Validating subcellular localization prediction tools with mycobacterial proteins

Daniel Restrepo-Montoya, Carolina Vizcaíno, Luis F. Niño, Marisol Ocampo, Manuel E. Patarroyo, Manuel A. Patarroyo

Resultado de la investigación: Contribución a RevistaArtículo

21 Citas (Scopus)

Resumen

Background: The computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins. Results: A final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out. Conclusion: Both subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model. © 2009 Restrepo-Montoya et al; licensee BioMed Central Ltd.
Idioma originalEnglish (US)
PublicaciónBMC Bioinformatics
DOI
EstadoPublished - may 7 2009

Huella dactilar

Proteins
Protein
Prediction
Protein Databases
Specificity
Correlation coefficient
Classifiers
Classifier
Bacterial Proteins
Metric
Predict
Vaccines
Proteome
Bioinformatics
Vaccine
Computational Biology
Biased
Annotation
Homology
Drugs

Citar esto

Restrepo-Montoya, Daniel ; Vizcaíno, Carolina ; Niño, Luis F. ; Ocampo, Marisol ; Patarroyo, Manuel E. ; Patarroyo, Manuel A. / Validating subcellular localization prediction tools with mycobacterial proteins. En: BMC Bioinformatics. 2009.
@article{13187070dfa64814a1c7fed32fb1b84e,
title = "Validating subcellular localization prediction tools with mycobacterial proteins",
abstract = "Background: The computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40{\%} identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins. Results: A final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out. Conclusion: Both subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model. {\circledC} 2009 Restrepo-Montoya et al; licensee BioMed Central Ltd.",
author = "Daniel Restrepo-Montoya and Carolina Vizca{\'i}no and Ni{\~n}o, {Luis F.} and Marisol Ocampo and Patarroyo, {Manuel E.} and Patarroyo, {Manuel A.}",
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month = "5",
day = "7",
doi = "10.1186/1471-2105-10-134",
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Validating subcellular localization prediction tools with mycobacterial proteins. / Restrepo-Montoya, Daniel; Vizcaíno, Carolina; Niño, Luis F.; Ocampo, Marisol; Patarroyo, Manuel E.; Patarroyo, Manuel A.

En: BMC Bioinformatics, 07.05.2009.

Resultado de la investigación: Contribución a RevistaArtículo

TY - JOUR

T1 - Validating subcellular localization prediction tools with mycobacterial proteins

AU - Restrepo-Montoya, Daniel

AU - Vizcaíno, Carolina

AU - Niño, Luis F.

AU - Ocampo, Marisol

AU - Patarroyo, Manuel E.

AU - Patarroyo, Manuel A.

PY - 2009/5/7

Y1 - 2009/5/7

N2 - Background: The computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins. Results: A final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out. Conclusion: Both subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model. © 2009 Restrepo-Montoya et al; licensee BioMed Central Ltd.

AB - Background: The computational prediction of mycobacterial proteins' subcellular localization is of key importance for proteome annotation and for the identification of new drug targets and vaccine candidates. Several subcellular localization classifiers have been developed over the past few years, which have comprised both general localization and feature-based classifiers. Here, we have validated the ability of different bioinformatics approaches, through the use of SignalP 2.0, TatP 1.0, LipoP 1.0, Phobius, PA-SUB 2.5, PSORTb v.2.0.4 and Gpos-PLoc, to predict secreted bacterial proteins. These computational tools were compared in terms of sensitivity, specificity and Matthew's correlation coefficient (MCC) using a set of mycobacterial proteins having less than 40% identity, none of which are included in the training data sets of the validated tools and whose subcellular localization have been experimentally confirmed. These proteins belong to the TBpred training data set, a computational tool specifically designed to predict mycobacterial proteins. Results: A final validation set of 272 mycobacterial proteins was obtained from the initial set of 852 mycobacterial proteins. According to the results of the validation metrics, all tools presented specificity above 0.90, while dispersion sensitivity and MCC values were above 0.22. PA-SUB 2.5 presented the highest values; however, these results might be biased due to the methodology used by this tool. PSORTb v.2.0.4 left 56 proteins out of the classification, while Gpos-PLoc left just one protein out. Conclusion: Both subcellular localization approaches had high predictive specificity and high recognition of true negatives for the tested data set. Among those tools whose predictions are not based on homology searches against SWISS-PROT, Gpos-PLoc was the general localization tool with the best predictive performance, while SignalP 2.0 was the best tool among the ones using a feature-based approach. Even though PA-SUB 2.5 presented the highest metrics, it should be taken into account that this tool was trained using all proteins reported in SWISS-PROT, which includes the protein set tested in this study, either as a BLAST search or as a training model. © 2009 Restrepo-Montoya et al; licensee BioMed Central Ltd.

U2 - 10.1186/1471-2105-10-134

DO - 10.1186/1471-2105-10-134

M3 - Article

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

ER -