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 (Brucellosis), Coxiella burnetii (Q fever),
Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia),
Leishmania braziliensis (Leishmaniasis), Leptospira interrogans
(Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis
(Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis
(Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus
pyogenes (Group A Streptococcal infections), Toxoplasma gondii
(Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). After training a
linear regression model the method achieves good to
excellent performance on most species, measured by the enrichment
of validated antigens at the top of the ranking. An unbiased validation
using independent data sets shows APRANK is successful in predicting
antigenicity for all pathogen species tested. We make APRANK available to
facilitate the identification of novel diagnostic antigens in infectious
diseases.