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

SUBMISSION

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

Type of input Fasta Peptide

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):  
8mer peptides 9mer peptides 10mer peptides 11mer peptides 12mer peptides 13mer peptides 14mer peptides

Paste a single or several peptides in PEPTIDE format into the field below:

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

Select species/loci
HLA supertype representative HLA-A HLA-B HLA-C HLA-E Chimpanzee (Patr) Rhesus macaque (Mamu) Mouse (H-2) BoLA Pig (SLA)
Select Allele (max 20 per submission) HLA-A*01:01 (A1) HLA-A*02:01 (A2) HLA-A*03:01 (A3) HLA-A*24:02 (A24) HLA-A*26:01 (A26) HLA-B*07:02 (B7) HLA-B*08:01 (B8) HLA-B*27:05 (B27) HLA-B*39:01 (B39) HLA-B*40:01 (B44) HLA-B*58:01 (B58) HLA-B*15:01 (B62) HLA-A*01:01 HLA-A*02:01 HLA-A*02:02 HLA-A*02:03 HLA-A*02:05 HLA-A*02:06 HLA-A*02:07 HLA-A*02:11 HLA-A*02:12 HLA-A*02:16 HLA-A*02:17 HLA-A*02:19 HLA-A*02:50 HLA-A*03:01 HLA-A*11:01 HLA-A*23:01 HLA-A*24:02 HLA-A*24:03 HLA-A*25:01 HLA-A*26:01 HLA-A*26:02 HLA-A*26:03 HLA-A*29:02 HLA-A*30:01 HLA-A*30:02 HLA-A*31:01 HLA-A*32:01 HLA-A*32:07 HLA-A*32:15 HLA-A*33:01 HLA-A*66:01 HLA-A*68:01 HLA-A*68:02 HLA-A*68:23 HLA-A*69:01 HLA-A*80:01 HLA-B*07:02 HLA-B*08:01 HLA-B*08:02 HLA-B*08:03 HLA-B*14:02 HLA-B*15:01 HLA-B*15:02 HLA-B*15:03 HLA-B*15:09 HLA-B*15:17 HLA-B*18:01 HLA-B*27:05 HLA-B*27:20 HLA-B*35:01 HLA-B*35:03 HLA-B*38:01 HLA-B*39:01 HLA-B*40:01 HLA-B*40:02 HLA-B*40:13 HLA-B*42:01 HLA-B*44:02 HLA-B*44:03 HLA-B*45:01 HLA-B*46:01 HLA-B*48:01 HLA-B*51:01 HLA-B*53:01 HLA-B*54:01 HLA-B*57:01 HLA-B*58:01 HLA-B*58:02 HLA-B*73:01 HLA-B*83:01 HLA-C*03:03 HLA-C*04:01 HLA-C*05:01 HLA-C*06:02 HLA-C*07:01 HLA-C*07:02 HLA-C*08:02 HLA-C*12:03 HLA-C*14:02 HLA-C*15:02 HLA-E*01:01 Patr-A*0101 Patr-A*0301 Patr-A*0401 Patr-A*0701 Patr-A*0901 Patr-B*0101 Patr-B*1301 Patr-B*2401 Mamu-A1*00101 (Mamu-A*01) Mamu-A1*00201 (Mamu-A*02) Mamu-A1*00701 (Mamu-A*07) Mamu-A1*01101 (Mamu-A*11) Mamu-A2*0101 Mamu-A1*02201 Mamu-A1*02601 (Mamu-A*26) Mamu-A7*0103 Mamu-B*00101 (Mamu-B*01) Mamu-B*00301 (Mamu-B*03) Mamu-B*00801 (Mamu-B*08) Mamu-B*01001 Mamu-B*01701 (Mamu-B*17) Mamu-B*03901 (Mamu-B*39) Mamu-B*05201 Mamu-B*06601 (Mamu-B*66) Mamu-B*08401 Mamu-B*08701 H-2-Db H-2-Dd H-2-Kb H-2-Kd H-2-Kk H-2-Ld BoLA-D18.4 BoLA-HD6 BoLA-JSP.1 BoLA-T2a BoLA-T2b BoLA-T2C SLA-1*0401 SLA-2*0401 SLA-3*0401 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 

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

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

Developed under the following contracts:

  * 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

PORTABLE VERSION

Would you prefer to run NetMHC at your own site? NetMHC v. 4.0 will soon be 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 software@cbs.dtu.dk.

GETTING HELP