DTU Health Tech
Department of Health Technology
This link is for the general contact of the DTU Health Tech institute.
If you need help with the bioinformatics programs, see the "Getting Help" section below the program.
NOTE. New in this version. The method has been updated to include the newest MHC allele releases from the IMGT/HLA and IPD-MHC databases (for non-human primates and pig). This includes adaptation of the new nomenclature for HLA and Rhesus macaque (Mamu) alleles. The MHC binding predictions has been updated to NetMHCpan version 2.3.
Predictions can be made for 8-11mer peptides. Note that all non 9mer predictions are made using approximations. Most HLA molecules have a strong preference for binding 9mers.
The method integrates prediction of peptide MHC class I binding, proteasomal C terminal cleavage and TAP transport efficiency. MHC class I binding and proteasomal cleavage is performed using artificial neural networks. TAP transport efficiency is predicted using weight matrix.
The prediction values are calculated as a weighted average of the MHC, TAP and C terminal cleavage scores. and as %-Rank to a set of 200.000 random natural peptides.
The MHC peptide binding is predicted using neural networks trained as described for the NetMHCpan-4.0 server. The proteasome cleavage event is predicted using the version of the NetChop neural networks trained on C terminals of known CTL epitopes as describe for the NetChop-3.0 server. The TAP transport efficiency is predicted using the weight matrix based method describe by Peters et al., 2003
Species Warning. Note, that both the proteasome and TAP predictions were developed using experimental data for human versions of the molecule. At least for TAP molecules, there are known to be some species dependent differences in specificity. Therefore, using these predictions for eptitope processing in non-human cells should only be done with extra caution in interpreting results.
For publication of results, please cite:
Data resources used to develop this server was obtained from
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:
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.
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).
Give threshold value for binding values to be displayed.
Click the box Sort by affinity to have the output sorted by descending predicted binding affinity
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 bottum of the results output file.
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.
# NetCTLpan version 1.1 # Peptide length 9 # NetCTLpan predictions for HLA-A01:01 allele. # N Sequence Name Allele Peptide MHC TAP Cle Comb %Rank 0 143B_BOVIN__P29 HLA-A01:01 TMDKSELVQ 0.10500 -0.18300 0.16188 0.13685 50.00 1 143B_BOVIN__P29 HLA-A01:01 MDKSELVQK 0.02300 0.21200 0.53837 0.14943 50.00 2 143B_BOVIN__P29 HLA-A01:01 DKSELVQKA 0.01200 -0.77000 0.78670 0.16976 50.00 3 143B_BOVIN__P29 HLA-A01:01 KSELVQKAK 0.07600 0.32900 0.45985 0.18769 32.00 4 143B_BOVIN__P29 HLA-A01:01 SELVQKAKL 0.01400 0.99100 0.91927 0.24561 32.00 ... 235 143B_BOVIN__P29 HLA-A01:01 DEGDAGEGE 0.00300 -2.21000 0.04146 -0.04292 50.00 236 143B_BOVIN__P29 HLA-A01:01 EGDAGEGEN 0.02000 -2.10100 0.05666 -0.01978 50.00 ---------------------------------------------------------------------- Number of MHC ligands 4 identified. Number of peptides 237. Allele HLA-A0101. Protein name 143B_BOVIN__P29 ----------------------------------------------------------------------
Main reference:
NetCTLpan. Pan-specific MHC class I pathway epitope predictions.
Stranzl T., Larsen M. V., Lundegaard C., Nielsen M.
Immunogenetics. 2010 Apr 9. [Epub ahead of print]
Center for Biological Sequence Analysis,
Technical University of Denmark,
DK-2800 Lyngby, Denmark
Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific MHC class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I binding affinities into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8, 9, 10 and 11-mer epitopes. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity.
The method was trained and validated on large data sets of experimentally identified MHC class I ligands and CTL epitope. It has been reported, that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC differences and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules.
The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further illustrate the importance of using full-type HLA restriction information when identifying MHC class-I epitopes. When compared to the NetMHCpan and NetCTL methods, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40% respectively.
The method and benchmark data set are available at http://www.cbs.dtu.dk/services/NetCTLpan-1.0.
PMID: 20379710
The data sets used in the benchmark calculationis are given below in the FASTA format. For each entry is given the Uniprot of the protein "hosting" the epitope, the epitope sequence, and the HLA full-type information (if applicable), and the HLA supertype (if applicable).
An example showing part of such a fasta file is given below
>sp|O43707|ACTN4_HUMAN AIDQLHLEY HLA-A*0101 A1 MVDYHAANQSYQYGPSSAGNGAGGGGSMGDYMAQEDDWDRDLLLDPAWEKQQRKTFTAWC NSHLRKAGTQIENIDEDFRDGLKLMLLLEVISGERLPKPERGKMRVHKINNVNKALDFIA SKGVKLVSIGAEEIVDGNAKMTLGMIWTIILRFAIQDISVEETSAKEGLLLWCQRKTAPY KNVNVQNFHISWKDGLAFNALIHRHRPELIEYDKLRKDDPVTNLNNAFEVAEKYLDIPKM LDAEDIVNTARPDEKAIMTYVSSFYHAFSGAQKAETAANRICKVLAVNQENEHLMEDYEK LASDLLEWIRRTIPWLEDRVPQKTIQEMQQKLEDFRDYRRVHKPPKVQEKCQLEINFNTL
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:
