DTU Health Tech
Department of Health Technology
DTU Health Tech
Department of Health Technology
NetMHCIIpan 3.2 server predicts binding of peptides to MHC class II molecules. The predictions are available for the three human MHC class II isotypes HLA-DR, HLA-DP and HLA-DQ, as well as mouse molecules (H-2).
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 in IC50 values (in nanoMolars) and as %Rank.
The percentile rank for a peptide is generated by comparing its score against the scores of 200,000 random natural peptides of the same length of the query peptide. 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.
The project is a collaboration between CBS, IMMI at copenhagen university and LIAI.
NEW: visualize sequence motifs of the molecules in the NetMHCIIpan library with the Motif viewer.
Note, if you download the stand alone version of the tool, please access the needed data.tar.gz file from data.Linux.tar.gz (Linux) or data.Darwin.tar.gz (MAC)
For publication of results, please cite:
Benchmark data used to develop this server were obtained from:
Any other symbol will be converted to X before processing.
The server allows for input in either FASTA or PEPTIDE format.
The sequences can be input in the following two ways:
There is a limit of 5000 sequences per submission, no longer than 20000 amino acids.
1. Specify peptide length (only for FASTA input). Buy default the server uses 15-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). Note: if you have chosen DP or DQ loci, you have to choose alpha and beta chains separately from the scroll-down menu. If you choose to type in the allele names, you can consult the List of MHC molecule names..
4. You can also paste a single full length MHC protein sequence in FASTA format or submit a file containing a full length MHC protein sequence in FASTA format directly from your local disk. For the DR molecules paste or submit only a sequence of the beta chain. For all other loci, paste or submit alpha and beta chain sequences separately.
5. Optionally specify thresholds for strong and weak binders, expressed in %Rank. The %Rank defines how the predicted affinity for a given peptide ranks compared to a set of 200,000 random natural peptides of the same length. For example, if a 15mer peptide is assigned a rank of 1%, it means that one can expect 2000 out of 200,000 random 15mers to have equal or higher affinity.
6. For large submissions, you may want to filter the results on % Rank and IC50 values. Only predictions for peptides with % Rank OR binding affinity (IC50) below the specified thresholds will be shown in the results page.
7. Optionally run the program in fast mode (recommended for large data sets). It uses a reduced ensemble of only 10 neural networks.
8. Tick the box Print only the strongest binding core to display the results only for the strongest binding core in overlapping consecutive peptides (Fasta submissions).
9. Tick the box Sort by affinity to have the output sorted by descending predicted binding affinity.
10. The server can produce a graphical representation of the peptide binding core registers. All possible binding registers are plotted with the fraction of networks in the ensemble selecting each register. The graphics can be made only for a maximum of 20 peptides (use together with the sorting option to display the graphics for the strongest predicted binders).
11. Tick the box Save prediction to xls file if you want the output to be exported in xls format.
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.
The prediction output for each molecule consists of the following columns:
If the "Graphical representation of the binding registers" option was selected, a list of figure links will be displayed. For the top 20 peptides, these graphs show the number of networks in the ensemble that agreed on each binding register (the Core_Rel measure for all registers). If the histogram shows alternative binding registers with comparable reliability measure (= similar height of the bars) for a predicted binder, that may suggest the presence of competitive binding cores within the peptide.
