IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method.

ABSTRACT: In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, 'IntegralVac,' by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing d

SUBMITTER: Suri S 

PROVIDER: S-EPMC9611587 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature