Polymorphic Epitope Prediction
Polymorphic Epitope Prediction is based on SNEP  and extends Epitope Prediction by incorporating variant information. From these variants neoantigens are constructed which enables the discovery of neoepitopes that are influenced by the used variant information. These neoepitopes play an important role in cancer immunotherapy since they usually are novel peptide sequences that can only be found the tumor cells. Therefore, these epitopes represent promising targets for personalized cancer vaccines.
Polymorphic Epitope Prediction is primarily designed for human data and variant analysis based on HG19 as reference.
In the following the different configuration steps of Polymorphic Epitope Prediction are described.
Step 1: Data Input
In the first step you specify the variants from which neoepitopes should be generated. You have two options as input:
If protein IDs are used all known variants annotated in dbSNP  are extracted and used for neoepitope generation.
The variants are than annotated with ANNOVAR  and all possible neoepitopes are generated based on the extracted variants.
You also have to specify the required length of the epitopes [8-16 AA]. Depending on the selected length the available prediction methods are filtered.
Additionally, you can specify an HLA Allele file from History. The Allele file contains HLA alleles in new nomenclature up to a detail level of 4-digits. The so specified alleles are used for predictions, if the selected prediction model supports them.
Polymorphic Epitope Prediction supports single nucleotide variations, insertion, deletions, and frame shifts. You can specify what type variations should be considered during the analysis by checking the corresponding checkboxes under Filter Variant Types.
Step 2: Prediction Methods
In the second step the prediction methods to be used are selected. Multiple methods can be used at the same time, but at least one prediction method has to be selected. The following prediction methods are available:
SYFPEITHI  are position-specific scoring matrices (PSSMs) that were designed based on expert knowledge and amino acid occurrences in naturally processed HLA ligands.
BIMAS  uses PSSMs derived from experimentally determined binding affinities measured as dissociation rates of the peptide:HLA:β2-microglobulin complex relative to a reference peptide.
SVMHC  is a SVM based classification method that was trained experimentally validated epitopes from the SYFPEITHI database and random generated peptides.
NetMHC family [6-9] comprises of NetMHC, NetMHCpan, NetMHCII, and NetMHCIIpan, which are all artificial neural network based regression methods. Furthermore, NetMHCpan and NetMHCIIpan incorporate structural information of the HLA-binding pockets to allow prediction for HLA alleles with insufficient data.
UniTope  is a SVM based prediction method that also combines structural information of the HLA binding groove with epitope sequences. In comparison to NetMHC(II)pan, the peptides are encoded using physicochemical properties.
TEPITOPEpan  uses PSSMs for epitope prediction and is based on Sturniolo et al’s virtual binding pocket approach. To allow predictions for alleles that originally were not covered by Sturniolo et al. TEPITOPEpan uses a phylogenetic-based weighting approach to reconstruct the allele-specific PSSM from the original matrices.
Step 3: HLA selection
In this step the alleles for which predictions should be performed have to be selected. A tree is generated based on the supported alleles of the previous selected prediction methods (Figure 2). Only the shared HLA alleles are displayed if multiple prediction methods were selected. If an HLA Allele file was specified the supported alleles are filter based on the contained alleles in the Allele file.
Figure 2: HLA allele tree for SYFPEITHI
By checking higher levels of the tree all HLA alleles of the lower levels are selected as well. If no HLA-Tree is generated or your favorite HLA allele is nowhere to be found, please click back and select a different prediction method.
Step 4: Results
Two outputs are generated. The first output is an internal representation of the predictions that can be directly used as input for Epitope Selection. The second output is a detailed and interactive html output of the prediction results.
Figure 3. Example result page.
The results are presented in a sortable and searchable table. Each row represents one prediction result of an epitope and a prediction method. The results can be exported in either CSV of Excel format by clicking Save and selecting the desired format. By clicking Print, the table is completely extended to be able to use the Browser print functionality. To return to the normal view hit ESC.
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