DTU Health Tech
Department of Health Technology
DTU Health Tech
Department of Health Technology
All previous versions are available online, for comparison and reference.
The NetH2pan server is a version of NetMHCpan dedicated to mouse MHC molecules.
The method was trained on binding affinity and ligand elution data from nine different H-2 class I molecules.
By default it predicts LIGAND likelihood scores, but it can be toggled to return predicted BINDING AFFINITY scores.
For publication of results, please cite:
Data resources used to develop this server was obtained from
Any other symbol will be converted to X before processing.
The server allows for input in either FASTA or PEPTIDE format.
Sequences can be submitted in the following two formats:
At most 5000 sequences per submission; each sequence not more than 20,000 amino acids and not less than 8 amino acids.
1. Specify peptide length (only for FASTA input). By default input proteins are digested into 9-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). If you choose to type in the allele names, you can consult the List of MHC molecule names.; use the molecule names in the first column.
4. Optionally 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 0.5%). 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 2%).
5. Tick the box Make BA predictions to predict binding affinity scores. By default, the method returns scores of eluted ligand likelihood.
6. Tick the box Sort by affinity to have the output sorted by descending predicted binding affinity.
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:
>sp|P06437|GB_HHV1K Envelope glycoprotein B OS=Human herpesvirus 1 (strain KOS) GN=gB PE=1 SV=2 MHQGAPSWGRRWFVVWALLGLTLGVLVASAAPTSPGTPGVAAATQAANGGPATPAPPPLG AAPTGDPKPKKNKKPKNPTPPRPAGDNATVAAGHATLREHLRDIKAENTDANFYVCPPPT GATVVQFEQPRRCPTRPEGQNYTEGIAVVFKENIAPYKFKATMYYKDVTVSQVWFGHRYS QFMGIFEDRAPVPFEEVIDKINAKGVCRSTAKYVRNNLETTAFHRDDHETDMELKPANAA TRTSRGWHTTDLKYNPSRVEAFHRYGTTVNCIVEEVDARSVYPYDEFVLATGDFVYMSPF YGYREGSHTEHTTYAADRFKQVDGFYARDLTTKARATAPTTRNLLTTPKFTVAWDWVPKR PSVCTMTKWQEVDEMLRSEYGGSFRFSSDAISTTFTTNLTEYPLSRVDLGDCIGKDARDA MDRIFARRYNATHIKVGQPQYYQANGGFLIAYQPLLSNTLAELYVREHLREQSRKPPNPT PPPPGASANASVERIKTTSSIEFARLQFTYNHIQRHVNDMLGRVAIAWCELQNHELTLWN EARKLNPNAIASVTVGRRVSARMLGDVMAVSTCVPVAADNVIVQNSMRISSRPGACYSRP LVSFRYEDQGPLVEGQLGENNELRLTRDAIEPCTVGHRRYFTFGGGYVYFEEYAYSHQLS RADITTVSTFIDLNITMLEDHEFVPLEVYTRHEIKDSGLLDYTEVQRRNQLHDLRFADID TVIHADANAAMFAGLGAFFEGMGDLGRAVGKVVMGIVGGVVSAVSGVSSFMSNPFGALAV GLLVLAGLAAAFFAFRYVMRLQSNPMKALYPLTTKELKNPTNPDASGEGEEGGDFDEAKL AEAREMIRYMALVSAMERTEHKAKKKGTSALLSAKVTDMVMRKRRNTNYTQVPNKDGDAD EDDL
will return the following predictions:
# NetMHCpan version 4.0_H2
# Tmpdir made /usr/opt/www/webface/tmp/server/netmhcpan/593AB384000035D69F4553E2/netMHCpane1KUiY
# Input is in FSA format
# Peptide length 8,9,10,11
# Make Eluted ligand likelihood predictions
H-2-Kb : Distance to training data 0.000 (using nearest neighbor H2-Kb)
# Rank Threshold for Strong binding peptides 0.500
# Rank Threshold for Weak binding peptides 2.000
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Pos HLA Peptide Core Of Gp Gl Ip Il Icore Identity Score %Rank BindLevel
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499 H2-Kb SSIEFARL SSIEF-ARL 0 0 0 5 1 SSIEFARL sp_P06437_GB_HH 0.9981590 0.0005 <= SB
391 H2-Kb ISTTFTTNL ISTTFTTNL 0 0 0 0 0 ISTTFTTNL sp_P06437_GB_HH 0.9163720 0.0420 <= SB
280 H2-Kb SVYPYDEF SVYP-YDEF 0 0 0 4 1 SVYPYDEF sp_P06437_GB_HH 0.9095360 0.0447 <= SB
793 H2-Kb FAFRYVMRL FAFRYVMRL 0 0 0 0 0 FAFRYVMRL sp_P06437_GB_HH 0.8978500 0.0493 <= SB
506 H2-Kb LQFTYNHI LQFT-YNHI 0 0 0 4 1 LQFTYNHI sp_P06437_GB_HH 0.8668980 0.0756 <= SB
154 H2-Kb IAPYKFKATM IAYKFKATM 0 2 1 0 0 IAPYKFKATM sp_P06437_GB_HH 0.8105570 0.1299 <= SB
727 H2-Kb ANAAMFAGL ANAAMFAGL 0 0 0 0 0 ANAAMFAGL sp_P06437_GB_HH 0.8054410 0.1355 <= SB
146 H2-Kb IAVVFKENI IAVVFKENI 0 0 0 0 0 IAVVFKENI sp_P06437_GB_HH 0.8039780 0.1371 <= SB
728 H2-Kb NAAMFAGL NAAM-FAGL 0 0 0 4 1 NAAMFAGL sp_P06437_GB_HH 0.7846630 0.1562 <= SB
155 H2-Kb APYKFKATM APYKFKATM 0 0 0 0 0 APYKFKATM sp_P06437_GB_HH 0.7813300 0.1590 <= SB
392 H2-Kb STTFTTNL ST-TFTTNL 0 0 0 2 1 STTFTTNL sp_P06437_GB_HH 0.7616710 0.1751 <= SB
845 H2-Kb EMIRYMAL EMIR-YMAL 0 0 0 4 1 EMIRYMAL sp_P06437_GB_HH 0.7440680 0.1871 <= SB
