Skip to main content

Predicting Virulence Factors of Immunological Interest

  • Protocol
Immunoinformatics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 409))

Summary

In this chapter, three prediction servers used for predicting virulence factors, bacterial toxins, and neurotoxins have been described. VICMpred server predicts the functional proteins of gram-negative bacteria that include virulence factors, information molecule, cellular process, and metabolism molecule. BTXpred server allows users to predict bacterial toxins, its release, and further classification of exotoxins. NTXpred server allows prediction of neurotoxins and further classifying them based on their function and source.

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

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Joachims, T. 1999. Making large-scale SVM learning particle. In Scholkopf, B., Burges, C., and Smola, A. (eds), Advances in Kernal Methods Support Vector Learning. MIT Press, Cambridge, MA and London, pp. 42–56.

    Google Scholar 

  2. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.

    Article  CAS  PubMed  Google Scholar 

  3. Eddy, S.R. 1998. Profile hidden Markov models. Bioinformatics 14: 755–763.

    Article  CAS  PubMed  Google Scholar 

  4. Saha, S. and Raghava, G.P.S. 2004. BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties. In Artificial Immune Systems, Nicosia, G., Cutello, V., Bentley, P.J., and Timis, J. (eds.) ICARIS, LNCS 3239, pp. 197–204.

    Google Scholar 

Download references

Acknowledgments

We acknowledge the financial support from the Council of Scientific and Industrial Research (CSIR) and Department of Biotechnology (DBT), Govt. of India.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Humana Press Inc.

About this protocol

Cite this protocol

Saha, S., Raghava, G.P. (2007). Predicting Virulence Factors of Immunological Interest. In: Flower, D.R. (eds) Immunoinformatics. Methods in Molecular Biology™, vol 409. Humana Press. https://doi.org/10.1007/978-1-60327-118-9_31

Download citation

  • DOI: https://doi.org/10.1007/978-1-60327-118-9_31

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-699-3

  • Online ISBN: 978-1-60327-118-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics