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NetMHC 4.0 Server

Prediction of peptide-MHC class I binding using artificial neural networks (ANNs).

View the version history of this server. All previous versions are available online, for comparison and reference.

ANNs have been trained for 81 different Human MHC alleles including HLA-A, -B, -C and -E. Furthermore, predictions for 41 animal (Monkey, Cattle, Pig, and Mouse) alleles are available. If your molecule of interest is not found in the list below, please use NetMHCpan which can predict peptide-MHC class I binding for any allele of known sequence.

Predictions can be made for peptides of any length.
Note that most HLA molecules have a strong preference for binding 9mers. Predictions for peptides longer than 11 amino acids should be taken with caution.

NEW: View sequence motifs for the alleles in the NetMHC library. Click on Sequence Motifs in the pink bar below.

The project is a collaboration between CBS, ISIM, and LIAI.

Instructions Output format Sequence motifs Article abstract


Hover the mouse cursor over the symbol for a short description of the options

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 (you may select multiple lengths):  

Select species/loci

Select Allele (max 20 per submission) or type allele names (ie HLA-A0101) separated by commas.

For list of allowed allele names click here List of MHC allele names.

Threshold for strong binders: % Rank  
Threshold for weak binders: % Rank  

Sort by predicted affinity  

Save output in XLS format 

At most 5000 sequences per submission; each sequence not more than 20,000 amino acids and not less than 8 amino acids.

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


Developed under the following contracts:

  * National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, contract No. HHSN272201200010C

  * Agencia Nacional de Promoción Científica y Tecnológica, Argentina (PICT-2012-0115)

For publication of results, please cite:

  • Gapped sequence alignment using artificial neural networks: application to the MHC class I system.
    Andreatta M, Nielsen M
    Bioinformatics (2016) Feb 15;32(4):511-7

    Abstract - PubMed: 26515819   [PDF]

  • Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.
    Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O.
    Protein Sci., (2003) 12:1007-17

    Abstract - PubMed: 12717023


Would you prefer to run NetMHC at your own site? NetMHC v. 4.0 is available as a stand-alone software package, with the same functionality as the service above. Ready-to-ship packages exist for the most common UNIX platforms. There is a download page for academic users; other users are requested to contact CBS Software Package Manager at


Scientific problems:        Technical problems: