NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database.

Background: Recombinant DNA technology has been extensively employed to generate a variety of products from genetically modified organisms (GMOs) over the last decade, and the development of technologies capable of analyzing these products is crucial to understanding gene expression patterns. Liquid chromatography coupled with mass spectrometry is a powerful tool for analyzing protein contents and possible expression modifications in GMOs. Specifically, the NanoUPLC-MSE technique provides rapid protein analyses of complex mixtures with supported steps for high sample throughput, identification and quantization using low sample quantities with outstanding repeatability. Here, we present an assessment of the peptide and protein identification and quantification of soybean seed EMBRAPA BR16 cultivar contents using NanoUPLC-MSE and provide a comparison to the theoretical tryptic digestion of soybean sequences from Uniprot database. Results: The NanoUPLC-MSE peptide analysis resulted in 3,400 identified peptides, 58% of which were identified to have no miscleavages. The experiment revealed that 13% of the peptides underwent in-source fragmentation, and 82% of the peptides were identified with a mass measurement accuracy of less than 5 ppm. More than 75% of the identified proteins have at least 10 matched peptides, 88% of the identified proteins have greater than 30% of coverage, and 87% of the identified proteins occur in all four replicates. 78% of the identified proteins correspond to all glycinin and betaconglycinin chains. The theoretical Uniprot peptide database has 723,749 entries, and 548,336 peptides have molecular weights of greater than 500 Da. Seed proteins represent 0.86% of the protein database entries. At the peptide level, trypsin-digested seed proteins represent only 0.3% of the theoretical Uniprot peptide database. A total of 22% of all database peptides have a pI value of less than 5, and 25% of them have a pI value between 5 and 8. Based on the detection range of typical NanoUPLC-MSE experiments, i.e., 500 to 5000 Da, 64 proteins will not be identified. Conclusions: NanoUPLC-MSE experiments provide good protein coverage within a peptide error of 5 ppm and a wide MW detection range from 500 to 5000 Da. A second digestion enzyme should be used depending on the tissue or proteins to be analyzed. In the case of seed tissue, trypsin protein digestion results offer good databank coverage. The Uniprot database has many duplicate entries that may result in false protein homolog associations when using NanoUPLC-MSE analysis. The proteomic profile of the EMBRAPA BR-16 seed lacks certain described proteins relative to the profiles of transgenic soybeans reported in other works.

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Main Authors: MURAD, A. M., RECH FILHO, E. L.
Other Authors: ANDRE MELRO MURAD, CENARGEN; ELIBIO LEOPOLDO RECH FILHO, CENARGEN.
Format: Separatas biblioteca
Language:pt_BR
por
Published: 2013-03-07
Subjects:Soybean, Seed proteomics, NanoUPLC-MSE, Uniprot database., Soja.,
Online Access:http://www.alice.cnptia.embrapa.br/alice/handle/doc/952421
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spelling dig-alice-doc-9524212017-08-15T23:52:38Z NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database. MURAD, A. M. RECH FILHO, E. L. ANDRE MELRO MURAD, CENARGEN; ELIBIO LEOPOLDO RECH FILHO, CENARGEN. Soybean Seed proteomics NanoUPLC-MSE Uniprot database. Soja. Background: Recombinant DNA technology has been extensively employed to generate a variety of products from genetically modified organisms (GMOs) over the last decade, and the development of technologies capable of analyzing these products is crucial to understanding gene expression patterns. Liquid chromatography coupled with mass spectrometry is a powerful tool for analyzing protein contents and possible expression modifications in GMOs. Specifically, the NanoUPLC-MSE technique provides rapid protein analyses of complex mixtures with supported steps for high sample throughput, identification and quantization using low sample quantities with outstanding repeatability. Here, we present an assessment of the peptide and protein identification and quantification of soybean seed EMBRAPA BR16 cultivar contents using NanoUPLC-MSE and provide a comparison to the theoretical tryptic digestion of soybean sequences from Uniprot database. Results: The NanoUPLC-MSE peptide analysis resulted in 3,400 identified peptides, 58% of which were identified to have no miscleavages. The experiment revealed that 13% of the peptides underwent in-source fragmentation, and 82% of the peptides were identified with a mass measurement accuracy of less than 5 ppm. More than 75% of the identified proteins have at least 10 matched peptides, 88% of the identified proteins have greater than 30% of coverage, and 87% of the identified proteins occur in all four replicates. 78% of the identified proteins correspond to all glycinin and betaconglycinin chains. The theoretical Uniprot peptide database has 723,749 entries, and 548,336 peptides have molecular weights of greater than 500 Da. Seed proteins represent 0.86% of the protein database entries. At the peptide level, trypsin-digested seed proteins represent only 0.3% of the theoretical Uniprot peptide database. A total of 22% of all database peptides have a pI value of less than 5, and 25% of them have a pI value between 5 and 8. Based on the detection range of typical NanoUPLC-MSE experiments, i.e., 500 to 5000 Da, 64 proteins will not be identified. Conclusions: NanoUPLC-MSE experiments provide good protein coverage within a peptide error of 5 ppm and a wide MW detection range from 500 to 5000 Da. A second digestion enzyme should be used depending on the tissue or proteins to be analyzed. In the case of seed tissue, trypsin protein digestion results offer good databank coverage. The Uniprot database has many duplicate entries that may result in false protein homolog associations when using NanoUPLC-MSE analysis. The proteomic profile of the EMBRAPA BR-16 seed lacks certain described proteins relative to the profiles of transgenic soybeans reported in other works. 2013-03-07T11:11:11Z 2013-03-07T11:11:11Z 2013-03-07 2012 2018-06-29T11:11:11Z Separatas BMC Biotechnology, v. 12, n. 82, 2012. http://www.alice.cnptia.embrapa.br/alice/handle/doc/952421 pt_BR por openAccess
institution EMBRAPA
collection DSpace
country Brasil
countrycode BR
component Bibliográfico
access En linea
databasecode dig-alice
tag biblioteca
region America del Sur
libraryname Sistema de bibliotecas de EMBRAPA
language pt_BR
por
topic Soybean
Seed proteomics
NanoUPLC-MSE
Uniprot database.
