DTU Health Tech

Department of Health Technology

NetMHCstabpan - 1.0

Pan-specific stability of peptide:MHC-I complexes


NetMHCstabpan server predicts binding stability of peptides to any known MHC molecule using artificial neural networks (ANNs). The method is trained on more than 25,000 quantitative stability data covering 75 different HLA molecules. The user can upload full length MHC protein sequences, and have the server predict MHC restricted peptides from any given protein of interest.

Predictions can be made for 8-14 mer peptides. Note, that all non 9mer predictions are made using approximations. Most HLA molecules have a strong preference for binding 9mers.

The prediction values are given in half life time in hours values and as %-Rank to a set of 200.000 random natural peptides.

The project is a collaboration between CBS and IMMI at Copenhagen university

Link to table (tab seperated) describing the training data Training data table

If you download the stand alone version of the tool, please access the needed data.tar.gz file from data.tar.gz

Submission


Type of input

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

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


Peptide length (several lengths are possible): 

Select species/loci

Select Allele (max 20 per submission) or type allele names (ie HLA-A01:01) 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:


Threshold for strong binder: % Rank 
Threshold for weak binder: % Rank 

Include affinity predictions Sort by score  

Save prediction to xls file 

Restrictions:
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.

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


CITATIONS

For publication of results, please cite:

  • Pan-specific prediction of peptide-MHC-I complex stability; a correlate of T cell immunogenicity
    Michael Rasmussen, Emilio Fenoy, Mikkel Harndahl, Anne Bregnballe Kristensen, Ida Kallehauge Nielsen, Morten Nielsen, Soren Buus. Accepted for publication Journal of Immunology, June 2016

DATA RESOURCES

Data resources used to develop this server was obtained from

  • IEDB database.
    • Quantitative peptide binding data were obtained from the IEDB database.
  • IMGT/HLA database. Robinson J, Malik A, Parham P, Bodmer JG, Marsh SGE: IMGT/HLA - a sequence database for the human major histocompatibility complex. Tissue Antigens (2000), 55:280-287.
    • HLA protein sequences were obtained from the IMGT/HLA database (version 3.1.0).

Instructions



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).
If the allele that you are looking for is not in the list, a full length MHC protein sequence can be submitted.

Give threshold value for binding values to be displayed.

Select if you want to include affinity predictions (Calculated with netMHCpan-2.8), and if this is the case, its contribution to the combined score.

Select one option from Sort by score to have the output sorted by descending order.

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 bottom 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.



Format of NetMHCstabpan-1.0 output



DESCRIPTION

The prediction output consists of 13 columns.

  • Residue number
  • HLA Allele
  • Peptide sequence
  • Protein identifier
  • Stability prediction score
  • Predicted half life of the pMHC complex (in hours)
  • %Random - %Rank of predicted stability score to a set of 200.000 random natural 9mer peptides
  • Affinity Prediction score (called 1-log50K(aff))
  • Affinity as IC50 value in nM (only for white-listed alleles)
  • %Random - %Rank of predicted affinity score to a set of 200.000 random natural 9mer peptides
  • Prediction score combining Affinity and Stability predictions
  • %Rank aproximation using both, stability and affinity %Rank
  • Binding level (SB: strong binder, WB: weak binder)

    If the "Include affinity predictions" option is set to "No", the Affinity related columns are not included into the output



  • OUTPUT EXAMPLES

    
    
    Excluding Affinity predictions
    
    # NetMHCstabpan version 1.0
    
    # Input is in PEPTIDE format
    
    HLA-A02:01 : Distance to traning data  0.000 (using nearest neighbor HLA-A02:01)
    
    # Rank Threshold for Strong binding peptides   0.500
    # Rank Threshold for Weak binding peptides   2.000
    -----------------------------------------------------------------------------------------------------
     pos      HLA         peptide         Identity       Pred     Thalf(h) %Rank_Stab BindLevel
    -----------------------------------------------------------------------------------------------------
        0  HLA-A*02:01    YIFLVSLLV         PEPLIST      0.820       3.50       1.00      <= WB
        0  HLA-A*02:01    ILYDVSIPP         PEPLIST      0.302       0.58       8.00
        0  HLA-A*02:01    YVSSILYDV         PEPLIST      0.922       8.55       0.25      <= SB
        0  HLA-A*02:01    FLKNSILNQ         PEPLIST      0.017       0.17      34.00
    -----------------------------------------------------------------------------------------------------
    
    Protein PEPLIST. Allele HLA-A*02:01. Number of high binders 1. Number of weak binders 1. Number of peptides 4
    
    
    Including Affinity predictions # NetMHCstabpan version 1.0 # Binding affinity prediction included ( using /usr/cbs/bio/src/netMHCpan-2.8/netMHCpan ) with relative weight 0.800000 # Input is in PEPTIDE format HLA-A02:01 : Distance to traning data 0.000 (using nearest neighbor HLA-A02:01) # Rank Threshold for Strong binding peptides 0.500 # Rank Threshold for Weak binding peptides 2.000 ----------------------------------------------------------------------------------------------------- pos HLA peptide Identity Pred Thalf(h) %Rank_Stab 1-log50k Aff(nM) %Rank_aff Combined Combined_%rank BindLevel ----------------------------------------------------------------------------------------------------- 0 HLA-A*02:01 YIFLVSLLV PEPLIST 0.820 3.50 1.00 0.682 31.29 1.50 0.710 1.36 <= WB 0 HLA-A*02:01 ILYDVSIPP PEPLIST 0.302 0.58 8.00 0.510 200.19 3.00 0.468 3.43 0 HLA-A*02:01 YVSSILYDV PEPLIST 0.922 8.55 0.25 0.775 11.44 0.50 0.804 0.42 <= SB 0 HLA-A*02:01 FLKNSILNQ PEPLIST 0.017 0.17 34.00 0.166 8299.63 15.00 0.136 16.89 ----------------------------------------------------------------------------------------------------- Protein PEPLIST. Allele HLA-A*02:01. Number of high binders 1. Number of weak binders 1. Number of peptides 4
    
    


    References


    Main reference:

    Pan-specific prediction of peptide-MHC-I complex stability; a correlate of T cell immunogenicity
    M Rasmussen2, E Fenoy3
    M Nielsen1,3, Buus S2, Accepted JI June, 2016

    1Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
    2Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark
    3

    Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of antigens to cytotoxic T lymphocytes (CTL) and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. Peptide-MHC-I interactions have traditionally been quantified by the strength of the interaction, i.e. the binding affinity, yet it has been show that the stability of the peptide-MHC-I complex is a better correlate of immunogenicity compared to binding affinity. Here, we have experimentally analyzed peptide-MHC-I complex stability of a large panel of human MHC-I allotypes and generated a body of data sufficient to develop neural networks based pan-specific predictor of peptide-MHC-I complex stability. Integrating the neural networks predictors of peptide-MHC-I complex stability with state-of-the-art predictors of peptide-MHC-I binding is shown to significantly improve the prediction of CTL epitopes. The method is publicly available at www.cbs.dtu.dk/services/NetMHCstabpan.

    Software Downloads




    GETTING HELP

    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: