NetTepi - 1.0

T-cell epitopes restricted to prevalent HLA-A and HLA-B molecules

NetTepi 1.0 predicts T-cell epitopes from protein sequences. The method integrates three prediction types, peptide-MHC binding affinity, peptide-MHC stability and T-cell propensity.

The server allows for predictions of T-cell epitopes restricted to 13 different Human MHC (HLA) alleles representing 11 of the 12 common HLA-A and B Supertypes as defined by Lund et al (2004).

Predictions of lengths 8-14: Predictions can be made for lengths between 8 and 14 for all alleles using an approximation algorithm. Note that only lengths 9 and 10 have been thoroughly benchmarked. Caution should be taken with predictions for all other lengths.

Prediction values are calculated as a weighted sum of binding affinity, stability and T-cell propensity prediction scores. A % Rank score based on predictions for 200.000 random natural peptides is also provided.

Peptide-MHC binding affinity predictions are obtained using the NetMHCcons method. Peptide-MHC stability predictions are obtained using the NetMHCstab method. T-cell propensity is predicted using the immunogenicity model described by Calis et al (2013).


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 (several lengths are possible): 

Select loci/species

Select Allele(s) or type allele names (ie HLA-A01:01) separated by commas (and no spaces).

For list of allowed allele names click here List of MHC allele names.

Relative weight on stability prediction: 
Relative weight on T cell propensity prediction: 

Sort by score  

Save prediction 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.

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


For publication of results, please cite:

NetTepi: an integrated method for the prediction of T cell epitopes. Trolle T, Nielsen M. Immunogenetics (2014). 66(7-8):449-56.


1. Specify the input sequences

All the input sequences must be in one-letter amino acid code. The allowed alphabet (not case sensitive) is as follows:

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

All the other symbols will be converted to X before processing.

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

Note that for Peptide input, all peptides MUST of equal length. Note also, that you must click the box Click if input is PEPTIDE format if the input is in peptide format.

The sequences can be input in the following two ways:

  • 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.

Both ways can be employed at the same time: all the specified sequences will be processed. However, there may be not more than 10 sequences in total in one submission. The sequences shorter than 15 or longer than 10000 amino acids will be ignored.

2. Customize your run

Select the peptide length(s) and allele(s) you want to make predictions for from the scroll-down menus. It is possible to select multiple lengths and/or alleles using the ctrl key. Alleles may also be selected by typing the allele names separated by commas (with out blank spaces).

The relative weights on stability and t-cell propensity may be customized. Note that the default values have been thoroughly benchmarked and are highly recommended.

Use the drop down menu to select a prediction score type to sort the output by. The output is sorted by peptide sequence by default.

Click the box save prediction to xls file to save the raw prediction output to an excel file. This file will be available in the bottum of the results output file.

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; you will be notified by e-mail when it has terminated. The e-mail message will contain the URL under which the results are stored; they will remain on the server for 24 hours for you to collect them.

Output format


The prediction output consists of 9 columns.

  • Residue number
  • HLA Allele
  • Peptide sequence
  • Protein identifier
  • NetMHCpan prediction score
  • NetMHCstab prediction score
  • T-cell propensity score
  • NetTepi combined prediction score
  • %Rank of prediction score to a set of 100.000 random natural 9mer peptides


    # NetTepi version 1.0
    # Input is in FASTA format
    # Peptide lengths: 9
     Pos       Allele      Peptide          Identity      Aff     Stab    Tcell     Comb   %Rank
       0   HLA-A02:01    ASQKRPSQR   seq2_optional_c    0.020    0.005   -0.422   -0.027   50.00       
       1   HLA-A02:01    SQKRPSQRH   seq2_optional_c    0.019    0.006   -0.252   -0.010   50.00       
       2   HLA-A02:01    QKRPSQRHG   seq2_optional_c    0.013    0.005   -0.202   -0.010   50.00       
       3   HLA-A02:01    KRPSQRHGS   seq2_optional_c    0.021    0.007   -0.187   -0.002   50.00       
       4   HLA-A02:01    RPSQRHGSK   seq2_optional_c    0.018    0.004   -0.157   -0.002   50.00 

    Article abstracts

    Main reference:

    NetTepi: an integrated method for the prediction of T-cell epitopes
    Thomas Trolle1, Morten Nielsen1,2,

    1 Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
    2 Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, San Martin, Buenos Aires, Argentina

    Multiple factors determine the ability of a peptide to elicit a cytotoxic T-cell lymphocyte response. Binding to a major histocompatibility complex class I (MHC-I) molecule is one of the most essential factors, as no peptide can become a T-cell epitope unless presented on the cell surface in complex with an MHC-I molecule. As such, peptide-MHC (pMHC) binding affinity predictors are currently the premier methods for T-cell epitope prediction, and these prediction methods have been shown to have high predictive performances in multiple studies. However, not all MHC-I binders are T-cell epitopes, and multiple studies have investigated what additional factors are important for determining the immunogenicity of a peptide. A recent study suggested that pMHC stability plays an important role determining if a peptide becoming a T-cell epitope. Likewise, a T-cell propensity model has been proposed for identifying MHC binding peptides with amino acid compositions favoring T-cell receptor interactions. In this study, we investigate if improved accuracy for T cell epitope discovery can be achieved by integrating predictions for pMHC binding affinity, pMHC stability and T-cell propensity. We show that a weighted sum approach allows pMHC stability and T-cell propensity predictions to enrich pMHC binding affinity predictions. The integrated model leads to a consistent and significant increase in predictive performance and we demonstrate how this can be utilized to decrease the experimental workload of epitope screens. The final method, NetTepi, is publically available at http://services.healthtech.dtu.dk/service.php?NetTepi-1.0.

    Training and Evaluation Data

    Evaluation Data
    Training Data

    Software Downloads

    • Version 1.0


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