NDLI: Quantitative shotgun proteomics using a protease with broad specificity and normalized spectral abundance factors

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Contents

Hot off the PressHot off the RSC Press ( Molecular BioSystems Vol - 3 Issue - 5 )

Visualizing RNA splicing in vivo

Proteomic approaches to studying protein tyrosine phosphatases

Mammalian antimicrobial proteins and peptides: overview on the RNase A superfamily members involved in innate host defence

Analytical nanobiotechnology for medicine diagnostics

A synthetic biology challenge: making cells compute

Quantitative shotgun proteomics using a protease with broad specificity and normalized spectral abundance factors

Back matter

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Molecular BioSystems Vol - 3 Issue - 4

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Molecular BioSystems Vol - 3 Issue - 2

Molecular BioSystems Vol -2

Quantitative shotgun proteomics using a protease with broad specificity and normalized spectral abundance factors

Content Provider	Royal Society of Chemistry (RSC)
Author	Florens, Laurence Washburn, Michael P. Zybailov, Boris L.
Copyright Year	2007
Abstract	Non-specific proteases are rarely used in quantitative shotgun proteomics due to potentially high false discovery rates. Yet, there are instances when application of a non-specific protease is desirable to obtain sufficient sequence coverage of otherwise poorly accessible proteins or structural domains. Using the non-specific protease, proteinase K, we analyzed Saccharomyces cerevisiae preparations grown in 14N rich media and 15N minimal media and obtained relative quantitation from the dataset using normalized spectral abundance factors (NSAFs). A critical step in using a spectral counting based approach for quantitative proteomics is ensuring the inclusion of high quality spectra in the dataset. One way to do this is to minimize the false discovery rate, which can be accomplished by applying different filters to a searched dataset. Natural log transformation of proteinase K derived NSAF values followed a normal distribution and allowed for statistical analysis by the t-test. Using this approach, we generated a dataset of 719 unique proteins found in each of the three independent biological replicates, of which 84 showed a statistically significant difference in expression levels between the two growth conditions.
Starting Page	354
Ending Page	360
Page Count	7
File Format	HTM / HTML PDF
ISSN	1742206X
Volume Number	3
Issue Number	5
Journal	Molecular BioSystems
DOI	10.1039/b701483j
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Interactive media Quantitative proteomics Protease Normal distribution Proteomics Proteinase K False discovery rate Shotgun sequencing Saccharomyces cerevisiae Isotopes of nitrogen Label-free quantification
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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PeptideWitch–A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

A label-free quantitative shotgun proteomics analysis of rice grain development

An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters

Significance analysis of spectral count data in label-free shotgun proteomics.

Evaluation of Normalization Methods on GeLC-MS/MS Label-Free Spectral Counting Data to Correct for Variation during Proteomic Workflows

The diversity of protein turnover and abundance under nitrogen-limited steady-state conditions in Saccharomyces cerevisiae

Substrate specificity of proteinase yscA from Saccharomyces cerevisiae

Validation of MS/MS identifications and label-free quantification using Proline

Quantitative shotgun proteomics using a protease with broad specificity and normalized spectral abundance factors

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PeptideWitch–A Software Package to Produce High-Stringency Proteomics Data Visualizations from Label-Free Shotgun Proteomics Data

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

A label-free quantitative shotgun proteomics analysis of rice grain development

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Evaluation of Normalization Methods on GeLC-MS/MS Label-Free Spectral Counting Data to Correct for Variation during Proteomic Workflows

The diversity of protein turnover and abundance under nitrogen-limited steady-state conditions in Saccharomyces cerevisiae

Substrate specificity of proteinase yscA from Saccharomyces cerevisiae

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Quantitative shotgun proteomics using a protease with broad specificity and normalized spectral abundance factors