NetBoLAIIpan - 1.0

Prediction of peptide interactions with bovine MHC Class II BoLA-DRB3 molecules

Submission is accepted in two formats - as a list of peptides or as a protein sequence in FASTA format. A comprehensive list of MHC molecules is available for prediction, alternatively the user can upload their MHC protein sequence of interest.

The prediction values are given likelihood for MHC antigen presentation and %Rank score. The percentile rank for a peptide is generated by comparing its score against the scores of 100,000 random natural peptides. For example, if a peptide is assigned a rank of 1%, it means that its predicted affinity is among the top 1% scores for the specified molecule.

Strong and weak binding peptides are identified based on %Rank, with customizable thresholds. You may sort the output based on predicted binding affinity and filter out non-binders.


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


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

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

... or load some sample data:


Use context encoding


Select Allele(s) (max. 20 per submission)

... or type a list of molecules names separated by commas without spaces (max 20 per submission)

For the list of available molecule names click here

Alternatively, upload full length BoLA-DRB3 protein sequences:


Threshold for strong binder (% Rank)  

Threshold for weak binder (% Rank)  

Turn on filtering options 

Print only the strongest binding core 

Sort output by prediction score 

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:

  • Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR-presented peptides and epitopes.
    Fisch A, Reynisson B, Benedictus L, Nicastri A, Vasoya D, Morrison I, Buus S, Ferreira BR, Santos IKFM, Ternette N, Connelley T, Nielsen M
    BioRxiv doi:



In this section, the user must define the input for the prediction server following these steps:

1) Specify the desired type of input data (FASTA or PEPTIDE ) using the drop down menu.

2) Provide the input data by means of pasting the data into the blank field, uploading it using the "Choose File" button or by loading sample data using the "Load Data" button. 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. At most 5000 sequences are allowed per submission; each sequence must be not more than 20,000 amino acids long and not less than 9 amino acids long.

3) If FASTA was selected as input type, the user must select the peptide length(s) the prediction server is going to work with. NetMHCIIpan-4.0 will "chop" the input FASTA sequence in overlapping peptides of the provided length and will predict binding against all of them. By default input proteins are digested into 15-mer peptides. Note that, if PEPTIDE was selected as input type, this step is unnecessary and thus the peptide length selector will directly not appear in the interface.

4)Context encoding informs the network of the proteolytic context the ligand. Context is automatically generated from the source protein if the user selects FASTA format. Briefly, context is made up of 12 amino acids: 3 amino acids upstream of the ligand, 3 first amino acids at the ligand N-terminus, 3 last amino acids at the ligand C-terminus and 3 amino acids downstream the ligand(in the source protein), all concatenated together. If the input type is PEPTIDE , the user must specify the ligand context(see PEPTIDECONT ).



In this section, the user must define which MHC molecule(s) the input data is going to be predicted against:

1) Here the user can select from a list of MHC molecules by first selecting the species/loci and clicking MHCs in the list. Note that for DP and DQ alleles, both ALPHA and BETA chains must be selected.

2) The user can also type the molecule names. Note, that for HLA-DP and HLA-DQ alleles, ALPHA and BETA chains must both be typed. Please consult List of MHC molecule names. Note that molecules selected from step 1. populate this bar.

3) If the molecule of interest is not provided in the lists, the user can input ALPHA and BETA sequences in fasta format(for HLA-DR, only the BETA chain is needed). With this option, rank score predictions are not available.


In this section, the user may define additional parameters to further customize the run:

1) 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 2%). 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 10%).

2) Tick this option to include also Binding Affinity predictions together with Eluted Ligand likelihood.

3) Tick this option to output only peptides with a % Rank score below a specified threshold. Useful for large submissions.

4) Tick this box to output only the strongest binding core.

5) Tick this box to have the output sorted by descending prediction score.

6) Enable this option to export the prediction output to .XLS format (readable for most spreadsheet softwares, like Microsoft Excel).
    Three "common" columns:
    1. Pos: peptide position (starting from 0).
    2. Peptide: peptide sequence.
    3. ID: Protein ID.
    5 additional columns for each MHC selected for prediction:
    4. EL-score: raw eluted ligand likelihood prediction score.
    5. EL_Rank: Rank of the predicted eluted ligand likelihood score compared to a set of random natural peptides.
    6. BA-score: if BA output was selected, this column will display the raw binding affinity prediction score.
    7. nM: if BA output was selected, this column will display the predicted IC50 binding values (in nM).
    8. BA_Rank: if BA output was selected, this column will display the binding affinity Rank score.
    And, finally, two last "common" columns:
    9. Ave: average over the raw EL predictions.
    10. NB: number of alleles a given peptide binds with EL %RANK threshold < 10%.


After the user has finished the "INPUT DATA", "MHC SELECTION" and "ADDITIONAL CONFIGURATION" steps, the submission can now be done. To do so, the user can click on "Submit" to submit the job to the processing server, or click on "Clear fields" to clear the page and start over.

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.

After the server has finished running the corresponding predictions, an output page will be delivered to the user. A description of the output format can be found HERE.