# NetMHCIIpan version 3.1
# Input is in PEPTIDE format
# Threshold for Strong binding peptides (IC50) 50.000 nM
# Threshold for Weak binding peptides (IC50) 500.000 nM
# Threshold for Strong binding peptides (%Rank) 0.5%
# Threshold for Weak binding peptides (%Rank) 2%
# Allele: DRB1_0301
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Seq Allele Peptide Identity Pos Core Core_Rel 1-log50k(aff) Affinity(nM) %Rank Exp_Bind BindingLevel
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0 DRB1_0301 AGFKGEQGPKGEPG Sequence 2 FKGEQGPKG 0.810 0.080 21036.68 50.00 9.999
1 DRB1_0301 GELIGTLNAAKVPAD Sequence 2 LIGTLNAAK 0.650 0.340 1268.50 32.00 9.999
2 DRB1_0301 PEVIPMFSALSEGATP Sequence 5 MFSALSEGA 0.385 0.180 7161.16 50.00 9.999
3 DRB1_0301 PKYVKQNTLKLAT Sequence 2 YVKQNTLKL 0.575 0.442 418.70 6.00 9.999 <=WB
4 DRB1_0301 VGSDWRFLRGYHQYA Sequence 0 VGSDWRFLR 0.575 0.466 322.07 10.00 9.999 <=WB
5 DRB1_0301 XFVKQNAAALX Sequence 2 VKQNAAALX 0.500 0.262 2939.20 15.00 9.999
6 DRB1_0301 AAYSDQATPLLLSPR Sequence 1 AYSDQATPL 0.395 0.291 2152.21 50.00 9.999
7 DRB1_0301 PVSKMRMATPLLMQA Sequence 4 MRMATPLLM 0.890 0.770 12.00 0.01 9.999 <=SB
8 DRB1_0301 AYMRADAAAGGA Sequence 2 MRADAAAGG 0.835 0.303 1887.87 15.00 9.999
9 DRB1_0301 PKYVKQNTLKLAT Sequence 2 YVKQNTLKL 0.575 0.442 418.70 6.00 9.999 <=WB
10 DRB1_0301 ENPVVHFFKNIVTPR Sequence 6 FFKNIVTPR 0.425 0.357 1049.04 32.00 9.999
11 DRB1_0301 GGVYHFVKKHVHES Sequence 2 VYHFVKKHV 0.450 0.354 1084.85 32.00 9.999
12 DRB1_0301 NPVVHFFKNIVTPRTPPPSQ Sequence 5 FFKNIVTPR 0.575 0.415 562.38 50.00 9.999
13 DRB1_0301 VHFFKNIVTPRTPGG Sequence 2 FFKNIVTPR 0.685 0.347 1166.24 32.00 9.999
14 DRB1_0301 MPLAQMLLPTAMRMKM Sequence 5 MLLPTAMRM 0.465 0.479 279.91 15.00 9.999 <=WB
15 DRB1_0301 KMRMATPLLMQALPM Sequence 1 MRMATPLLM 0.910 0.712 22.67 0.10 9.999 <=SB
16 DRB1_0301 KPVSKMRMATPLLMQALPM Sequence 5 MRMATPLLM 0.875 0.792 9.49 0.03 9.999 <=SB
17 DRB1_0301 XPKWVKQNTLKLAT Sequence 4 VKQNTLKLA 0.475 0.447 397.31 8.00 9.999 <=WB
18 DRB1_0301 PVSKMRMATPLLMQA Sequence 4 MRMATPLLM 0.890 0.770 12.00 0.01 9.999 <=SB
19 DRB1_0301 GSDARFLRGYHLYA Sequence 3 ARFLRGYHL 0.380 0.325 1485.69 32.00 9.999
20 DRB1_0301 APPAYEKLSAEQSPP Sequence 4 YEKLSAEQS 0.345 0.101 16682.44 50.00 9.999
21 DRB1_0301 VVKQNCLKLATK Sequence 1 VKQNCLKLA 0.665 0.275 2539.02 32.00 9.999
22 DRB1_0301 PEVIPMFSALSEG Sequence 2 VIPMFSALS 0.475 0.143 10603.35 50.00 9.999
23 DRB1_0301 WNRQLYPEWTEAQRLD Sequence 4 LYPEWTEAQ 0.545 0.235 3925.16 50.00 9.999
24 DRB1_0301 SAVRLRSSVPGVR Sequence 4 LRSSVPGVR 0.790 0.405 624.44 9.00 9.999
25 DRB1_0301 GVYATRSSAVRLR Sequence 1 VYATRSSAV 0.285 0.376 859.30 15.00 9.999
26 DRB1_0301 ATEYRVRVNSAYQDK Sequence 5 VRVNSAYQD 0.575 0.312 1708.18 50.00 9.999
27 DRB1_0301 SAVRLRSSVPGVR Sequence 4 LRSSVPGVR 0.790 0.405 624.44 9.00 9.999
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Number of strong binders: 4 Number of weak binders: 5
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Major histocompatibility complex class II (MHC-II) molecules are expressed on the surface of professional antigen-presenting cells where they display peptides to T helper cells, which orchestrate the onset and outcome of many host immune responses. Understanding which peptides will be presented by the MHC-II molecule is therefore important for understanding the activation of T helper cells and can be used to identify T-cell epitopes. We here present updated versions of two MHC-II-peptide binding affinity prediction methods, NetMHCII and NetMHCIIpan. These were constructed using an extended data set of quantitative MHC-peptide binding affinity data obtained from the Immune Epitope Database covering HLA-DR, HLA-DQ, HLA-DP and H-2 mouse molecules. We show that training with this extended data set improved the performance for peptide binding predictions for both methods. Both methods are publicly available at www.cbs.dtu.dk/services/NetMHCII-2.3 and www.cbs.dtu.dk/services/NetMHCIIpan-3.2.
