NetH2pan - 1.0

Prediction of peptide interactions with murine MHC class I (H2) molecules.

All previous versions are available online, for comparison and reference.

The NetH2pan server is a version of NetMHCpan dedicated to mouse MHC molecules.
The method was trained on binding affinity and ligand elution data from nine different H-2 class I molecules. By default it predicts LIGAND likelihood scores, but it can be toggled to return predicted BINDING AFFINITY scores.


Hover the mouse cursor over the symbol for a short description of the options

Type of input

Paste a single sequence or several sequences in FASTA format into the field below:

or submit a file in FASTA format directly from your local disk:

Peptide length (you may select multiple lengths):  

Select species/loci

Select Allele (max 20 per submission) or type allele names (i.e. H-2-Dq) separated by commas (and no spaces).

or paste a single full length MHC protein sequence in FASTA format into the field below:

or submit a file containing a full length MHC protein sequence in FASTA format directly from your local disk:

Threshold for strong binder: % Rank 
Threshold for weak binder: % Rank 

Make BA predictions  

Sort by predicted affinity 

Save predictions to XLS file 

At most 5000 sequences per submission; each sequence not more than 20,000 amino acids and not less than 8 amino acids. Max 20 MHC alleles per submission.

The sequences are kept confidential and will be deleted after processing.


For publication of results, please cite:

  • NetH2pan: A Computational Tool to Guide MHC peptide prediction on Murine Tumors
    Christa I. DeVette, Massimo Andreatta, Wilfried Bardet, Steven J. Cate, Vanessa I. Jurtz, Kenneth W. Jackson, Alana L. Welm, Morten Nielsen, William H. Hildebrand
    Cancer Immunology Research (2018) DOI: 10.1158/2326-6066.CIR-17-0298
    Full text  
  • NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data
    Vanessa Jurtz, Sinu Paul, Massimo Andreatta, Paolo Marcatili, Bjoern Peters and Morten Nielsen
    The Journal of Immunology (2017) ji1700893; DOI: 10.4049/jimmunol.1700893
    Full text  


Data resources used to develop this server was obtained from

  • IEDB database.
    • Quantitative peptide binding data were obtained from the IEDB database.
  • IMGT/HLA database. Robinson J, Malik A, Parham P, Bodmer JG, Marsh SGE: IMGT/HLA - a sequence database for the human major histocompatibility complex. Tissue Antigens (2000), 55:280-287.
    • MHC protein sequences were obtained from the IMGT/HLA database (version 3.1.0).

Usage instructions

1. Specify the input sequences

All the input sequences must be in one-letter amino acid code. The alphabet is as follows (case sensitive):

A C D E F G H I K L M N P Q R S T V W Y and X (unknown)

Any other symbol will be converted to X before processing.

The server allows for input in either FASTA or PEPTIDE format.

Sequences can be submitted in the following two formats:

  • Paste a single sequence (just the amino acids) or a number of sequences in FASTA format or a list of peptides into the upper window of the main server page.

  • Select a FASTA or PEPTIDE file on your local disk, either by typing the file name into the lower window or by browsing the disk.

At most 5000 sequences per submission; each sequence not more than 20,000 amino acids and not less than 8 amino acids.

2. Customize your run

1. Specify peptide length (only for FASTA input). By default input proteins are digested into 9-mer peptides.

2. Select species/loci from the scroll-down menu.

3. Select allele(s) from the scroll-down menu or type in the allele names separated by commas (without blank spaces). If you choose to type in the allele names, you can consult the List of MHC molecule names.; use the molecule names in the first column.

4. Optionally specify thresholds for strong and weak binders. They are expressed in terms of %Rank, that is percentile of the predicted binding affinity compared to the distribution of affinities calculated on set of random natural peptides. The peptide will be identified as a strong binder if it is found among the top x% predicted peptides, where x% is the specified threshold for strong binders (by default 0.5%). The peptide will be identified as a weak binder if the % Rank is above the threshold of the strong binders but below the specified threshold for the weak binders (by default 2%).

5. Tick the box Make BA predictions to predict binding affinity scores. By default, the method returns scores of eluted ligand likelihood.

6. Tick the box Sort by affinity to have the output sorted by descending predicted binding affinity.

3. Submit the job

Click on the "Submit" button. The status of your job (either 'queued' or 'running') will be displayed and constantly updated until it terminates and the server output appears in the browser window.

At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; when it terminates you will be notified by e-mail with a URL to your results. They will be stored on the server for 24 hours.

4. Output

A description of the output format can be found on the output tab.

Output format


The prediction output for each molecule consists of the following columns:

  • Pos Residue number (starting from 0)

  • HLA Molecule/allele name

  • Peptide Amino acid sequence of the potential ligand

  • Core The minimal 9 amino acid binding core directly in contact with the MHC

  • Of The starting position of the Core within the Peptide (if > 0, the method predicts a N-terminal protrusion)

  • Gp Position of the deletion, if any.

  • Gl Length of the deletion.

  • Ip Position of the insertions, if any.

  • Il Length of the insertion.

  • Icore Interaction core. This is the sequence of the binding core including eventual insertions of deletions.

  • Identity Protein identifier, i.e. the name of the Fasta entry.

  • Score The raw prediction score

  • Aff(nM) Predicted binding affinity in nanoMolar units (if binding affinity predictions is selected).

  • %Rank Rank of the predicted affinity compared to a set of random natural peptides. This measure is not affected by inherent bias of certain molecules towards higher or lower mean predicted affinities. Strong binders are defined as having %rank<0.5, and weak binders with %rank<2. We advise to select candidate binders based on %Rank rather than nM Affinity

  • BindLevel (SB: strong binder, WB: weak binder). The peptide will be identified as a strong binder if the % Rank is below the specified threshold for the strong binders, by default 0.5%. The peptide will be identified as a weak binder if the % Rank is above the threshold of the strong binders but below the specified threshold for the weak binders, by default 2%.


    - Fasta input:

    >sp|P06437|GB_HHV1K Envelope glycoprotein B OS=Human herpesvirus 1 (strain KOS) GN=gB PE=1 SV=2

    - Peptide length: 8, 9, 10, 11
    - Allele: H-2-Kb

    will return the following predictions:

    # NetMHCpan version 4.0_H2
    # Tmpdir made /usr/opt/www/webface/tmp/server/netmhcpan/593AB384000035D69F4553E2/netMHCpane1KUiY
    # Input is in FSA format
    # Peptide length 8,9,10,11
    # Make Eluted ligand likelihood predictions
    H-2-Kb : Distance to training data  0.000 (using nearest neighbor H2-Kb)
    # Rank Threshold for Strong binding peptides   0.500
    # Rank Threshold for Weak binding peptides   2.000
      Pos          HLA         Peptide       Core Of Gp Gl Ip Il        Icore        Identity     Score   %Rank  BindLevel
      499        H2-Kb        SSIEFARL  SSIEF-ARL  0  0  0  5  1     SSIEFARL sp_P06437_GB_HH 0.9981590  0.0005 <= SB
      391        H2-Kb       ISTTFTTNL  ISTTFTTNL  0  0  0  0  0    ISTTFTTNL sp_P06437_GB_HH 0.9163720  0.0420 <= SB
      280        H2-Kb        SVYPYDEF  SVYP-YDEF  0  0  0  4  1     SVYPYDEF sp_P06437_GB_HH 0.9095360  0.0447 <= SB
      793        H2-Kb       FAFRYVMRL  FAFRYVMRL  0  0  0  0  0    FAFRYVMRL sp_P06437_GB_HH 0.8978500  0.0493 <= SB
      506        H2-Kb        LQFTYNHI  LQFT-YNHI  0  0  0  4  1     LQFTYNHI sp_P06437_GB_HH 0.8668980  0.0756 <= SB
      154        H2-Kb      IAPYKFKATM  IAYKFKATM  0  2  1  0  0   IAPYKFKATM sp_P06437_GB_HH 0.8105570  0.1299 <= SB
      727        H2-Kb       ANAAMFAGL  ANAAMFAGL  0  0  0  0  0    ANAAMFAGL sp_P06437_GB_HH 0.8054410  0.1355 <= SB
      146        H2-Kb       IAVVFKENI  IAVVFKENI  0  0  0  0  0    IAVVFKENI sp_P06437_GB_HH 0.8039780  0.1371 <= SB
      728        H2-Kb        NAAMFAGL  NAAM-FAGL  0  0  0  4  1     NAAMFAGL sp_P06437_GB_HH 0.7846630  0.1562 <= SB
      155        H2-Kb       APYKFKATM  APYKFKATM  0  0  0  0  0    APYKFKATM sp_P06437_GB_HH 0.7813300  0.1590 <= SB
      392        H2-Kb        STTFTTNL  ST-TFTTNL  0  0  0  2  1     STTFTTNL sp_P06437_GB_HH 0.7616710  0.1751 <= SB
      845        H2-Kb        EMIRYMAL  EMIR-YMAL  0  0  0  4  1     EMIRYMAL sp_P06437_GB_HH 0.7440680  0.1871 <= SB
      794        H2-Kb        AFRYVMRL  A-FRYVMRL  0  0  0  1  1     AFRYVMRL sp_P06437_GB_HH 0.7372430  0.1918 <= SB..
    Protein sp_P06437_GB_HH. Allele H2-Kb. Number of high binders 26. Number of weak binders 52. Number of peptides 3582