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


For the following FASTA input example:


With parameters:

Peptide length: 15
Allele: DRB1_0101
Sort by prediction score: On

NetMHCIIpan-4.0 will return the following output (showing the first 12 predicted peptides):

# NetMHCIIpan version 4.0

# Input is in FASTA format

# Peptide length 15

# Prediction Mode: EL

# Threshold for Strong binding peptides (%Rank) 2%
# Threshold for Weak binding peptides (%Rank) 10%

# Allele: DRB1_0101
 Pos           MHC              Peptide   Of        Core  Core_Rel        Identity      Score_EL %Rank_EL Exp_Bind  BindLevel
  40     DRB1_0101      QGQWRGAAGTAAQAA    3   WRGAAGTAA     1.000          P9WNK5      0.826571     0.45       NA   <=SB
  39     DRB1_0101      LQGQWRGAAGTAAQA    4   WRGAAGTAA     1.000          P9WNK5      0.729407     0.77       NA   <=SB
  41     DRB1_0101      GQWRGAAGTAAQAAV    2   WRGAAGTAA     1.000          P9WNK5      0.451999     1.97       NA   <=SB
  38     DRB1_0101      SLQGQWRGAAGTAAQ    5   WRGAAGTAA     1.000          P9WNK5      0.420826     2.17       NA   <=WB
  53     DRB1_0101      AAVVRFQEAANKQKQ    3   VRFQEAANK     0.907          P9WNK5      0.096784     6.99       NA   <=WB
  73     DRB1_0101      STNIRQAGVQYSRAD    3   IRQAGVQYS     1.000          P9WNK5      0.067163     8.66       NA   <=WB
  26     DRB1_0101      KTQIDQVESTAGSLQ    3   IDQVESTAG     0.993          P9WNK5      0.066535     8.70       NA   <=WB
  42     DRB1_0101      QWRGAAGTAAQAAVV    1   WRGAAGTAA     0.947          P9WNK5      0.066096     8.73       NA   <=WB
  52     DRB1_0101      QAAVVRFQEAANKQK    4   VRFQEAANK     0.860          P9WNK5      0.053628     9.80       NA   <=WB
  14     DRB1_0101      EAGNFERISGDLKTQ    4   FERISGDLK     0.993          P9WNK5      0.044413    10.82       NA       
  54     DRB1_0101      AVVRFQEAANKQKQE    2   VRFQEAANK     0.573          P9WNK5      0.043962    10.87       NA       
  72     DRB1_0101      ISTNIRQAGVQYSRA    4   IRQAGVQYS     1.000          P9WNK5      0.038268    11.70       NA       


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

  • Pos Residue number (starting from 0)

  • MHC MHC molecule name

  • Peptide Amino acid sequence

  • Of Starting position offset of the optimal binding core (starting from 0)

  • Core Binding core register

  • Core_Rel Reliability of the binding core, expressed as the fraction of networks in the ensemble selecting the optimal core

  • Identity Annotation of the input sequence, if specified

  • Score_EL Eluted ligand prediction score

  • %Rank_EL Percentile rank of eluted ligand prediction score

  • Exp_bind If the input was given in PEPTIDE format with an annotated affinity value (mainly for benchmarking purposes).

  • Score_BA Predicted binding affinity in log-scale (printed only if binding affinity predictions were selected)

  • Affinity(nM) Predicted binding affinity in nanomolar IC50 (printed only if binding affinity predictions were selected)

  • %Rank_BA % Rank of predicted affinity compared to a set of 100.000 random natural peptides. This measure is not affected by inherent bias of certain molecules towards higher or lower mean predicted affinities (printed only if binding affinity predictions were selected)

  • 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. 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.

  • Article abstracts

    Integral use of immunopeptidomics and immunoinformatics for the characterization of antigen presentation and rational identification of BoLA-DR-presented peptides and epitopes.
    Fisch A, Reynisson B, Benedictus L, Nicastri A, Vasoya D, Morrison I, Buus S, Ferreira BR, Santos IKFM, Ternette N, Connelley T, Nielsen M
    BioRxiv doi:

    Major histocompatibility complex (MHC) peptide binding and presentation is the most selective event defining the landscape of T cell epitopes. Consequently, understanding the diversity of MHC alleles in a given population and the parametersthat define the set of ligands that can be bound and presented by each of these alleles (the immunopeptidome) has an enormous impact on our capacity to predict and manipulate the potential of protein antigens to elicit functional T cell responses. Liquid chromatography-mass spectrometry (LC-MS) analysis of MHC eluted ligands (EL data) has proven to be a powerful technique for identifying such peptidomes, and methods integrating such data for prediction of antigen presentation have reached a high level of accuracy for both MHC class I and class II. Here, we demonstrate how these techniques and prediction methods can be readily extended to the bovine leukocyte antigen class II DR locus (BoLA-DR). BoLA-DR binding motifs were characterized by EL data derived from cell lines expressing a range of DRB3 alleles prevalent in Holstein-Friesian populations. The model generated (NetBoLAIIpan - available as a web-server at ) was shown to have unprecedented predictive power to identify known BoLA-DR restricted CD4 epitopes. In summary, the results demonstrate the power of an integrated approach combining advanced MS peptidomics with immunoinformatics for characterization of the BoLA-DR antigen presentation system and provide a novel tool that can be utilised to assist in rational evaluation and selection of bovine CD4 T cell epitopes.


    Correspondence:        Technical Support: