Skip to main content

Advertisement

Log in

HLAB27Pred: SVM-based precise method for predicting HLA-B*2705 binding peptides in antigenic sequences

  • Original Article
  • Published:
Network Modeling Analysis in Health Informatics and Bioinformatics Aims and scope Submit manuscript

Abstract

Binding of Major histocompatibility complex (MHC) peptide is a prerequisite for T cell activation in the immune system. MHC-binding peptides have shown promising results for immunodiagnostics and immunotherapeutic purposes. HLA-B*2705 is found to be associated with the development of variety of autoimmune diseases including Ankylosing spondylitis. Detecting MHC class I allele HLA-B*2705 binding peptides can reduce the number of peptides that need to be tested experimentally. This work describes the implementation of SVM algorithm, developed for the identification of HLA-B*2705 binding peptides in antigenic sequences. The specificity and sensitivity obtained during the development of this server are 85 and 86 %, respectively. Whereas average precision and average recall values were observed to be 85 and 86 %, respectively. Training on wide-scale data made this method more accurate and robust than all available other methods for the HLA-B*2705 allele and would prove its usability in the biomedical domain. A web server HLA-B27pred is available at http://www.nuccore.org/hlab27pred for academic and research purpose.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Acar M, Cora T, Tunc R, Acar H (2011) HLA-B27 subtypes in Turkish patients with ankylosing spondylitis and healthy controls, Rheumatol Int (Epub ahead of print)

  • Allen R, Bowness P, McMichael A (1999) The role of HLA-B27 in spondyloarthritis. Immunogenetics 50:220–227

    Article  Google Scholar 

  • Bhasin M, Raghava GPS (2007) A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J Biosci 32(1):31–42

    Article  Google Scholar 

  • Brusic V, Rudy G, Kyne AP, Harrison LC (1997) MHCPEP, a database of MHC-binding peptides: update 1996. Nucleic Acids Res 25:269–271

    Article  Google Scholar 

  • Chandra S, Singh TR (2012) Linear B-cell epitopes prediction for epitope vaccine design against meningococcal disease and their computational validations through physicochemical properties. Netw Model Anal Health Inf Bioinform 1:153–159

    Article  Google Scholar 

  • Chandra S, Singh D, Singh TR (2010) Prediction and characterization of T-cell epitopes for epitope vaccine design from outer membrane protein of Neisseria meningitidis serogroup B. Bioinformation 5(4):155–161

    Article  Google Scholar 

  • Dale DC, Federman DD (1997) SAM CD: a comprehensive knowledge base of internal medicine. Scientific American, New York

    Google Scholar 

  • Diyarbakir E, Eyerci N, Melikoglu M, Topcu A, Pirim I (2012) HLA B27 subtype distribution among patients with ankylosing spondylitis in Eastern Turkey, Genet Test Mol Biomarkers 16(5):456–458. doi:10.1089/gtmb.2011.0183

  • Donnes P, Kohlbacher O (2006) SVMHC: a server for prediction of MHC-binding peptides. Nucleic Acids Res 34:W617–W622

    Article  Google Scholar 

  • Doytchinova IA, Flower DR (2001) Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A*0201. J Med Chem 44:3572–3581

    Article  Google Scholar 

  • Flower DR (2003) Towards in silico prediction of immunogenic epitopes. Trends Immunol 24:667–674

    Article  Google Scholar 

  • Flower DR (2008) Bioinformatics for vaccinology. Wiley-Blackwell, USA

    Book  Google Scholar 

  • Flower DR, Doytchinova IA (2002) Immunoinformatics and the prediction of immunogenicity, Appl Bioinformatics 1(A):167–176

  • Gupta A, Chaukiker D, Singh TR (2011) Comparative analysis of computational epitope predictions: proposed library of putative vaccine candidates for HIV. Bioinformation 5(9):386–389