794 H2-Kb AFRYVMRL A-FRYVMRL 0 0 0 1 1 AFRYVMRL sp_P06437_GB_HH 0.7372430 0.1918 <= SB..
..
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Protein sp_P06437_GB_HH. Allele H2-Kb. Number of high binders 26. Number of weak binders 52. Number of peptides 3582
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MAIN REFERENCE
NetH2pan: A Computational Tool to Guide MHC peptide prediction on Murine Tumors
Christa I. DeVette 1, Massimo Andreatta 2, Wilfried Bardet 1, Steven J. Cate 1, Vanessa I. Jurtz 3, Kenneth W. Jackson 1, Alana L. Welm 4, Morten Nielsen 2,3, and William H. Hildebrand1
Cancer Immunology Research (2018)
1
University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA.
2
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina.
3
Department of Bio and Health Informatics, Technical University of Denmark, Kgs. Lyngby, Denmark.
4
Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.
With the advancement of personalized cancer immunotherapies, new tools are needed for identifying tumor antigens and evaluating T cell responses in model systems, specifically those that exhibit clinically relevant tumor progression. Key transgenic mouse models of breast cancer are generated and maintained on the FVB genetic background, and one such model is the MMTV-PyMT mouse – an immunocompetent transgenic mouse that exhibits spontaneous mammary tumor development and metastasis with high penetrance. Backcrossing the MMTV-PyMT mouse from the FVB strain onto a B6 genetic background, in order to leverage well-developed B6 immunological tools, results in delayed tumor development and variable metastatic phenotypes. Therefore, we initiated characterization of the FVB MHC Class I H-2-q haplotype to establish useful immunological tools for evaluating antigen specificity in the murine FVB strain. Our study provides the first detailed molecular and immunoproteomic characterization of the FVB H-2-q MHC Class I alleles, including >8500 unique peptide ligands, a multi-allele murine MHC peptide prediction tool, and in vivo validation of these data using MMTV-PyMT primary tumors. This work allows researchers to rapidly predict H-2 peptide ligands for immune testing, including, but not limited to, the MMTV-PyMT model for metastatic breast cancer.
PMID: 29615400
EARLIER REFERENCES
NetMHC pan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data
Vanessa Jurtz 1, Sinu Paul 2, Massimo Andreatta 3, Paolo Marcatili 1, Bjoern Peters 2, and Morten Nielsen1,3
The Journal of Immunology (2017)
1
Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Lyngby, Denmark
2
Division of Vaccine Discovery, La Jolla Institute for Allergy and Immunology, CA92037 La Jolla, USA
3
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina
Cytotoxic T cells are of central importance in the immune system's response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC (major histocompatibility complex) class I molecules. Peptide binding to MHC molecules is the single most selective step in the antigen presentation pathway. On the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has therefore attracted large attention. In the past, predictors of peptide-MHC interaction have in most cases been trained on binding affinity data. Recently an increasing amount of MHC presented peptides identified by mass spectrometry has been published containing information about peptide processing steps in the presentation pathway and the length distribution of naturally presented peptides. Here, we present NetMHCpan-4.0, a method trained on both binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increased predictive performance compared to state-of-the-art both when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.
PMID: 28978689
Full text: [PDF]
NetMHCpan - MHC class I binding prediction beyond humans
Hoof I1,
Peter B3,
Sidney J3,
Pedersen LE2
Lund O1,
Buus S2,
Nielsen M1
Immunogenetics. 2009 Jan;61(1):1-13.
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
3La Jolla Institute for Allergy and Immunology, San Diego, California, United States of America
Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide-MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method's ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.
PMID: 19002680
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:
