DTU Health Tech

Department of Health Technology

NetBoLApan - 1.0

Prediction of peptide interactions with bovine MHC class I (BoLA) molecules.

The NetBoLApan server is a version of NetMHCpan-4.0 dedicated to bovine MHC molecules.

NetBoLApan server predicts binding of peptides to any BoLA molecule of known sequence using artificial neural networks (ANNs). The method is trained on a combinatino of more than 180,000 quantitative binding data and MS derived MHC eluted ligands. The binding affinity data covers 172 MHC molecules from human (HLA-A, B, C, E), mouse (H-2), cattle (BoLA), primates (Patr, Mamu, Gogo) and swine (SLA). The MS eluted ligand data covers 61 allelee, including 6 BoLA.

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. BoLA-1:00901) 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:

  • Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data
    M Nielsen, T Connelley, N Ternette
    Journal of proteome research (2017) - ACS Publications
    PMID: 29115832  
  • 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  
  • NetMHCpan, a method for MHC class I binding prediction beyond humans
    Ilka Hoof, Bjoern Peters, John Sidney, Lasse Eggers Pedersen, Ole Lund, Soren Buus, and Morten Nielsen
    Immunogenetics 61.1 (2009): 1-13
    PMID: 19002680   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 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.

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%.


    Peptide vs. iCore vs. Core

    Three amino acid sequences are reported for each row of predictions:
    The Peptide is the complete amino acid sequence evaluated by NetMHCpan. Peptides are the full sequences submitted as a peptide list, or the result of digestion of source proteins (Fasta submission)
    The iCore is a substring of Peptide, encompassing all residues between P1 and P-omega of the MHC. For all intents and purposes, this is the minimal candidate ligand/epitope that should be considered for further validation.
    The Core is always 9 amino acids long, and is a construction used for sequence aligment and identification of binding anchors.


    Fasta input:


    Peptide length: 8, 9, 10, 11, 12
    Allele: HLA-A*0301
    Toggle Sort by prediction score

    will return the following predictions:
    # NetMHCpan version 4.0
    # Tmpdir made /usr/opt/www/webface/tmp/server/netmhcpan/59DBCCFF00005A84DAFF1311/netMHCpanVszuD8
    # Input is in FSA format
    # Peptide length 8,9,10,11,12
    # Make Eluted ligand likelihood predictions
    HLA-A03:01 : Distance to training data  0.000 (using nearest neighbor HLA-A03:01)
    # 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
       15  HLA-A*03:01       HQAAMQMLK  HQAAMQMLK  0  0  0  0  0    HQAAMQMLK     Gag_180_209 0.5697290  0.2857 <= SB
       14  HLA-A*03:01      GHQAAMQMLK  GQAAMQMLK  0  1  1  0  0   GHQAAMQMLK     Gag_180_209 0.2137130  1.1582 <= WB
        7  HLA-A*03:01       TMLNTVGGH  TMLNTVGGH  0  0  0  0  0    TMLNTVGGH     Gag_180_209 0.0487720  3.0466
        8  HLA-A*03:01       MLNTVGGHQ  MLNTVGGHQ  0  0  0  0  0    MLNTVGGHQ     Gag_180_209 0.0319510  3.7842
       13  HLA-A*03:01     GGHQAAMQMLK  GQAAMQMLK  0  1  2  0  0  GGHQAAMQMLK     Gag_180_209 0.0313010  3.8215
       12  HLA-A*03:01    VGGHQAAMQMLK  VQAAMQMLK  0  1  3  0  0 VGGHQAAMQMLK     Gag_180_209 0.0166440  5.2079
       15  HLA-A*03:01      HQAAMQMLKE  HQAAMQMLK  0  0  0  0  0    HQAAMQMLK     Gag_180_209 0.0124970  5.9719
       16  HLA-A*03:01        QAAMQMLK  QAA-MQMLK  0  0  0  3  1     QAAMQMLK     Gag_180_209 0.0086270  7.1279
       21  HLA-A*03:01       MLKETINEE  MLKETINEE  0  0  0  0  0    MLKETINEE     Gag_180_209 0.0079270  7.4157
    Protein Gag_180_209. Allele HLA-A*03:01. Number of high binders 1. Number of weak binders 1. Number of peptides 105
    Link to Allele Frequencies in Worldwide Populations HLA-A03:01

    Article abstracts




    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

    Submitted (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.

    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


    If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0) and the options you have selected. If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).

    If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.

    Correspondence: Technical Support: