A new computational approach has been developed for predicting and prioritizing neoantigens, peptides that arise during tumor transformation and are promising candidates for creating anti-cancer vaccines. The IIS indicator is an integral indicator of immunogenicity that combines two key parameters: the affinity of the peptide to MHC I and its ability to elicit an immune response. The developed modular system, implemented in Python, uses an efficient computing strategy that includes a preliminary calculation of immunogenicity to reduce computational load. The platform supports analysis for both individual HLA alleles and population data, which allows it to be used to develop both personalized and universal vaccines. The results of the work include a ranked list of candidates with verification against the IEAtlas and IEDb databases. The system is available in an open repository on GitHub and represents a significant step forward in identifying targets for cancer immunotherapy.
Bezdvornykh et al. (Thu,) studied this question.