Version history


Please click on the version number to activate the corresponding server.

2.3 The current server (online since Sept, 2017). New in this version:
  • The method has been retained on an extented data set
2.2 The current server (online since April 14, 2010). New in this version:
  • The method has been retained on an extented data set including six HLA-DQ and six HLA-DP alleles.
2.1 The current server (online since 22 November 2009). New in this version:
  • The method has been retained on an extented data set including four HLA-DQ and four HLA-DP alleles.
2.0 Online since 30 September 2009. New in this version:
  • The NN-align method was applied to training the predictor for 14 HLA-DR and three mouse MHC class II alleles.
  • Publication: A neural network-based alignment algorithm for MHC class II peptide binding prediction. Nielsen M and Lund O. BMC Bioinformatics. 2009 Sep 18;10:296.
1.1 Online since 20 August 2009. New in this version:
  • The method has been retained on an extented data set including four HLA-DQ and four HLA-DP alleles.
1.0a Retrained version trained on binding data from Wang et al. PLoS Comput Biol. 2008 Apr 4;4(4).
  • For comparisons the SMM-align method has been reatrained on the Wang benchmark data set using 10 fold cross validation. The predictive performamce values are available online at Wang Benchmark
1.0 Original version (online version until August 2009):

Main publication: Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. Morten Nielsen, Claus Lundegaard, and Ole Lund. BMC Bioinformatics: 8: 238, 2007.

  • Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.
    Morten Nielsen*, Claus Lundegaard, and Ole Lund.
    BMC Bioinformatics: 8: 238, 2007.
    View the abstract, the full text version at BMC Bioinformatics: Full text

    NOTE! The published version of the manuscript has an inconsistent reference listing. For a correct reference listing click here




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