Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.

The availability of complete pathogen genomes has renewed interest in the development of diagnostics for infectious diseases. Synthetic peptide microarrays provide a rapid, high-throughput platform for immunological testing of potential B-cell epitopes. However, their current capacity prevent the ex...

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Autores principales:	Santiago J Carmona, Paula A Sartor, María S Leguizamón, Oscar E Campetella, Fernán Agüero
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2402880ab73446b7bf01a2e0ec2b0ec4
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spelling	oai:doaj.org-article:2402880ab73446b7bf01a2e0ec2b0ec42021-11-18T08:04:58ZDiagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.1932-620310.1371/journal.pone.0050748https://doaj.org/article/2402880ab73446b7bf01a2e0ec2b0ec42012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23272069/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The availability of complete pathogen genomes has renewed interest in the development of diagnostics for infectious diseases. Synthetic peptide microarrays provide a rapid, high-throughput platform for immunological testing of potential B-cell epitopes. However, their current capacity prevent the experimental screening of complete "peptidomes". Therefore, computational approaches for prediction and/or prioritization of diagnostically relevant peptides are required. In this work we describe a computational method to assess a defined set of molecular properties for each potential diagnostic target in a reference genome. Properties such as sub-cellular localization or expression level were evaluated for the whole protein. At a higher resolution (short peptides), we assessed a set of local properties, such as repetitive motifs, disorder (structured vs natively unstructured regions), trans-membrane spans, genetic polymorphisms (conserved vs. divergent regions), predicted B-cell epitopes, and sequence similarity against human proteins and other potential cross-reacting species (e.g. other pathogens endemic in overlapping geographical locations). A scoring function based on these different features was developed, and used to rank all peptides from a large eukaryotic pathogen proteome. We applied this method to the identification of candidate diagnostic peptides in the protozoan Trypanosoma cruzi, the causative agent of Chagas disease. We measured the performance of the method by analyzing the enrichment of validated antigens in the high-scoring top of the ranking. Based on this measure, our integrative method outperformed alternative prioritizations based on individual properties (such as B-cell epitope predictors alone). Using this method we ranked [Formula: see text]10 million 12-mer overlapping peptides derived from the complete T. cruzi proteome. Experimental screening of 190 high-scoring peptides allowed the identification of 37 novel epitopes with diagnostic potential, while none of the low scoring peptides showed significant reactivity. Many of the metrics employed are dependent on standard bioinformatic tools and data, so the method can be easily extended to other pathogen genomes.Santiago J CarmonaPaula A SartorMaría S LeguizamónOscar E CampetellaFernán AgüeroPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 12, p e50748 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Santiago J Carmona Paula A Sartor María S Leguizamón Oscar E Campetella Fernán Agüero Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.
description	The availability of complete pathogen genomes has renewed interest in the development of diagnostics for infectious diseases. Synthetic peptide microarrays provide a rapid, high-throughput platform for immunological testing of potential B-cell epitopes. However, their current capacity prevent the experimental screening of complete "peptidomes". Therefore, computational approaches for prediction and/or prioritization of diagnostically relevant peptides are required. In this work we describe a computational method to assess a defined set of molecular properties for each potential diagnostic target in a reference genome. Properties such as sub-cellular localization or expression level were evaluated for the whole protein. At a higher resolution (short peptides), we assessed a set of local properties, such as repetitive motifs, disorder (structured vs natively unstructured regions), trans-membrane spans, genetic polymorphisms (conserved vs. divergent regions), predicted B-cell epitopes, and sequence similarity against human proteins and other potential cross-reacting species (e.g. other pathogens endemic in overlapping geographical locations). A scoring function based on these different features was developed, and used to rank all peptides from a large eukaryotic pathogen proteome. We applied this method to the identification of candidate diagnostic peptides in the protozoan Trypanosoma cruzi, the causative agent of Chagas disease. We measured the performance of the method by analyzing the enrichment of validated antigens in the high-scoring top of the ranking. Based on this measure, our integrative method outperformed alternative prioritizations based on individual properties (such as B-cell epitope predictors alone). Using this method we ranked [Formula: see text]10 million 12-mer overlapping peptides derived from the complete T. cruzi proteome. Experimental screening of 190 high-scoring peptides allowed the identification of 37 novel epitopes with diagnostic potential, while none of the low scoring peptides showed significant reactivity. Many of the metrics employed are dependent on standard bioinformatic tools and data, so the method can be easily extended to other pathogen genomes.
format	article
author	Santiago J Carmona Paula A Sartor María S Leguizamón Oscar E Campetella Fernán Agüero
author_facet	Santiago J Carmona Paula A Sartor María S Leguizamón Oscar E Campetella Fernán Agüero
author_sort	Santiago J Carmona
title	Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.
title_short	Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.
title_full	Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.
title_fullStr	Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.
title_full_unstemmed	Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.
title_sort	diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/2402880ab73446b7bf01a2e0ec2b0ec4
work_keys_str_mv	AT santiagojcarmona diagnosticpeptidediscoveryprioritizationofpathogendiagnosticmarkersusingmultiplefeatures AT paulaasartor diagnosticpeptidediscoveryprioritizationofpathogendiagnosticmarkersusingmultiplefeatures AT mariasleguizamon diagnosticpeptidediscoveryprioritizationofpathogendiagnosticmarkersusingmultiplefeatures AT oscarecampetella diagnosticpeptidediscoveryprioritizationofpathogendiagnosticmarkersusingmultiplefeatures AT fernanaguero diagnosticpeptidediscoveryprioritizationofpathogendiagnosticmarkersusingmultiplefeatures
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Diagnostic peptide discovery: prioritization of pathogen diagnostic markers using multiple features.

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