ImmunoBiology Computer exercises

Learning objectives:

Get familiar with MHC-peptide binding prediction tools.
Demonstrate how certain assumptions drastically effect the results of computational analysis.
Get a better understanding of MHC-disease associations via HIV-1 example.

Background: Read Kiepiela et al beforehand. Think about the following issues as a preparation for the exercise:

Which CTL responses seem to be associated with slow disease progression in HIV-1 infection?
Which HLA alleles are associated with slow and fast progression to AIDS?

Main research question: What is the mechanism behind the fact that certain MHC molecules are associated with slow progression to AIDS? Try to answer this question (the subquestions below will help you to do that) as much as possible using your own analysis. If you are stuck, refer to the hints at the end of the page.

HLA-disease associations

Study Figure 2 of Kiepiela et al again. What does this result tell you about the relationship between CD8 responses to different HIV-1 proteins and viral loads?

Design and perform an analysis that can show that B*5703 and B*1801 target different HIV-1 proteins, ie. do they have different number (or density) of epitopes from different HIV-1 proteins using the peptide binding predictions: NetMHCpan server (see hints 1&2). Focus on peptides of length 9 for this exercise and assume that top 1% of the peptides (sorted based on binding affinity) is the set of predicted epitopes for each HLA molecule.

Compare your results to expected number of epitopes from each protein. In NetMHC output you can see easily how many peptides of length 9 there are per HIV-1 protein. Remember: we are using the top 1% threshold to define potential epitopes.

Study the binding motif of B*5703 and B*1801 using the Motif viewer link on NetMHCpan page. Can you now understand why these two HLA molecules present different HIV-1 peptides?

it has even been suggested that stability correlates better with immunogenicity than affinity does. It is possible to predict the stability of peptide-MHC complexes (in terms of their helf-lives) using NetMHCstabpan . Repeat the analysis you performed for the binding affinities now for the stability of peptide-MHC complexes for HLA-B*5703 and B*1801. Compare these results with your findings on the binding affinities.

Why do you think it might be beneficial to present HIV-1 Gag? To answer this question search internet/PubMed for more information mutability of HIV-1 proteins.
7.	Now let us connect these results with what you learned on the lecture about Selective Sweep by SIV in chimpanzee populations by Prof. Ronald Bontrop. Chimpanzee MHC alleles PatrB02 and PatrB03 were protective against SIV infection. Can you repeat the following analysis for those alleles using SIV proteins? Are they also preferentially presenting epitopes from Gag protein?

Links

Scanning electron micrograph of HIV-1 budding from cultured lymphocyte

Hints

To find which MHC molecules target which HIV-1 proteins, you need to use a data set that contains all HIV-1 proteins. We have prepared such a file for you here. Alternatively, you can generate your HIV-1 protein data set in many ways, e.g. you can search for the HIV-1 genome at NCBI and via the protein coding regions download all protein sequences (use the "Protein" link on the right hand side of the page under "Related Information" header. If you search for "Human immunodeficiency virus 1", it is easier to reach the genome assembly of HIV-1. Under Summary option, at the top of the page, you can choose for "FASTA (text)" format and download all the sequences at once). Make sure you don't use concatenated proteins (e.g. Gag-Pol) for the analysis. If you have such sequences check out the NCBI entry to figure out which positions contain Gag and which positions contain Pol.
To make MHC-peptide predictions upload a file in fasta format to NetMHCpan. Make sure that input type is set to Fasta. To analyze the output of NetMHCpan use the "Save output in XLS format" option, save the output (link is at the end of the output page; using right mouse button and "save as" option you can save the output). You can then use this output file in R to analyse the results. Try this first self, and if you get stuck, you can take a look at this help file. To make a prediction for a specific allele, choose under the Select species/loci menu HLA-B. Remember smaller the predicted binding value, the better the binding. If you are creating your own fasta files it should contain the protein names as the first word of the identifier line.
To analyse the relationship between chimpanzee alleles and SIV, you can use the protein file we prepapred for you here. We have learned in the lecture that PatrB02 and PatrB03 alleles were protective with respect to SIV infections. More information on the CTL responses generated by these alleles can be found in this article .