Exercise:AntigenProcessing ans: Difference between revisions

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*'''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?'''
=== Answers ===


'''Answer:''' Record the peptide numbers shown in the MHCMotifDecon output for:
*'''Q6: How many peptides were assigned to each of the alleles, and how many were assigned to the trash cluster?'''


DRB1_0101 = [number from your run]
'''Answer:'''
DRB1_0401 = [number from your run]
Trash      = [number from your run]


'''Explanation:''' These numbers should be taken directly from the analysis rather than hard-coded because they depend on the exact input peptide list, filtering and chosen %Rank threshold.
{| class="wikitable"
! Patient
! HLA allele 1
! Peptides
! HLA allele 2
! Peptides


MHCMotifDecon predicts the binding of every peptide to each supplied HLA molecule and assigns the peptide to its most likely restriction element. With the default settings, peptides that have a predicted '''%Rank greater than 20 for all supplied HLA molecules''' are assigned to the '''trash cluster'''.
| ! Trash  |
| --------- |
| RA1      |
| DRB1_0402 |
| 141      |
| DRB1_1104 |
| 71        |
| 18        |
| -        |
| RA2      |
| DRB1_0101 |
| 193      |
| DRB1_0401 |
| 245      |
| 52        |
| -        |
| RA3      |
| DRB1_0101 |
| 124      |
| DRB1_0401 |
| 301      |
| 11        |
| -        |
| RA4      |
| DRB1_0401 |
| 51        |
| DRB1_1501 |
| 16        |
| 3        |
| -        |
| RA5      |
| DRB1_0801 |
| 101      |
| DRB1_1501 |
| 31        |
| 7        |
| }        |


Therefore, the trash cluster is not a third HLA allele. It represents peptides for which none of the supplied HLA molecules provides a sufficiently convincing predicted binding interaction.
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.'''
*'''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 a particular HLA allele usually form a more recognizable '''HLA-binding motif''', whereas the trash peptides tend to show a much weaker or less coherent sequence pattern.
'''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.


'''Explanation:''' HLA-DR molecules bind peptides through a characteristic peptide-binding core. Certain amino acids are preferred at particular anchor positions, especially within the approximately nine-residue binding core. When many peptides presented by the same HLA molecule are aligned, these preferences produce a recognizable sequence logo.
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.


The trash cluster contains peptides that are poorly predicted to bind any of the HLA molecules provided to MHCMotifDecon. Consequently, they often:
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.
 
* lack a clear HLA-binding motif;
* have weaker predicted binding scores;
* contain more heterogeneous sequences; and
* may include co-immunoprecipitated contaminants or peptides presented by an HLA molecule that was not included in the analysis.
 
The trash cluster is therefore useful: instead of forcing every peptide into one of the specified HLA motifs, MHCMotifDecon can separate sequences that do not fit the proposed HLA repertoire.


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


'''Answer:''' Changing the %Rank threshold changes how stringent MHCMotifDecon is when deciding whether a peptide should be assigned to an HLA molecule or placed in the trash cluster.
'''Answer:''' The %Rank threshold determines how strong the predicted interaction must be for a peptide to be assigned to an HLA molecule.
 
'''Explanation:'''
 
A '''lower %Rank threshold''' is more stringent:
 
* fewer peptides are accepted as HLA binders;
* more peptides enter the trash cluster; and
* the resulting HLA motifs are generally cleaner, but some real ligands may be discarded.
 
A '''higher %Rank threshold''' is less stringent:
 
* more peptides are assigned to HLA alleles;
* fewer peptides enter the trash cluster; and
* weaker predicted binders and potentially contaminating peptides may be included.


Remember that for %Rank, '''smaller values indicate stronger predicted binding'''. The default MHCMotifDecon trash threshold for MHC class II is 20%.
The current analysis uses:


The exercise therefore illustrates the trade-off between '''sensitivity''' and '''specificity''': very stringent filtering produces cleaner assignments but risks losing true ligands, whereas permissive filtering retains more peptides but may introduce noise.
%Rank threshold = 20


*'''Q9: How many peptides are now associated with HLA-DRB4? Where did they come from?'''
Lowering the threshold makes the assignment '''more stringent''':


'''Answer:''' The exact DRB4 peptide count should be recorded from the second MHCMotifDecon run:
lower %Rank threshold → fewer peptides assigned to HLA → more peptides assigned to Trash


DRB4_0101 = [number from your run]
Increasing the threshold makes the assignment '''more permissive''':


The important observation is that adding '''DRB4_0101''' causes a subset of peptides to be reassigned to DRB4.
higher %Rank threshold → more peptides assigned to HLA → fewer peptides assigned to Trash


'''Where did they come from?''' They generally come from peptides that, in the first analysis, had been:
A more stringent threshold may produce cleaner motifs, but it may also exclude genuine HLA ligands.


* assigned to one of the DRB1 molecules, particularly because DRB4 was not available as an alternative; or
*'''Q9: How many peptides are now associated with HLA-DRB4? Where did these peptides come from?'''
* placed in the trash cluster because neither DRB1*01:01 nor DRB1*04:01 provided a sufficiently good prediction.


'''Explanation:''' HLA-DRB1*04:01 is commonly linked with expression of '''HLA-DRB4*01:01'''. If DRB4 is genuinely expressed but is omitted from the HLA typing supplied to the algorithm, MHCMotifDecon has no opportunity to assign peptides to it.
'''Answer:''' This cannot yet be determined from the current analysis because '''DRB4_0101 was not included among the HLA alleles in this run'''.


This demonstrates an important principle of supervised motif deconvolution:
To answer this question, repeat the analysis after adding:


'''The quality of the deconvolution depends on the completeness of the HLA typing supplied to the method.'''
DRB4_0101


Once DRB4 is included, peptides fitting its binding specificity can form a separate motif rather than being incorrectly associated with another allele or classified as trash.
to the relevant patients expressing '''DRB1*04:01''' (RA2, RA3 and RA4).


DRB4 normally contributes a smaller peptide repertoire than the accompanying major DRB1 molecule, which also helps explain why its motif can be harder to identify with an unsupervised method.
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 ===
=== GibbsCluster ===

Revision as of 21:14, 22 September 2026

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?

Answers

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

Answer:

Patient HLA allele 1 Peptides HLA allele 2 Peptides

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.

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