NetMHCIIpan - 4.0

Pan-specific binding of peptides to MHC class II alleles of known sequence

The NetMHCIIpan-4.0 server predicts peptide binding to any MHC II molecule of known sequence using Artificial Neural Networks (ANNs). It is trained on an extensive dataset of over 500.000 measurements of Binding Affinity (BA) and Eluted Ligand mass spectrometry (EL), covering the three human MHC class II isotypes HLA-DR, HLA-DQ, HLA-DP, as well as the mouse molecules (H-2). The introduction of EL data extends the number of MHC II molecules covered, since BA data covers 59 molecules and EL data covers 74. As mentioned, the network can predict for any MHC II of known sequence, which the user can specify as FASTA format. The network can predict for peptides of any length.

The output of the model is a prediction score for the likelihood of a peptide to be naturally presented by and MHC II receptor of choice. The output also includes %rank score, which normalizes prediction score by comparing to prediction of a set of random peptides. Optionally, the model also outputs BA prediction and %rank scores.

New in this version: The two output neuron architechture introduced in NetMHCpan-4.0 permits the inclusion of EL data, and the new training algorithm NNAlign_MA extends training data to ligands of ambiguous allele assignments. The model also, optionally, encodes ligand context.

Note: If you have downloaded the stand alone version of the tool before Maj 1, 2020, please download the data file again from data.tar.gz. The earlier file was missing a few pre-calculated files to estimate percentile rank values.

Refer to the instructions page for more details.

The project is a collaboration between CBS, and LIAI.

View the version history of this server.


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