Bioinformatics Advance Access originally published online on November 28, 2007
Bioinformatics 2007 23(24):3265-3275; doi:10.1093/bioinformatics/btm471
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Modeling the adaptive immune system: predictions and simulations
mir 1,21Center for biological sequence analysis, CBS, Kemitorvet 208, Technical University of Denmark, DK-2800 Lyngby, Denmark and 2Theoretical biology/bioinformatics, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
*To whom correspondence should be addressed.
 |
Abstract |
|---|
Motivation: Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered.
Summary: Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.
Contact: lunde{at}cbs.dtu.dk
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Jonathan Wren
Received on June 21, 2007; revised on September 10, 2007; accepted on September 10, 2007
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  C. Lundegaard, K. Lamberth, M. Harndahl, S. Buus, O. Lund, and M. Nielsen NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11 Nucleic Acids Res., July 1, 2008; 36(suppl_2): W509 - W512. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 C. Lundegaard, O. Lund, and M. Nielsen Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers Bioinformatics, June 1, 2008; 24(11): 1397 - 1398. [Abstract] [Full Text] [PDF]
|
|