Bioinformatic Approach for Identifying Parasite and Fungal Candidate Subunit Vaccines

In silico genome analysis enables systematic identification of potential antigens within a pathogen. Of primary importance is the accuracy of computer algorithms used for antigen prediction. Most bioinformatics tools are based on sequence alignment and are not able to predict truly novel antigenic proteins which lack similarity to existing antigens or which encode antigenicity in a cryptic manner. To surmount such obstacles, we have recently developed an alignment-free approach to in silico antigen identification, based on the auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Here, we apply this approach to finding parasite and fungal immunoprotective antigens. The models derived in this study demonstrate good predictive ability with 78% to 97% accuracy under internal cross validation in 7 groups. Under external validation, they gave 69% sensitivity ranking the true immunoprotective proteins in the first 25% of their proteomes.