Soja.
Soybean
Seed proteomics
NanoUPLC-MSE
Uniprot database.
Soja.
spellingShingle Soybean
Seed proteomics
NanoUPLC-MSE
Uniprot database.
Soja.
Soybean
Seed proteomics
NanoUPLC-MSE
Uniprot database.
Soja.
MURAD, A. M.
RECH FILHO, E. L.
NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database.
description Background: Recombinant DNA technology has been extensively employed to generate a variety of products from genetically modified organisms (GMOs) over the last decade, and the development of technologies capable of analyzing these products is crucial to understanding gene expression patterns. Liquid chromatography coupled with mass spectrometry is a powerful tool for analyzing protein contents and possible expression modifications in GMOs. Specifically, the NanoUPLC-MSE technique provides rapid protein analyses of complex mixtures with supported steps for high sample throughput, identification and quantization using low sample quantities with outstanding repeatability. Here, we present an assessment of the peptide and protein identification and quantification of soybean seed EMBRAPA BR16 cultivar contents using NanoUPLC-MSE and provide a comparison to the theoretical tryptic digestion of soybean sequences from Uniprot database. Results: The NanoUPLC-MSE peptide analysis resulted in 3,400 identified peptides, 58% of which were identified to have no miscleavages. The experiment revealed that 13% of the peptides underwent in-source fragmentation, and 82% of the peptides were identified with a mass measurement accuracy of less than 5 ppm. More than 75% of the identified proteins have at least 10 matched peptides, 88% of the identified proteins have greater than 30% of coverage, and 87% of the identified proteins occur in all four replicates. 78% of the identified proteins correspond to all glycinin and betaconglycinin chains. The theoretical Uniprot peptide database has 723,749 entries, and 548,336 peptides have molecular weights of greater than 500 Da. Seed proteins represent 0.86% of the protein database entries. At the peptide level, trypsin-digested seed proteins represent only 0.3% of the theoretical Uniprot peptide database. A total of 22% of all database peptides have a pI value of less than 5, and 25% of them have a pI value between 5 and 8. Based on the detection range of typical NanoUPLC-MSE experiments, i.e., 500 to 5000 Da, 64 proteins will not be identified. Conclusions: NanoUPLC-MSE experiments provide good protein coverage within a peptide error of 5 ppm and a wide MW detection range from 500 to 5000 Da. A second digestion enzyme should be used depending on the tissue or proteins to be analyzed. In the case of seed tissue, trypsin protein digestion results offer good databank coverage. The Uniprot database has many duplicate entries that may result in false protein homolog associations when using NanoUPLC-MSE analysis. The proteomic profile of the EMBRAPA BR-16 seed lacks certain described proteins relative to the profiles of transgenic soybeans reported in other works.
author2 ANDRE MELRO MURAD, CENARGEN; ELIBIO LEOPOLDO RECH FILHO, CENARGEN.
author_facet ANDRE MELRO MURAD, CENARGEN; ELIBIO LEOPOLDO RECH FILHO, CENARGEN.
MURAD, A. M.
RECH FILHO, E. L.
format Separatas
topic_facet Soybean
Seed proteomics
NanoUPLC-MSE
Uniprot database.
Soja.
author MURAD, A. M.
RECH FILHO, E. L.
author_sort MURAD, A. M.
title NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database.
title_short NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database.
title_full NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database.
title_fullStr NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database.
title_full_unstemmed NanoUPLC-MSE proteomic data assessment of soybean seeds using the Uniprot database.
title_sort nanouplc-mse proteomic data assessment of soybean seeds using the uniprot database.
publishDate 2013-03-07
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/952421
work_keys_str_mv AT muradam nanouplcmseproteomicdataassessmentofsoybeanseedsusingtheuniprotdatabase
AT rechfilhoel nanouplcmseproteomicdataassessmentofsoybeanseedsusingtheuniprotdatabase
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