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MHCcluster - 2.0

MHC clustring based on binding specififcity


The MHC genomic region in most species is extremely polymorphic. The distinct specificity of the majority of the MHC molecules remains uncharacterized. The MHCcluster is a tool to functionally cluster MHC class I molecules (MHCI) based on their predicted binding specificity. The tool provides highly intuitive heat-map and graphical tree-based visualizations of the functional relationship between MHC class I and class II variants.

For MHC class I, peptide binding predictions are made using NetMHCpan-2.8 PMID 19002680.
For MHC class II, peptide binding predictions are made using NetMHCIIpan-3.2 PMID 21073747.

Note, the predictions used by MHCCluster are generated using NetMHCpan-2.8 (class I) and NetMHGCIIpan-3.2 (class II). These tools are outdated, and the accuracy of the prediction motifs should be taken with caution, in perticular for HLA-C and HLA-II

Submission

Number of peptides to include  

Number of Bootstrap calculations 

Fraction of peptides to include in correaltion analysis 

Select MHC class: 

Select NetMHCpan prediction method (only for class I)

Select allele set/loci/species to include in tree

Select Allele(s) or type allele names (ie HLA-A01:01) separated by commas (and no spaces). 

or upload file with allele names (one name per line). 

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


or paste full length MHC protein sequence(s) in FASTA format into the field below:

or submit a file containing full length MHC protein sequence(s) in FASTA format directly from your local disk:

Restrictions:

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

CITATIONS

For publication of results, please cite:

  • MHCcluster, a method for functional clustering of MHC molecules. Martin Thomsen, Claus Lundegaard, Soren Buus, Michael Rasmussen, Ole Lund and Morten Nielsen
    Immunogenetics. 2013 Jun 18. [Epub ahead of print]

Article abstracts

Main reference:

MHCcluster, a method for functional clustering of MHC molecules.
Thomsen M1, Lundegaard C1,3, Buus S4, Lund O1, Nielsen M1,2,
Immunogenetics. 2013, Jun 18.

1Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
2Instituto de Investigaciones Biotecnolo#gicas, Universidad Nacional de San Martin, San Martin, Buenos Aires, Argentina,
2ALK, Boege Alle 6, DK-2970 Hoersholm, Denmark,
4Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark

The identification of peptides binding to major histocompatibility complexes (MHC) is a critical step in the understanding of T cell immune responses. The human MHC genomic region (HLA) is extremely polymorphic comprising several thousand alleles, many encoding a distinct molecule. The potentially unique specificities remain experimentally uncharacterized for the vast majority of HLA molecules. Likewise, for nonhuman species, only a minor fraction of the known MHC molecules have been characterized. Here, we describe a tool, MHCcluster, to functionally cluster MHC molecules based on their predicted binding specificity. The method has a flexible web interface that allows the user to include any MHC of interest in the analysis. The output consists of a static heat map and graphical tree-based visualizations of the functional relationship between MHC variants and a dynamic TreeViewer interface where both the functional relationship and the individual binding specificities of MHC molecules are visualized. We demonstrate that conventional sequence-based clustering will fail to identify the functional relationship between molecules, when applied to MHC system, and only through the use of the predicted binding specificity can a correct clustering be found. Clustering of prevalent HLA-A and HLA-B alleles using MHCcluster confirms the presence of 12 major specificity groups (supertypes) some however with highly divergent specificities. Importantly, some HLA molecules are shown not to fit any supertype classification. Also, we use MHCcluster to show that chimpanzee MHC class I molecules have a reduced functional diversity compared to that of HLA class I molecules. MHCcluster is available at www.cbs.dtu.dk/services/MHCcluster-2.0 .

PMID: 23775223

Full text

Usage Instructions

The submission screen can be split in the following three parts:

  1. Adjust prediction settings.
  2. Choose MHC alleles to compare (choose at least 3 alleles).
  3. Upload your own MHC alleles (optional) *.
* Note: Be patient with new MHC uploads. Computing prediction models for new alleles is computational exhaustive, thus significantly prolonging the computation time.

Adjusting Prediction Settings

In the prediction settings part the user can:

  1. Adjust the amount of peptides to include.
  2. Adjust the amount of bootstrap calculations.
  3. Adjust the fraction of peptides to use in the correlation analysis.

Adjusting Prediction Settings


 

Choose MHC Alleles

In the MHC allele lists part the user can:

  1. Set the class type (MHC class I or MHC class II).
  2. Switch between allele sets.
  3. Pick specific alleles from the list, or select all alleles in the set.
  4. Manually add or remove chosen alleles.
  5. Upload a file containing the allele names
  6. See a list of all available alleles.
Choose MHC Alleles


 

New MHC Alleles

In the new MHC alleles part the user can paste or upload full length MHC protein sequences in fasta format. *
This option enables the user to compare new MHC alleles against the known MHC alleles.

* Note: Be patient. Computing prediction models for new alleles is computational exhaustive, thus significantly prolonging the computation time.
New MHC Alleles

 

Output Guide

The result screen can be split in the following three parts:

  1. A static tree showing the MHC binding motif relationship. *
  2. A heat map showing the Pearson correlation between the MHC alleles.
  3. Download links for the result files.
* Note: By clicking on the image or the link below you will be redirected to the interactive tree viewer (click here to see the tree viewer guide).

Static Tree

The static tree shows how the different MHC alleles clusters together.
By clicking the on the image or the link below, the user is taken to an advanced tree viewer. Here the user can customize the tree and see the binding motifs directly on the tree.
To see the guide for this interactive tree click here.

Static Tree


 

Heat Map

The heat map gives a quick overview of how similar the MHC binding motifs are. The scale corresponds to the distance between the alleles, where Red (0) is very similar and beige/white (1) is dissimilar.
On the left side and on the top of the matrix is two small trees depicting the hierarchical clusters of the MHC alleles.
On the right side and in the bottom of the matrix are the labels.

Heat Map


 

Download Links

This part lists all the downloadable files:

  1. Link to the tree in PNG.
  2. Link to the tree in PDF.
  3. Link to the concensus tree. A newick file which can be shown in most tree viewers.
  4. Link to the heat map in PNG.
  5. Link to the heat map in PDF.
  6. Link to the heat map distance file.
  7. Link to the prediction accuracy file.
  8. Link to a zip file containing all the prediction files.
  9. Link to a zip file containing all the binding motifs of the alleles.
Download Links

 

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: