Exercise:AntigenProcessing ans

Answers

Get the Data and Filter It

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

Answer: 646 Epitopes.

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

Answer: Not really. It would be more precise to call them MHC ligands or HLA-presented peptides.

Explanation: Mass spectrometry demonstrates that a peptide was isolated in association with an MHC molecule and therefore provides evidence that the peptide is naturally processed and presented. However, this does not by itself demonstrate that the peptide is recognized by a T-cell receptor or induces a T-cell response.

An epitope generally refers to a molecular structure that is recognized by the adaptive immune system. Therefore, an MHC ligand only becomes a demonstrated T-cell epitope when there is evidence of T-cell recognition.

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

Answer: Wang et al. found 2274 HLA-DR presented peptides

Be aware that the number of non-redundant presented peptides reported in the paper is not be identical to the IEDB number: 1,593. Why is that?

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

+ DEAM(N2) and + CITR(R8) Deamination and Citrulation

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

Answer: Each RA sample has different alleles:

RA1    DRB1_0402,DRB1_1104
RA2    DRB1_0101,DRB1_0401
RA3    DRB1_0101,DRB1_0401
RA4    DRB1_0401,DRB1_1501
RA5    DRB1_0801,DRB1_1501

MHCMotifDecon

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

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

A total of 1365 peptides were analysed. Although 1496 sequences were submitted, only peptides within the selected length range of 12–21 amino acids were included in the deconvolution.

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

Answer: Yes. Peptides assigned to specific HLA alleles show clear and reproducible binding motifs, with strong amino-acid preferences at particular positions of the peptide-binding core.

In contrast, the Trash clusters show much weaker and less defined sequence motifs. These peptides are predicted to bind poorly to all HLA alleles included for that patient and may therefore represent weak binders, contaminants, or peptides presented by an HLA molecule that was not included in the analysis.

For RA4 and RA5, only 3 and 7 peptides, respectively, were assigned to Trash. Because MHCMotifDecon requires at least 10 sequences to generate a logo, no Trash logo was produced for these patients.

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

Answer: The %Rank threshold determines how strong the predicted interaction must be for a peptide to be assigned to an HLA molecule.

The current analysis uses:

%Rank threshold = 20

Lowering the threshold makes the assignment more stringent:

lower %Rank threshold → fewer peptides assigned to HLA → more peptides assigned to Trash

Increasing the threshold makes the assignment more permissive:

higher %Rank threshold → more peptides assigned to HLA → fewer peptides assigned to Trash

A more stringent threshold may produce cleaner motifs, but it may also exclude genuine HLA ligands.

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

Answer: This cannot yet be determined from the current analysis because DRB4_0101 was not included among the HLA alleles in this run.

To answer this question, repeat the analysis after adding:

DRB4_0101

to the relevant patients expressing DRB1*04:01 (RA2, RA3 and RA4).

After the new run, compare the peptide assignments with the original analysis. Peptides assigned to DRB4_0101 may previously have been assigned to one of the DRB1 alleles or to the Trash cluster.

GibbsCluster

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

Answer: Not necessarily. The number of sequence clusters identified by GibbsCluster does not have to equal the number of HLA molecules expressed by the sample.

In this experiment, after taking DRB4 into account, there are three relevant HLA-DR specificities:

DRB1*01:01 DRB1*04:01 DRB4*01:01

However, an unsupervised clustering analysis may recover only the strongest distinguishable motifs and may fail to resolve a smaller peptide population such as the DRB4 repertoire as an independent cluster.

Explanation: GibbsCluster does not know the patient's HLA genotype. It only examines patterns in the peptide sequences and asks how the sequences can best be partitioned into clusters.

Therefore:

number of sequence clusters ≠ necessarily number of HLA alleles

Two alleles with similar motifs may be merged into one cluster, a small allele-specific repertoire may not form a sufficiently strong independent cluster, or noisy peptides may affect the clustering solution.

This contrasts with MHCMotifDecon, where the known HLA molecules are explicitly supplied to the algorithm.

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

Answer: Yes. The most important difference is that MHCMotifDecon is supervised by HLA-binding predictions and known HLA typing, whereas GibbsCluster is an unsupervised sequence-clustering method.

Explanation:

With MHCMotifDecon:

• the HLA alleles expressed by the sample are supplied beforehand;
• each peptide is evaluated using allele-specific binding predictions;
• clusters are directly labelled with specific HLA molecules;
• peptides that do not bind any supplied allele sufficiently well can be placed in a trash cluster; and
• relatively small allele-specific peptide populations may still be detected because the algorithm already knows which alleles to test.

With GibbsCluster:

• no HLA genotype information is required;
• peptides are grouped according to similarities in their sequence motifs;
• the resulting clusters are not intrinsically labelled as particular HLA alleles;
• allele identities have to be inferred afterwards by comparing the motifs with known HLA-binding motifs; and
• weak or small motifs can be merged with larger clusters or may not emerge as independent clusters.

Therefore, the two approaches answer slightly different questions.

MHCMotifDecon asks: Given the HLA molecules that I know are present, which HLA molecule most likely presented each peptide?

GibbsCluster asks: Without knowing which HLA molecules are present, how many different sequence patterns can I detect in this peptide dataset?

Using both approaches is useful because agreement between them gives additional confidence in the inferred motifs, while disagreement can reveal incomplete HLA typing, low-abundance HLA molecules, overlapping binding specificities, or contaminants.