    Article  Google Scholar 

  • Hertz T, Yanover C (2006) PepDist: a new framework for protein-peptide binding prediction based on learning peptide distance functions. BMC Bioinformatics 7:S3

    Article  Google Scholar 

  • Joachims T (1999) Making large-scale SVM learning practical. In: Scholkopf B, Burges C, Smole A (eds) Advances in Kernel methods-support vector learning. MIT Press, Cambridge, pp 169–184

    Google Scholar 

  • Khan MA, Mathieu A, Sorrentino R, Akkoc N (2007) The pathogenetic role of HLA-B27 and its subtypes. Autoimmun Rev 6:183–189

    Article  Google Scholar 

  • Kim TH, Uhm WS, Inman R (2005) Pathogenesis of ankylosing spondylitis and reactive arthritis. Curr Opin Rheumatol 17:400–405

    Article  Google Scholar 

  • Lata S, Bhasin M, Raghava GPS (2009) MHCBN 4.0: a database of MHC/TAP binding peptides and T-cell epitopes. BMC Res Notes 2:61

    Article  Google Scholar 

  • Mamitsuka H (1998) Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins 33:460–474

    Article  Google Scholar 

  • Mou Y, Wu Z, Gu J, Liao Z, Lin Z, Wei Q, Huang J, Li Q (2010) HLA-B27 polymorphism in patients with juvenile and adult-onset ankylosing spondylitis in Southern China. Tissue Antigens 75:56–60

    Article  Google Scholar 

  • Murugen N, Dai Y (2005) Prediction of MHC class II binding peptides based on an iterative learning model. Immunome Res 1:6

    Article  Google Scholar 

  • Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12:1007–1017

    Article  Google Scholar 

  • Parker KC, Bednarek MA, Coligan JE (1991) Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol 152:163–175

    Google Scholar 

  • Parker KC, Bednarek MA, Coligan JE (1994) Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol 152:163

    Google Scholar 

  • Purcell AW, McCluskey J, Rossjohn J (2007) More than one reason to rethink the use of peptides in vaccine design. Nat Rev Drug Discov 6:404–414

    Article  Google Scholar 

  • Rammensee H, Bachmann J, Emmerich NP, Bachor OA, Stevanovic´ S (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50:213–219

    Article  Google Scholar 

  • Reche PA, Glutting JP, Reinherz EL (2002) Prediction of MHC class I binding peptides using profile motifs. Hum Immunol 63:701–709

    Article  Google Scholar 

  • Salomon J, Flower DR (2006) Predicting class II MHC-peptide binding: a kernel based approach using similarity scores. BMC Bioinformatics 7:501

    Article  Google Scholar 

  • Singh H, Raghava GPS (2003) ProPred 1: prediction of promiscuous MHC class I binding sites. Bioinformatics 19:1009–1014

    Article  Google Scholar 

  • Vyes YC, Tood JA (1996) Genetic analysis of autoimmune disease. Cell 85:311–318

    Article  Google Scholar 

  • Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B (2010) The immune epitope database 2.0. Nucleic Acids Res 38(Database issue):D854–D862

    Article  Google Scholar 

  • Zhao L, Liu CH, Yu D (2011) High-throughput screening of chemical libraries for modulators of gene promoter activity of HLA-B2705: environmental pathogenesis and therapeutics of ankylosing spondylitis. J Rheumatol 38:1061–1065

    Article  Google Scholar 

Download references

Acknowledgments

Authors would like to thank Sachin Pundhir, Digvijay Singh Chauhan, and Shekhar Chandra for technical help and discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tiratha Raj Singh.

Additional information

A. Gupta and S. Chandra are joint first authors and equal contributors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, A., Chandra, S. & Singh, T.R. HLAB27Pred: SVM-based precise method for predicting HLA-B*2705 binding peptides in antigenic sequences. Netw Model Anal Health Inform Bioinforma 3, 56 (2014). https://doi.org/10.1007/s13721-014-0056-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13721-014-0056-z

Keywords

Navigation