Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)

Availability of highly parallelized immunoassays has renewed interest in the discovery of serology-based biomarkers for infectious diseases. Protein and peptide microarrays now provide a high-throughput platform for immunological screening of potential antigens and B-cell epitopes. However, there is still a need to prioritize relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize antigenic targets starting from a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We applied this method to the prioritization of potential diagnostic antigens and peptides in a number of pathogen proteomes and human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucel...