Exercise:AntigenProcessing

Exercise written by Carolina Barra Quaglia

Introduction

Immunopeptidomics experiments can identify thousands of peptides presented on the cell surface by MHC molecules. However, because an individual can express several different HLA alleles, it is often not immediately clear which HLA molecule is responsible for presenting each peptide. In addition, immunopeptidomics datasets may contain co-purified peptides that are not true MHC ligands.

In this exercise, we will work with an immunopeptidomics dataset from patients with rheumatoid arthritis (RA). We will first use the Immune Epitope Database (IEDB) to identify and retrieve the experimental peptide dataset and the corresponding HLA typing information.

We will then use MHCMotifDecon to assign the detected peptides to their most likely HLA restriction element and investigate how different analysis parameters and assumptions about the HLA genotype affect the deconvolution.

Finally, we will analyse the same peptide dataset with GibbsCluster, an unsupervised peptide clustering method, and compare the resulting motifs and clusters with those obtained using MHCMotifDecon.

Learning Objectives

After completing this exercise, you should be able to:

• Retrieve and filter an immunopeptidomics dataset from the IEDB.
• Identify relevant experimental information, including peptide modifications and the HLA alleles expressed in the sample.
• Explain the purpose of MHC motif deconvolution in the analysis of immunopeptidomics data.
• Use MHCMotifDecon to assign peptides to likely HLA restriction elements.
• Evaluate how the %rank threshold and the choice of HLA alleles influence the deconvolution results.
• Recognize the importance of considering additional HLA-DR molecules, such as HLA-DRB3, HLA-DRB4, and HLA-DRB5, when interpreting HLA-DR immunopeptidomics data.
• Use GibbsCluster to perform unsupervised clustering of MHC class II peptide ligands.
• Compare supervised and unsupervised approaches for identifying peptide-binding motifs in complex immunopeptidomics datasets.

Current Challenges

Immunopeptidomics is currently used in several biomedical applications, including neo-epitope discovery in oncology, protein-drug immunogenicity assessment, as well as next-generation vaccine development. Immunopeptidome data are highly complex given by natural complexity of proteomics data, and particularly for immunopeptidomics, due to the expression of multiple HLA alleles on the cell membrane and the presence of co-immunoprecipitated contaminants, which is an inherent drawback of the immunopeptidomic workflow.

To resolve this problem, we can use either MHCMotifDecon or GibbsCluster. MHCMotifDecon benefits from state-of-the-art MHC class I and class II predictions, NetMHCpan and NetMHCIIpan, respectively, to accurately deconvolute immunopeptidome datasets and assign individual ligands to the most likely MHC-restriction element while discarding co-purified contaminants. If we have HLA typing this is a great tool, on the other hand in absence of HLA alleles, GibbsCluster can assess what are the different specificities in a immunopeptidomics sample.

Data Collection: Get the Data and Filter It

Every good bioinformatics exercise starts with some data collection.

Start by going to the IEDB home page to perform your query.

Go to the IEDB and search the database for assays with MHC ligands in particular we want "mass spectrometry assays" in humans with rheumatoid arthritis.

There is growing evidence that MHC class II has a role in presenting self-peptides to induce autoimmunity, so we are going to focus only on MHC class II.

• Q1: How many epitopes do you find with the initial filter?

• Q2: Do you agree with calling these peptides epitopes? Why?

Now refine your search by including only mass-spectrometry assays on HLA-DR restriction alleles. From the References tab, select the publication from the Journal of Proteome Research from 2017 by Wang et al.

• Q3: How many eluted ligands did they find in this study?

Under the Assays tab, select MHC ligand assays and export these results.

Open the exported file using Excel or your preferred program and inspect the different columns. We are interested in the epitope modification column (Modifications).

Note that there are two peptide sequences that contain post-translational modifications.

• Q4: What kind of post-translational modifications are present?

These modified peptides look very interesting. However, the MHC Motif Deconvolution software cannot currently deal with them, so exclude them from the dataset.

In addition, inspect the column containing the MHC Types present in the host.

• Q5: Which alleles are expressed in the patient used for this assay?

You should now have both the peptide dataset and the HLA typing information required to use MHCMotifDecon.

MHCMotifDecon

Go to the MHCMotifDecon server.

Enter the submission page and select MHC class II.

Paste the list of peptides into the input window. The input should contain only one column of peptide sequences.

Scroll down and select the HLA-DR alleles expressed by the cell line for which the immunopeptidomics assay was performed.

Press Submit.

Note: Be aware that HLA alleles are entered into this software using a particular naming format. For example:

HLA-DRB1*01:01 → DRB1_0101

• Q6: How many peptides were assigned to each of the alleles, and how many were assigned to the trash cluster?

• Q7: Can you see any differences between the trash-cluster peptides and the peptides assigned to a specific allele? Explain.

Go back to the previous submission and modify the %rank threshold.

• Q8: What is the effect of changing the %rank threshold?

We know that HLA-DRB1 is not the only HLA-DR protein expressed by humans. HLA-DRB3, HLA-DRB4, and HLA-DRB5 are located in close proximity to HLA-DRB1 on the chromosome and are often expressed together.

In particular, cell lines expressing HLA-DRB1*04:01 are strongly associated with the expression of a second allele called HLA-DRB4*01:01.

If you want to learn more about this topic, search for HLA-DR linkage disequilibrium.

Having this information, extend the HLA typing of the experiment by including:

DRB4_0101

Run MHCMotifDecon again with this additional allele.

• Q9: How many peptides are now associated with HLA-DRB4? Where did these peptides come from?

GibbsCluster

We will now analyse the same ligand dataset using an unsupervised approach.

Go to the GibbsCluster server.

Enter the submission page and paste the list of ligands into the input window.

Because GibbsCluster uses unsupervised learning, you do not need to specify the HLA alleles present in the sample.

Select the MHC class II parameters.

Change:

Number of iterations per sequence per temperature step = 100

and select:

Preference for hydrophobic AAs at P1

Click Submit.

• Q10: Does the number of clusters found by GibbsCluster correspond to the number of HLA alleles in your sample?

• Q11: Can you identify other differences between the GibbsCluster solution and the MHCMotifDecon solution?

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You should now have experience with two different approaches for analysing complex immunopeptidomics datasets:

• MHCMotifDecon – supervised motif deconvolution using known HLA alleles and MHC-binding predictions.
• GibbsCluster – unsupervised clustering of peptide sequences without prior knowledge of the HLA alleles.

Consider how prior knowledge of the HLA type influences the interpretation of an immunopeptidomics dataset and how the results from the two approaches differ.

Done!