    Article abstracts


    NetH2pan: A Computational Tool to Guide MHC peptide prediction on Murine Tumors

    Christa I. DeVette 1, Massimo Andreatta 2, Wilfried Bardet 1, Steven J. Cate 1, Vanessa I. Jurtz 3, Kenneth W. Jackson 1, Alana L. Welm 4, Morten Nielsen 2,3, and William H. Hildebrand1

    Cancer Immunology Research (2018)

    1 University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
    2 Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina.
    3 Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
    4 Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.

    With the advancement of personalized cancer immunotherapies, new tools are needed for identifying tumor antigens and evaluating T cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the MMTV-PyMT mouse – an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a B6 genetic background, in order to leverage well-developed B6 immunological tools, results in delayed tumor development and variable metastatic phenotypes. Therefore, we initiated characterization of the FVB MHC Class I H-2-q haplotype to establish useful immunological tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2-q MHC Class I alleles, including >8500 unique peptide ligands, a multi-allele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer.

    PMID: 29615400  


    NetMHC pan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data

    Vanessa Jurtz 1, Sinu Paul 2, Massimo Andreatta 3, Paolo Marcatili 1, Bjoern Peters 2, and Morten Nielsen1,3

    The Journal of Immunology (2017)

    1 Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark
    2 Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, CA92037 La Jolla, USA
    3 Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina

    Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC (major histocompatibility complex) class I molecules. Peptide binding to MHC molecules is the single most selective step in the antigen presentation pathway. On the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has therefore attracted large attention. In the past, predictors of peptide-MHC interaction have in most cases been trained on binding affinity data. Recently an increasing amount of MHC presented peptides identified by mass spectrometry has been published containing information about peptide processing steps in the presentation pathway and the length distribution of naturally presented peptides. Here, we present NetMHCpan-4.0, a method trained on both binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increased predictive performance compared to state-of-the-art both when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

    PMID: 28978689  

    Full text: [PDF]

    NetMHCpan - MHC class I binding prediction beyond humans
    Hoof I1, Peter B3, Sidney J3, Pedersen LE2 Lund O1, Buus S2, Nielsen M1

    Immunogenetics. 2009 Jan;61(1):1-13.

    1Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
    2Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark
    3La Jolla Institute for Allergy and Immunology, San Diego, California, United States of America

    Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide-MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method's ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.

    PMID: 19002680

    Full text

    Software Downloads


    Correspondence:        Technical Support: