NetMHCpan - 4.1

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

The NetMHCpan-4.1 server predicts binding of peptides to any MHC molecule of known sequence using artificial neural networks (ANNs). The method is trained on a combination of more than 850,000 quantitative Binding Affinity (BA) and Mass-Spectrometry Eluted Ligands (EL) peptides. The BA data covers 201 MHC molecules from human (HLA-A, B, C, E), mouse (H-2), cattle (BoLA), primates (Patr, Mamu, Gogo), swine (SLA) and equine (Eqca). The EL data covers 289 MHC molecules from human (HLA-A, B, C, E), mouse (H-2), cattle (BoLA), primates (Patr, Mamu, Gogo), swine (SLA), equine (Eqca) and dog (DLA). Furthermore, the user can obtain predictions to any custom MHC class I molecule by uploading a full length MHC protein sequence. Predictions can be made for peptides of any length.

Note, as of 28/7/2020 the server has been updated (retrained on data resolving a curation error in the IEDB for a single allele (SA) eluted ligand H2-Db/H2-Kb data set. This recuration only affected ~2000 H2 data points, but has minor impacts on predictions for all MHC's.

To access the earlier version of NetMHCpan-4.1 click here version 4.1a

Note also, if you have installed the earlier version of NetMHCpan-4.1, click her e to download the updated data file data.tar.gz, and a file with the update test directory test.tar.gz.

The server returns as default the likelihood of a peptide being a natural ligand of the selected MHC(s). If selected, also the predicted binding affinity is rseported.

New in this version: together with Binding Affinity (BA) data, the method has now been trained on EL data from Single Allele (SA, peptides annotated to a single MHC) and Multi Allele (MA, peptides annotated to multiple MHCs) sources. The use of EL MA data is possible due to an upgrade af NNAlign (the core algorithm of NetMHCpan) called NNALign_MA (PMID: 31578220), which enables pseudo-labelling.

View the version history of this server. All previous versions are available online, for comparison and reference.

The project is a collaboration between CBS, and LIAI.


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