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?

RA1 DRB1_0402=141,DRB1_1104=71 Trash=18
RA2 DRB1_0101=193,DRB1_0401=245 Trash=52
RA3 DRB1_0101=124,DRB1_0401=301 Trash=11
RA4 DRB1_0401=51,DRB1_1501=16 Trash=3
RA5 DRB1_0801=101,DRB1_1501=31 Trash=7

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: Lowering the %Rank threshold from 20% to 2% makes the peptide assignment considerably more stringent. Peptides with a best predicted binding rank between 2% and 20% are no longer accepted as binders and are instead assigned to the Trash cluster.

The effect can be seen directly by comparing the two runs:

RA1 Trash: 18 → 51 (+33)
RA2 Trash: 52 → 91 (+39)
RA3 Trash: 11 → 34 (+23)
RA4 Trash: 3 → 11 (+8)
RA5 Trash: 7 → 37 (+30)

Across all five patients:

%Rank 20 91 peptides in Trash
%Rank 2 224 peptides in Trash

Thus, lowering the threshold to 2% moved 133 additional peptides into the Trash cluster.

Correspondingly, the number of peptides assigned to the HLA-DRB1 alleles decreased:

RA1 DRB1_0402: 141 → 120 DRB1_1104: 71 → 59
RA2 DRB1_0101: 193 → 177 DRB1_0401: 245 → 222
RA3 DRB1_0101: 124 → 118 DRB1_0401: 301 → 284
RA4 DRB1_0401: 51 → 44 DRB1_1501: 16 → 15
RA5 DRB1_0801: 101 → 77 DRB1_1501: 31 → 25

The total number of peptides assigned to an HLA allele therefore decreased from 1274 at the 20% threshold to 1141 at the 2% threshold.

Importantly, the HLA-specific motifs remain broadly similar, but the stricter threshold retains only peptides with stronger predicted binding. The main effect of lowering the threshold is therefore to increase confidence in the retained HLA assignments at the cost of assigning many more peptides to Trash.

At the 2% threshold, all five patients also have at least 10 Trash peptides, so MHCMotifDecon is able to generate a Trash motif for every patient.

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: After including the secondary HLA-DR molecules, 97 peptides were assigned to DRB3, DRB4 or DRB5.

The assignments for each patient were:

RA1 DRB3_0202=19,DRB4_0101=8
RA2 DRB4_0101=14
RA3 DRB4_0101=12
RA4 DRB4_0101=1,DRB5_0101=7
RA5 DRB5_0101=36

Overall:

DRB3 19 peptides
DRB4 35 peptides
DRB5 43 peptides

These peptides were previously assigned either to one of the DRB1 alleles or to the Trash cluster because the corresponding secondary HLA-DR molecules were not included in the first analysis.

Comparing the two runs shows:

RA1 27 peptides reassigned: 12 from DRB1_0402, 7 from DRB1_1104 and 8 from Trash
RA2 14 peptides reassigned: 3 from DRB1_0101, 6 from DRB1_0401 and 5 from Trash
RA3 12 peptides reassigned: 1 from DRB1_0101, 10 from DRB1_0401 and 1 from Trash
RA4 8 peptides reassigned: 6 from DRB1_0401 and 2 from DRB1_1501
RA5 36 peptides reassigned: 28 from DRB1_0801, 7 from DRB1_1501 and 1 from Trash

For RA2, RA3 and RA5, only one new secondary allele was introduced, so the origin of the reassigned peptides can be determined directly from the change in counts.

For RA1, both DRB3_0202 and DRB4_0101 were added, and for RA4 both DRB4_0101 and DRB5_0101 were added. Therefore, the summary counts alone cannot determine exactly which previous DRB1 or Trash peptides were reassigned to each individual secondary allele.

The result demonstrates that including the secondary HLA-DR molecules substantially changes the deconvolution. In total, 97 peptides that were previously attributed to DRB1 or Trash are instead predicted to be presented by DRB3, DRB4 or DRB5.

GibbsCluster

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

Answer: No. When all five RA patients were combined, GibbsCluster tested solutions containing 1–5 clusters but selected a solution containing only one sequence motif.

This does not correspond to the number of HLA-DR molecules present in the samples. Across the five patients there are already six different DRB1 alleles:

DRB1_0101
DRB1_0401
DRB1_0402
DRB1_0801
DRB1_1104
DRB1_1501

If the secondary HLA-DR molecules identified in the previous exercise are also considered, the dataset additionally contains:

DRB3_0202
DRB4_0101
DRB5_0101

Thus, several different HLA-DR specificities are present in the combined dataset, but GibbsCluster finds only one dominant motif.

This illustrates an important limitation of unsupervised motif deconvolution: the number of sequence clusters does not necessarily correspond directly to the number of HLA alleles. Different HLA molecules can have overlapping binding preferences, and smaller motifs may be hidden by larger, dominant peptide populations.

In the selected one-cluster solution:

Cluster 1	948 peptides
Outliers	94 peptides

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

Answer: Yes. The two methods produce quite different interpretations of the same immunopeptidomics data.

MHCMotifDecon uses the known HLA genotype of each patient together with peptide–HLA binding predictions. It therefore separates the peptides into motifs corresponding to specific HLA molecules, for example:

RA2	DRB1_0101,DRB1_0401,DRB4_0101
RA5	DRB1_0801,DRB1_1501,DRB5_0101

In contrast, GibbsCluster does not know which HLA alleles are present. It groups peptides only according to similarities in their amino-acid sequences. Consequently, when peptides from all five patients are combined, the different HLA-specific motifs are largely merged into a single dominant motif rather than being separated according to HLA allele.

GibbsCluster also identifies outliers rather than an allele-specific Trash group. In the selected solution, 94 peptides were classified as outliers.

Another important difference is that GibbsCluster first collapses the input to unique peptide sequences. In this analysis, 1072 unique sequences were read from the combined dataset. In contrast, MHCMotifDecon analysed the peptides separately according to their patient labels, allowing the same peptide to contribute independently to different patient-specific analyses.

Overall, MHCMotifDecon is better able to separate overlapping HLA motifs when the HLA genotype is known, whereas GibbsCluster attempts to discover motifs directly from the peptide sequences without prior HLA information.

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.