. In silico approach for predicting toxicity of peptides and proteins. PLoS One. 2013;8:e73957.CrossRefPubMedPubMedCentral

35.

Vens C, Rosso MN, Danchin EG. Identifying discriminative classification-based motifs in biological sequences. Bioinformatics. 2011;27:1231–8.CrossRefPubMed

36.

Rammensee HG, Friede T, Stevanoviic S. MHC ligands and peptide motifs: first listing. Immunogenetics. 1995;41:178–228.CrossRefPubMed

37.

Nielsen M, Lund O, Buus S, Lundegaard C. MHC class II epitope predictive algorithms. Immunology. 2010;130:319–28.CrossRefPubMedPubMedCentral

38.

Ward JJ, McGuffin LJ, Buxton BF, Jones DT. Secondary structure prediction with support vector machines. Bioinformatics. 2003;19:1650–5.CrossRefPubMed

39.

Wang HW, Pai TW. Machine learning-based methods for prediction of linear B-cell epitopes. Methods Mol Biol. 2014;1184:217–36.CrossRefPubMed

40.

Mohammad TA, Nagarajaram HA. SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids. J Bioinform Comput Biol. 2011;9:489–502.CrossRefPubMed

41.

Liu Y. Active learning with support vector machine applied to gene expression data for cancer classification. J Chem Inf Comput Sci. 2004;44:1936–41.CrossRefPubMed

42.

Zavaljevski N, Stevens FJ, Reifman J. Support vector machines with selective kernel scaling for protein classification and identification of key amino acid positions. Bioinformatics. 2002;18:689–96.CrossRefPubMed

43.

Chaudhary N, Sharma AK, Agarwal P, Gupta A, Sharma VK. 16S classifier: a tool for fast and accurate taxonomic classification of 16S rRNA hypervariable regions in metagenomic datasets. PLoS One. 2015;10:e0116106.CrossRefPubMedPubMedCentral

44.

Bhasin M, Raghava GP. Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences. Nucleic Acids Res. 2005;33:W202–7.CrossRefPubMedPubMedCentral

Title: ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins
Authors: Sudheer Gupta
Midhun K. Madhu
Ashok K. Sharma
Vineet K. Sharma
Publication date: 01-12-2016
Publisher: BioMed Central
Published in: Journal of Translational Medicine / Issue 1/2016
Electronic ISSN: 1479-5876
DOI: https://doi.org/10.1186/s12967-016-0928-3

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