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Introduction
You can find detailed instructions on how to operate the EpiQuest-B program and utilize its settings in the Program's Manual. The Demo version of the program provides several demo sequences for analysis, allowing you to learn how to read and interpret the results and understand potential difficulties you may encounter while using the program.
The established B-epitopes for Demo sequences can be accessed from the IEDB collection, along with references to the original research articles (unless specific data references are provided). It's important to note that we adhere to the definitions of high, intermediate, and low antigenicity epitopes as described in IEDB.
Ro ribonucleoprotein
Below is the histogram illustrating the distribution of antigenicity for linear sequences of the Ro protein. The known epitopes, regardless of their strength, are highlighted by red frames, while the predicted epitopes are represented as peaks in the histogram and red bars below the sequences. To view the full-size image, please click on the picture.
It's evident that the program correctly predicts almost all epitopes of Ro. However, some of the predicted epitopes should be excluded based on their lack of exposure at the molecule's surface, as indicated by the accessibility analysis (blue bars showing the exposed regions).
These are the epitopes predicted in Ro sequence in tabular from. The hidden sequences are indicated (non-exposed). The sequences of known epitopes are indicated in red within the sequences of the predicted ones.
Alpha-S2 Casein
Analysis of the Alpha-S2 casein sequence predicts several epitopes of varying antigenicity. The best one contains the sequence of the established immunodominant epitope in this protein, although the program shows a wider area than the established epitope.
To isolate the most antigenic area, you can increase the cut-off threshold of the program (T), thereby obtaining shorter epitopes.
You may also try changing the settings to even higher values of T (2, 3) and observe the changes in the histogram and tabular results. Increasing the threshold will likely result in shorter predicted epitopes, potentially isolating the most antigenic regions more precisely.
To zoom, please click on the image.
Major pollen allergen Jun a1
Sometimes, the presence of highly non-antigenic sequences immediately following the epitope can cause a shift in the detected epitope position due to the influence of the strongly negative context.
This effect is demonstrated in the presented example for the analysis of the Jun a1 sequence, where the dominant epitope is predicted correctly, but a part of the actual epitope sequence is excluded from the predicted one. Therefore, we recommend running the analysis with Frame 3, 6, and 9 to observe whether the context has an influence on the position of the epitope. In most cases, the position will remain the same; however, when you observe the effect of a shifting epitope, it's better to include all sequences observed at different frame sizes into the selected epitopes.
Outer membrane protein P5 (Fimbrin)
This example highlights that sometimes the predicted sequence may be represented by two fused epitopes. To better discriminate between them, we recommend raising the threshold. It's worth noting that we do not discuss here why one of the two main predicted epitopes is not immunogenic in the actual immune response to the molecule (this can be explored further along with accessibility analysis). Additionally, it should be mentioned that the known epitope in this case is not the actual sequence but a wider area established during screening of sera with overlapping peptides.
Alpha/beta-gliadin A-V (Prolamin)
Here we demonstrate how several overlapping linear epitopes may be reflected in the histogram obtained with EpiQuest-B. While the immunodominant linear sequence of Prolamin is predicted correctly, one peak may encompass several epitopes. This phenomenon illustrates the potential overlap and complexity of epitope recognition within protein sequences.
We have presented the analysis of several cases of demo sequences, illustrating the accuracy of the prediction and how the results are affected by the settings for analysis. For more examples, visit our blog.
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