Abstract
Availability of highly parallelized immunoassays has renewed interest in the discovery of serology biomarkers for infectious diseases. Protein and peptide microarrays now provide a rapid, high-throughput platform for immunological testing and validation of potential antigens and B-cell epitopes. However, there is still a need for tools to prioritize and select 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 potentially antigenic proteins and peptides in 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 trained and tested this method with a number of bacteria and protozoa causing 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). We have evaluated this integrative method using non-parametric ROC-curves and made an unbiased validation using Onchocerca volvulus as an independent data set. We found that APRANK is successful in predicting antigenicity for all pathogen species tested, facilitating the production of antigen-enriched protein subsets. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.
Highlights
Infectious diseases are one of the first causes of death worldwide, disproportionately affecting poor and young people in developing countries
We have previously shown for Trypanosoma cruzi (Chagas Disease) that different criteria can be integrated and exploited in a computational strategy to further guide the process of diagnostic peptide discovery [11]
We did not include viruses in this version of APRANK because they have small proteomes that are already amenable to experimental experimentation
Summary
Infectious diseases are one of the first causes of death worldwide, disproportionately affecting poor and young people in developing countries. One of the preferred methods to diagnose infections relies on the detection of pathogen-specific antibodies in the fluids of infected patients (most often serum obtained from blood) [2, 3] For this reason, there is a big interest in developing reliable methods able to improve the fast and sensitive identification of potential specific antigens. Taking advantage of complete genome sequences from pathogens, it is theoretically possible to scan every encoded protein with short peptides against sera from infected hosts While this is straightforwardly achieved for viral pathogens and small bacteria, it gets more difficult when dealing with larger bacteria or eukaryotic parasites, since they can reach thousands of proteins with millions of peptides, exceeding the average capacity of standard protein or peptide microarrays [5]. One example are serological strain typing strategies [6], which would stress the capacity of these platforms
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