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Immunology Background
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PepTcells breakthrough technology enables identification of multiple T cell
epitope
s/
peptides
(small fragments of
protein
s) that will be recognised by the immune system, which are conserved in the relevant organism and will be presented on the surface of certain human cells. In a vaccine, these
epitope
s are presented to T cells by HLA molecules, activating a specific T cell population that is then primed to eliminate the disease or virus (or other pathogen) on infection. Importantly, the selected T cell population remains in the circulation in sufficient numbers to recognise and eliminate subsequent disease or virus or pathogen infections with new strains. PepTcell's novel technologies are underpinned by the extensive and detailed knowledge acquired over the last century on the organisation and processes that make up the immune system.
At the centre of PepTcell's breakthrough technology is a highly accurate model that predicts those
epitope
s of the internal low- or non-mutating
protein
s in viruses (and other pathogens or human diseases) against which the cellular immune system could induce “active” immunity. These are referred to as “binding & reactive”
epitope
s.
PepTcell's novel T cell
epitope
prediction model sequentially establishes:
All the
peptides
(
epitope
s) that are produced by degradation of the
protein
in the host cell;
Which of those
peptides
(
epitope
s) binds to the HLA
protein
;
Which of those
peptides
(
epitope
s) elicit a T cell response
The PepTcell model does not rely on statistical comparisons of past historic data in order to establish rules on binding and reactivity as other competitor models do. Instead PepTcell’s model was created by closely examining molecular interactions, molecular dynamics, crystallographic images and structural alignments and identifying 16 determinants that are relevant in ascertaining whether an
epitope
will be both binding and reactive. These determinants where then applied to historic data in order to ascertain differentiation levels.
PepTcell's algorithm has been extensively tested and cross-validated against available experimental data, and the results of this analysis have corroborated its accuracy. In all tests carried out to date it has proven superior to all its commercial and non-commercial competitors.
For example, when applied to Human Herpesvirus 8 Kaposin
protein
. PepTcell’s algorithm successfully identified the prime reactive peptide which was not identified by other models. Of equal relevance, PepTcell’s algorithm, when tested against a list of 360
peptides
known to bind HLA-A*0201, was able to corre
ctl
y identify all 106 non-reactive and 254 reactive
epitope
s without the need for any experimental procedure. This corresponds to an accuracy level of over 99.98% for the overall population.
PepTcell has also validated their model with a major pharmaceutical company and the very recent pre-clinical work on HIV and Influenza has further validated the model.
Current vaccine development relies on highly cost and labour intensive methods to identify immunoreactive
protein
s suitable for clinical trials. By removing the need to physically produce and test thousands of individual
protein
s, PepTcell’s approach eliminates the need for time consuming and expensive research.
Related Links
Downloads: Pathogens Peptides Cells MHC
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