DTU Health Tech
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If you need help with the bioinformatics programs, see the "Getting Help" section below the program.
The NetMHCIIphosPan-1.0 server predicts binding of phosphorylated peptides to HLA class II molecules using Artificial Neural Networks (ANNs). It is trained on an dataset of over 19,000 measurements of mass-spectrometry eluted ligands (EL), covering the three human MHC class II isotypes: HLA-DR, HLA-DP and HLA-DQ.
The server allows predictions for any HLA class II molecule with a known sequence, which users can input in FASTA format. Predictions can be made for phosphorylated peptides of any length.
The output of the method is a prediction score for the likelihood of a phosphorylated peptide to be naturally presented by an HLA-II receptor of choice. The output also includes a %rank score, which normalizes the prediction score by comparing to predictions of a set of random phosphopeptides. Optionally, the model also outputs binding affinity (BA) prediction and %rank scores.
It is important to note that this model was also trained on over 120,000 unmodified peptides measured in binding affinity (BA) experiments and over 145,000 unmodified MS-eluted ligands (EL) from the NetMHCIIpan-4.3 training dataset.
All the same, this method is specifically tailored to make binding predictions for phosphorylated peptides (i.e. peptides with one or more phosphorylated serine, threonine, or tyrosine). To make predictions for unmodified peptides, please refer to the NetMHCIIpan-4.3 method.
The project is a collaboration between DTU-Bioinformatics, and LIAI.
For publication of results, please cite:
NetMHCIIphosPan-1.0 is available as a stand-alone software package, with the same functionality as the service above. Ready-to-ship packages exist for Linux and macOS. There is a download page for academic users; other users are requested to contact Health Tech Software Package Manager at health-software@dtu.dk.
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 pasting it into the blank field, uploading it using the "Choose File" button, or by loading sample data using the "Load Data" button. All input sequences must be in one-letter amino acid code. The alphabet is as follows (case sensitive):
s t y A C D E F G H I K L M N P Q R S T V W Y and X (unknown)
Please note that "s" represents phosphoserine, "t" represents phosphothreonine and "y" phosphotyrosine.
Any other symbol will be converted to X before processing. A maximum of 5000 sequences are allowed per submission; each sequence must be between 9 and 20,000 amino acids long.
3) If FASTA was selected, the user must select the peptide length(s) for the prediction server. NetMHCIIphosPan-1.0 will "chop" the FASTA sequence into overlapping peptides of the selected length and predict binding for each. By default, input proteins are digested into 15-mer peptides. If PEPTIDE was selected, this step is unnecessary, and the peptide length selector will not appear.
Note that context encoding is NOT available for this method.
In this section, the user must define which MHC molecule(s) to predict against:
1) Select MHC molecules from a list by selecting a group and choosing MHCs. MS-COVERED refers to molecules covered by the NetMHCIIphosPan-1.0 training data.
2) Alternatively, the user can type the molecule names. Both ALPHA and BETA chains must be typed (see List of MHC molecule names). Selections from step 1 populate this bar.
3) If the desired molecule is not in the list, the user can input ALPHA and BETA sequences in FASTA format. Rank score predictions are not available in this case.
In this section, additional parameters can be defined to customize the run:
1) Specify thresholds for strong and weak binders (%Rank). Peptides identified in the top x% are strong binders (default: 1%). Peptides between strong and weak thresholds are weak binders (default: 5%).
2) Include Binding Affinity predictions alongside Eluted Ligand likelihood.
3) Enable peptide inversion prediction for all selected MHC-II molecules (optional; default is HLA-DP only).
4) Output only peptides below a specified %Rank score (useful for large submissions).
5) Output only the strongest binding core.
6) Sort output by descending prediction score.
7) Export output to .XLS format.
After completing the "INPUT DATA", "MHC SELECTION", and "ADDITIONAL CONFIGURATION" steps, the submission can now be done. Click "Submit" to send the job to the server, or click "Clear fields" to reset the form.
Job status ('queued' or 'running') will be displayed and updated until it terminates, and the output will appear in the browser window.
After completion, an output page will be delivered. A description of the output format can be found here.
You can enter your email address at any time to receive notification when the job is complete.
# NetMHCIIphosPan version 1.0a # Input is in FASTA format # Peptide length 15 # Prediction Mode: EL # Threshold for Strong binding peptides (%Rank) 1.00% # Threshold for Weak binding peptides (%Rank) 5.00% # DRB1_0101 : Distance to training data 0.000 (using nearest neighbor DRB1_0101) # Allele: DRB1_0101 -------------------------------------------------------------------------------------------------------------------------------------------- Pos MHC Peptide Of Core Core_Rel Inverted Identity Score_EL %Rank_EL Exp_Bind BindLevel -------------------------------------------------------------------------------------------------------------------------------------------- 101 DRB1_0101 PPAyEKLsAEQSPPP 3 yEKLsAEQS 1.000 0 sp_Q16655_MAR1_ 0.866951 0.09 NA <= SB 85 DRB1_0101 KVsLQEKNCEPVVPN 3 LQEKNCEPV 0.900 0 sp_Q16655_MAR1_ 0.034566 14.72 NA 82 DRB1_0101 RDSKVsLQEKNCEPV 3 KVsLQEKNC 0.720 0 sp_Q16655_MAR1_ 0.020288 19.72 NA 76 DRB1_0101 QEGFDHRDSKVsLQE 3 FDHRDSKVs 0.960 0 sp_Q16655_MAR1_ 0.019929 19.89 NA 93 DRB1_0101 CEPVVPNAPPAyEKL 3 VVPNAPPAy 0.930 0 sp_Q16655_MAR1_ 0.011383 25.98 NA 83 DRB1_0101 DSKVsLQEKNCEPVV 3 VsLQEKNCE 0.100 0 sp_Q16655_MAR1_ 0.006428 32.83 NA 90 DRB1_0101 EKNCEPVVPNAPPAy 3 CEPVVPNAP 0.690 0 sp_Q16655_MAR1_ 0.001225 54.80 NA 97 DRB1_0101 VPNAPPAyEKLsAEQ 3 APPAyEKLs 0.310 0 sp_Q16655_MAR1_ 0.000078 87.60 NA -------------------------------------------------------------------------------------------------------------------------------------------- Number of strong binders: 1 Number of weak binders: 0 --------------------------------------------------------------------------------------------------------------------------------------------
The prediction output for each molecule consists of the following columns:
Here, you will find the datasets used for the training of NetMHCIIphosPan-1.0.
Download the file and untar the content using:
cat NetMHCIIphosPan_train.tar.gz | tar xvf -
This will create the directory called NetMHCIIphosPan_train. In this directory you will find 12 files.
LTGIKHELQANCyEEVKDR 1 Racle__3869_GA 1 LtGMAFRVPTANVSVVD 1 Racle__TIL3 1 LtHCQDINECLTLG 1 Saghar_9090_DQ 1 LTKIHPKAFLtTKK 1 Racle__3830NJF 1 LtLHKPTQVMPCRAPKVG 1 Racle__TIL3 1 ... VKKFPRFRNREL 0 PvanBalen_DP_AZP_2877 0 GMRLKEAGNINR 0 PvanBalen_DP_AZP_2877 0where the different columns are peptide (1st), target value (2nd), MHC-molecule/cell-line (3rd) and peptide type (unmodified=0, phosphorylated=1) (4th).
Download and untar the file as indicated above.
In the directory called NetMHCIIphosPan_ext_benchmark you will find 3 files.
Please click on the version number to activate the corresponding server.
1.0 |
The current version (online since January 2025). 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) and the options you have selected. 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: