• Method retrained on an extensive dataset of over 100,000 datapoints, covering 36 HLA-DR, 27 HLA-DQ, 9 HLA-DP, and 8 mouse MHC-II molecules.

• Improved methods for predicting peptide binding affinity to MHC class II molecules.
  Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B, Nielsen M.
  Immunology. 2018 Jan 6. doi: 10.1111/imm.12889.
  PubMed: 29315598

• Improved binding core identification by realigning individual networks in the ensemble.
• Introduced a reliability measure on the predicted binding core (Core_Rel column).
• Graphical representation of the binding core register and of possible multiple cores.

• Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification
  Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, and Nielsen M
  Immunogenetics (2015)
  PubMed: 26416257

• The user can make predictions for all DR, DP and DQ molecules with known protein sequence. Likewise can the user upload full length MHC class II alpha and beta chain and have the server predict MHC restricted peptides from any given protein of interest

• User can upload full length MHC class II beta chain and have the server predict MHC restricted peptides from any given protein of interest.

• New concurent algorithm used to train the network.

• %-rank measure include for each prediction value. The %-rank score give the rank of the prediction score to a distribution of prediction scores from 200.000 natural random 15mer peptides.

Main publication:

• Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan.
  Nielsen M, et al. (2008) PLoS Comput Biol. Jul 4;4(7):e1000107. View the full text article at PLoS Compu: Full text.