PMID: 29315598
Here, you will find the data set used for training and testing, as well as the T cell epitope data used for evaluation of the NetMHCIIpan-3.2 method.
The training binding data are partitioned in 5 files to be used for cross-validation. For instance does the train1 file contain training data, and test1 file test data for the first cross-validation partitioning. It is critical that this data partitioning is maintained.
The format for each of the files is
AAAGAEAGKATTEEQ 0.190842 DRB1_0101 AAAGAEAGKATTEEQ 0.006301 DRB1_0301 AAAGAEAGKATTEEQ 0.066851 DRB1_0401 AAAGAEAGKATTEEQ 0.006344 DRB1_0405 AAAGAEAGKATTEEQ 0.035130 DRB1_0701 AAAGAEAGKATTEEQ 0.006288 DRB1_0802 AAAGAEAGKATTEEQ 0.176268 DRB1_0901 AAAGAEAGKATTEEQ 0.042555 DRB1_1101 AAAGAEAGKATTEEQ 0.114855 DRB1_1302 AAAGAEAGKATTEEQ 0.006377 DRB1_1501
where the first column gives the peptide, the second column the log50k transformed binding affinity (i.e. 1 - log50k( aff nM)), and the last column the class II allele.
When classifying the peptides into binders and non-binders for calculation of the AUC values for instance, a threshold of 500 nM is used. This means that peptides with log50k transformed binding affinity values greater than 0.426 are classified as binders.
train1 (Train data) test1 (Test data)
train2 (Train data) test2 (Test data)
train3 (Train data) test3 (Test data)
train4 (Train data) test4 (Test data)
train5 (Train data) test5 (Test data)
The format is
>0705172A=AAHAEINEA=H2-IAb 385 gi|223299|prf||0705172A GSIGAASMEFCFDVFKELKVHHANENIFYCPIAIMSALAMVYLGAKDSTRTQINKVVRFD KLPGFGDSIEAQCGTSVNVHSSLRDILNQITKPNDVYSFSLASRLYAEERYPILPEYLQC VKELYRGGLEPINFQTAADQARELINSWVESQTNGIIRNVLQPSSVDSQTAMVLVNAIVF KGLWEKAFKDEDTQAMPFRVTEQESKPVQMMYQIGLFRVASMASEKMKILELPFASGTMS MLVLLPDEVSGLEQLESIINFEKLTEWTSSNVMEERKIKVYLPRMKMEEKYNLTSVLMAM GITDVFSSSANLSGISSAESLKISQAVHAAHAEINEAGREVVGSAEAGVDAASVSEEFRA DHPFLFCIKHIATNAVLFFGRCVSP
where the first part of the fasta header contains the proteinID (0705172A), the epitope (AAHAEINEA), and the MHC restriction (H2-IAb)
Please click on the version number to activate the corresponding server.
| 3.2 |
The current server (online since January 2018). New in this version:
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| 3.1 |
(online since December 2014). New in this version:
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| 3.0 |
(online since June 2013). New in this version:
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| 2.1 |
(online since 6 June 2011). New in this version:
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| 2.0 |
(online since 17 Nov 2010). New in this version:
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| 1.1 |
(online since 15 April 2010). New in this version:
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| 1.0 |
Original version (online version until April 15 2010):
Main publication:
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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). 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: