DOI: 10.5061/dryad.zcrjdfnb1
title: Regression models generated by APRANK (computational prioritization of antigenic proteins and peptides from complete pathogen proteomes)

description: 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.
description: A curated dataset of known / validated antigens was obtained from
 each of the 15 human pathogens listed (bacteria, eukaryotes). Other
 proteins encoded in these annotated genomes were considered non-antigenic
 or with no antigenicity precedence/information. Using these data a number
 of protein features were calculated or predicted using a bioionformaics
 pipeline (described in the manuscript). To create and train a
 generalized-linear model first we created 15 individual training sets (one
 per species) containing a set of 3000 proteins (with balanced positive and
 negative training examples). A merged training set containing data from
 all species was used to train the protein model. A similar approach was
 followed to create and train a model for peptides (epitopes). In this
 case, we created 15 individual training sets containing a balanced set of
 100,000 peptides.
description: These files contain: R data
 structures that can be fed into R. They contain generalized linear models
 derived from curated (validated) antigens from 15 different human
 pathogens. Most likely users of these files may want to use our APRANK
 software (Antigenic Protein and Peptide Ranker,
 https://github.com/trypanosomatics/aprank), which is a pipeline that would
 rank proteins and peptides from a complete proteome based on predicted
 antigenicity. APRANK  Antigenicity Scores for all 15
 organisms analyzed in this work. Scores are provided for all proteins in
 the proteomes of these 15 human pathogens, and for the top scoring
 peptides (score &gt;0.7, not less than 1% of the total
 peptides)