TCED™ & iTope-AI

iTope-AI is a proprietary in silico tool which models the binding between amino acid side chains of a peptide and specific binding pockets within the binding grooves of 46 human MHC class II alleles (DR, DP and DQ) with an optional human proteome filter. Levels of false negative / false positive rates have been reduced using machine learning prediction algorithms.

The contribution of individual amino acids to peptide binding can be determined for each allele, and this provides information on the precise location of the core 9mer sequences that interact with the MHC class II binding groove. iTope-AI provides a useful first step in evaluating the immunogenic potential of antibodies and proteins but, similarly to other in silico binding algorithms, iTope-AI significantly over-predicts the presence of CD4+ T cell epitopes and is thus best used prior to immunogenicity screening using EpiScreen® 2.0.

For immunogenic amino acid sequences already identified using T cell epitope mapping, iTope-AI determines the core 9mer sequences that can be used to design non-antigenic sequence variants in which T cell epitopes are mutated to disrupt MHC Class II binding.

Data from EpiScreen® 2.0 T cell epitope mapping studies conducted over a decade have enabled us to develop a database of CD4+ T cell epitopes. Sequences from antibodies and proteins that are candidates for development as therapeutic agents can be analyzed for homology to these known T cell epitopes, enabling the rapid and accurate in silico identification of their potential to elicit an immunogenic response.

The combined TCED™/iTope-AI technology is typically used for rapid analysis of multiple sequences (e.g., from therapeutic antibody candidates) in order to identify a strong lead sequence with a reduced risk of immunogenicity.

iTope-AI is also available for MHC class I. This has utility for epitope identification for vaccine design.