NetCTLpan - 1.1

Pan-specific integrated class I antigen presentation

NetCTLpan 1.1 server predicts CTL epitopes in protein sequences. The current version 1.1 is an update to the original NetCTL server that allows for prediction of CTL epitope with restriction to any MHC molecules of known protein sequence.

NOTE. New in this version. The method has been updated to include the newest MHC allele releases from the IMGT/HLA and IPD-MHC databases (for non-human primates and pig). This includes adaptation of the new nomenclature for HLA and Rhesus macaque (Mamu) alleles. The MHC binding predictions has been updated to NetMHCpan version 2.3.

Predictions can be made for 8-11mer peptides. Note that all non 9mer predictions are made using approximations. Most HLA molecules have a strong preference for binding 9mers.

The method integrates prediction of peptide MHC class I binding, proteasomal C terminal cleavage and TAP transport efficiency. MHC class I binding and proteasomal cleavage is performed using artificial neural networks. TAP transport efficiency is predicted using weight matrix.

The prediction values are calculated as a weighted average of the MHC, TAP and C terminal cleavage scores. and as %-Rank to a set of 200.000 random natural peptides.

The MHC peptide binding is predicted using neural networks trained as described for the NetMHCpan-4.0 server. The proteasome cleavage event is predicted using the version of the NetChop neural networks trained on C terminals of known CTL epitopes as describe for the NetChop-3.0 server. The TAP transport efficiency is predicted using the weight matrix based method describe by Peters et al., 2003

Species Warning. Note, that both the proteasome and TAP predictions were developed using experimental data for human versions of the molecule. At least for TAP molecules, there are known to be some species dependent differences in specificity. Therefore, using these predictions for eptitope processing in non-human cells should only be done with extra caution in interpreting results.


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

Submit a file in FASTA format directly from your local disk:

Select species

Select Allele (max 20 per submission) or type allele names (ie HLA-A0101) separated by commas (and no spaces). Max 20 alleles per submission)

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

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:

Peptide length 

Threshold for showing predictions  

Weight on C terminal cleavage          
Weight on TAP transport efficiency  
Threshold for epitope identification 

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. Max 20 MHC alleles per submission.

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


For publication of results, please cite:

  • NetCTLpan - Pan-specific MHC class I epitope predictions
    Stranzl T., Larsen M. V., Lundegaard C., Nielsen M.
    Immunogenetics. 2010 Apr 9. [Epub ahead of print]
  • PMID: 20379710
  • Full text


Data resources used to develop this server was obtained from

  • IEDB database.
    • Quantitative peptide binding data were obtained from the IEDB database.


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 allele(s) you want to make predictions for from the scroll-down menu (select multiple alleles using the ctrl key), or type in the allele names separated by commas (with out blank spaces).

Give threshold value for binding values to be displayed.

Click the box Sort by affinity to have the output sorted by descending predicted binding affinity

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 11 columns.

  • Prediction number
  • Protein identifier
  • HLA Allele
  • Peptide sequence
  • MHC Prediction score (in 1-log50K(aff) uniqs)
  • TAP Prediction score
  • Cleavage Prediction score
  • Combined Prediction score
  • %Random - %Rank of prediction score to a set of 1000.000 random natural 9mer peptides
  • Epitope assignment


    # NetCTLpan version 1.1
    # Peptide length 9
    # NetCTLpan predictions for HLA-A01:01 allele.
    #  N   Sequence Name       Allele      Peptide      MHC      TAP      Cle     Comb  %Rank
       0 143B_BOVIN__P29    HLA-A01:01    TMDKSELVQ  0.10500 -0.18300  0.16188  0.13685  50.00 
       1 143B_BOVIN__P29    HLA-A01:01    MDKSELVQK  0.02300  0.21200  0.53837  0.14943  50.00 
       2 143B_BOVIN__P29    HLA-A01:01    DKSELVQKA  0.01200 -0.77000  0.78670  0.16976  50.00 
       3 143B_BOVIN__P29    HLA-A01:01    KSELVQKAK  0.07600  0.32900  0.45985  0.18769  32.00 
       4 143B_BOVIN__P29    HLA-A01:01    SELVQKAKL  0.01400  0.99100  0.91927  0.24561  32.00 
     235 143B_BOVIN__P29    HLA-A01:01    DEGDAGEGE  0.00300 -2.21000  0.04146 -0.04292  50.00 
     236 143B_BOVIN__P29    HLA-A01:01    EGDAGEGEN  0.02000 -2.10100  0.05666 -0.01978  50.00 
    Number of MHC ligands 4 identified. Number of peptides 237. Allele HLA-A0101. Protein name 143B_BOVIN__P29


    Main reference:

    NetCTLpan. Pan-specific MHC class I pathway epitope predictions.
    Stranzl T., Larsen M. V., Lundegaard C., Nielsen M.
    Immunogenetics. 2010 Apr 9. [Epub ahead of print]

    Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark

    Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific MHC class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I binding affinities into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8, 9, 10 and 11-mer epitopes. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity.

    The method was trained and validated on large data sets of experimentally identified MHC class I ligands and CTL epitope. It has been reported, that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC differences and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules.

    The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further illustrate the importance of using full-type HLA restriction information when identifying MHC class-I epitopes. When compared to the NetMHCpan and NetCTL methods, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40% respectively.

    The method and benchmark data set are available at

    PMID: 20379710

    Full text

    Supplementary material for the NetCTLpan method (version 1.0):

    Original method

    NetCTLpan. Pan-specific MHC class I pathway epitope predictions.
    Thomas Stranzl, Mette Voldby Larsen, Claus Lundegaard, and Morten Nielsen

    The data sets used in the benchmark calculationis are given below in the FASTA format. For each entry is given the Uniprot of the protein "hosting" the epitope, the epitope sequence, and the HLA full-type information (if applicable), and the HLA supertype (if applicable).

    An example showing part of such a fasta file is given below

    >sp|O43707|ACTN4_HUMAN  AIDQLHLEY       HLA-A*0101      A1

    SYFPEITHI 9mer training data set
    SYFPEITHI 9mer evaluation data set
    SYFPEITHI 8,10, and 11mer evaluation data set
    SYFPEITHI HLA-C evaluation data set
    HIV dataset
    Supplementary Table. HLA supertype association for the HLA alleles used in the study


    Stranzl T., Larsen M. V., Lundegaard C., and Nielsen M. NetCTLpan. Pan-specific MHC class I pathway epitope predictions. Paper Submitted

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