NetMHCIIpan 4.0 Server
CITATIONS
For publication of results, please cite:
-
Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data.
Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M
J Proteome Res 2020 Apr 30. doi: 10.1021/acs.jproteome.9b00874.
PubMed: 32308001
DATA RESOURCES
Benchmark data used to develop this server were obtained from:
PORTABLE VERSION
NetMHCIIpan 4.0 is available
as a stand-alone software package, with the same functionality as the service
above. Ready-to-ship packages exist for Linux and MacOSX. There is the tap "download"
for academic users; other users are requested to contact CBS Software Package
Manager at health-software@dtu.dk.
INSTRUCTIONS
INPUT DATA
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 ).
MHC SELECTION
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.
ADDITIONAL CONFIGURATION
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).
SUBMISSION
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 at outpur 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.
Output format
EXAMPLE OUTPUT
For the following FASTA input example:
>P9WNK5
MAEMKTDAATLAQEAGNFERISGDLKTQIDQVESTAGSLQGQWRGAAGTAAQAAVVRFQEAANKQKQELDEISTNIRQAGVQYSRADEEQQQALSSQMGF
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
DESCRIPTION
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
Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data.
Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M
J Proteome Res 2020 Apr 30. doi: 10.1021/acs.jproteome.9b00874.
PubMed:
32308001
Major Histocompatibility Complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenousantigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Further, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0, demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes and development of personalized immunotherapies.
Supplementary material
Here, you will find the data set used for evaluation of NetMHCpan-4.1 and NetMHCIIpan-4.0 methods.
NetMHCpan-4.1
CD8 Epitope data set
CD8_epitopes.fsa
MS Ligands
HLA-A02:02
HLA-A02:05
HLA-A02:06
HLA-A02:11
HLA-A11:01
HLA-A23:01
HLA-A25:01
HLA-A26:01
HLA-A30:01
HLA-A30:02
HLA-A32:01
HLA-A33:01
HLA-A66:01
HLA-A68:01
HLA-B07:02
HLA-B08:01
HLA-B14:02
HLA-B15:01
HLA-B15:02
HLA-B15:03
HLA-B15:17
HLA-B18:01
HLA-B35:03
HLA-B37:01
HLA-B38:01
HLA-B40:01
HLA-B40:02
HLA-B45:01
HLA-B46:01
HLA-B53:01
HLA-B58:01
HLA-C03:03
HLA-C05:01
HLA-C07:02
HLA-C08:02
HLA-C12:03
NetMHCIIpan-4.0
CD4_epitopes.fsa
References
NetMHCpan-4.1 and NetMHCIIpan-4.0: Improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data
Submitted 2020.
Version history
Please click on the version number to activate the corresponding server.
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4.0
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The current server (online since April 2020). New in this version:
- The two output neuron architechture introduced in NetMHCpan-4.0 permits the inclusion of EL data, and the new training algorithm NNAlign_MA extends training data to ligands of ambiguous allele assignments. The model also, optionally, encodes ligand context.
Main publication:
-
Improved methods for predicting peptide binding affinity to MHC class II molecules.
Reynisson B, Barra C, Kaabinejadian S, Hildebrand WH, Peters B, Nielsen M
J Proteome Res 2020 Apr 30. doi: 10.1021/acs.jproteome.9b00874.
PubMed: 32308001
|
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3.2
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(online since January 2018). New in this version:
- Method retrained on an extensive dataset of over 100,000 datapoints, covering 36 HLA-DR, 27 HLA-DQ, 9 HLA-DP, and 8 mouse MHC-II molecules.
Main publication:
-
Improved methods for predicting peptide binding affinity to MHC class II molecules.
Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B, Nielsen M.
Immunology. 2018 Jan 6. doi: 10.1111/imm.12889.
PubMed: 29315598
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3.1
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(online since December 2014). New in this version:
- Improved binding core identification by realigning individual networks in the ensemble.
- Introduced a reliability measure on the predicted binding core (Core_Rel column).
- Graphical representation of the binding core register and of possible multiple cores.
Main publication:
-
Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification
Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, and Nielsen M
Immunogenetics (2015)
PubMed: 26416257
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3.0
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(online since June 2013). New in this version:
- The user can make predictions for all DR, DP and DQ molecules with known protein
sequence. Likewise can the user upload full length MHC class II alpha and beta chain and have the server predict MHC restricted peptides from any given protein of interest
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2.1
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(online since 6 June 2011). New in this version:
- User can upload full length MHC class II beta chain and have the server predict MHC restricted peptides from any given protein of interest.
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2.0
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(online since 17 Nov 2010). New in this version:
- New concurent algorithm used to train the network.
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1.1
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(online since 15 April 2010). New in this version:
- %-rank measure include for each prediction value. The %-rank score give the rank of the
prediction score to a distribution of prediction scores from 200.000 natural random 15mer peptides.
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1.0
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Original version (online version until April 15 2010):
Main publication:
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Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan.
Nielsen M, et al. (2008) PLoS Comput Biol. Jul 4;4(7):e1000107.
View the full text article at PLoS Compu:
Full text.
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