Spacer Design for Epitope Assembly is concerned with determining the optimal amino acid composition and length of a small sequence connecting two epitopes within a string-of-beads poly-peptide and the epitope order to maximizes the probability that the epitopes will be fully recovered after proteasomal cleavage. This is an important step for vaccine design and has potentially high impact on the efficacy of the designed vaccine. Epitope Assembly formulates the epitope ordering problem as a traveling salesperson problem, where epitopes represent the cities to visit and the distances between the cities represent the recovery probabilities.
The configuration steps of Spacer Design are explained in the following:
Epitope Assembly supports two types of input:
Files can be uploaded with the (Figure 1 (1.)) Upload File tool or the (Figure 1 (2.)) jQuery Upload tool. Figure 1. Upload possibilities. 1. Upload tool. 2. jQuery Upload tool.
After specifying the epitope input, you have to choose which proteasomal cleavage and epitope prediction method should be used during optimization. Currently, Spacer Design supports two proteasomal cleavage site prediction methods:
For epitope prediction, we currently support:
In addition to the prediction methods, you have to specify a binding threshold for the chosen epitope prediction method to distinguish immunogenic peptides from non-immunogenic peptides. This is dependent on the selected prediction method.
Peptide or protein sequences containing non-standard amino acids are not considered in epitope prediction.
In the Advanced Options you can specify the max. length of the spacer sequences. But be aware that the optimal length is automatically determined within the specified boundaries. Furthermore, you can influence how much of the max obtainable cleavage score should be retained when minimizing neo-immunogenicity by changing the Alpha parameter [0,1].
If you want to minimize non-junction cleavage sites as well you can specify so und change the Beta [0,1] parameter accordingly. Beta, similar to Alpha, specifies how strongly the minimal obtainable neo-immunogenicity has to be fulfilled from the next optimization in order to achieve a smaller non-junction cleavage score. For example a Beta value of 0.99 would mean that the next solution is only allowed to have a 1% higher neo-immunogenicity objective as the score obtained during neo-immunogenicity optimization.
In the second step you have to specify the target population. This can be selected based on geographic region, pre-defined population, or manually.
The HLA alleles are filtered based on the selected prediction method, the (optionally) entered HLA Allele File, and the HLA allele specified in the Prediction Table.
In this step you have to select the annotated HLA alleles. A HLA-Tree is generated based on the previous configurations (Figure 2).
Figure 2: Example HLA allele tree
By checking higher levels of the tree all HLA alleles of the lower levels are selected as well. If you select HLA-A for example, prediction will be made for all HLA-A alleles that are supported by the selected prediction methods. If no HLA-Tree is generated or your favorite HLA allele is nowhere to be found, please select a different prediction method.
Two outputs are generated. The first output is an internal representation of the assembly. The second output is an interactive html output of the assembly results.
It summarizes the configuration and shows the cleavage scores for string-of-beads construct and epitope predictions for the specified HLA alleles (Figure 3). You can download the cleavage prediction table by clicking Save and selecting either CSV or XLS as output format. By clicking Print, the table is completely extended to be able to use the Browser print function. To return to the normal view hit ESC.
Additionally, the output provides the finished string-of-beads construct with their optimized spacer sequences oriented from N- to C-terminus (Figure 3).