Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Online nanoscale ERLIC-MS outperforms RPLC-MS for shotgun proteomics in complex mixtures.

ABSTRACT: We have explored the use of electrostatic repulsion hydrophilic interaction chromatography (ERLIC) as an alternative to the gold-standard in shotgun proteomics: reversed-phase (RP) LC for online ESI-MS/MS. Conditions for sample solubilization and initial gradient conditions were optimized to strike a balance between peptide solubility and maximum peptide retention when using mobile phase with high organic solvent concentration. Online ERLIC-MS demonstrated a 57% increase in total peptide identifications compared to RP-MS. We examined the mechanism of this improved performance and found that it stems from ERLIC's propensity to retain longer peptides, which can be identified with greater confidence. Online nanoscale ERLIC-MS provides a powerful new tool for enhancing MS-based shotgun proteomic in a broad range of applications.

SUBMITTER: de Jong EP

PROVIDER: S-EPMC3487405 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Online nanoscale ERLIC-MS outperforms RPLC-MS for shotgun proteomics in complex mixtures.

de Jong Ebbing P EP Griffin Timothy J TJ

Journal of proteome research 20120914 10

We have explored the use of electrostatic repulsion hydrophilic interaction chromatography (ERLIC) as an alternative to the gold-standard in shotgun proteomics: reversed-phase (RP) LC for online ESI-MS/MS. Conditions for sample solubilization and initial gradient conditions were optimized to strike a balance between peptide solubility and maximum peptide retention when using mobile phase with high organic solvent concentration. Online ERLIC-MS demonstrated a 57% increase in total peptide identif ...[more]

PMID: 22950739

Similar Datasets

Thermodynamic analysis of protein-ligand interactions in complex biological mixtures using a shotgun proteomics approach.

Project description:Shotgun proteomics protocols are widely used for the identification and/or quantitation of proteins in complex biological samples. Described here is a shotgun proteomics protocol that can be used to identify the protein targets of biologically relevant ligands in complex protein mixtures. The protocol combines a quantitative proteomics platform with a covalent modification strategy, termed Stability of Proteins from Rates of Oxidation (SPROX), which utilizes the denaturant dependence of hydrogen peroxide-mediated oxidation of methionine side chains in proteins to assess the thermodynamic properties of proteins and protein-ligand complexes. The quantitative proteomics platform involves the use of isobaric mass tags and a methionine-containing peptide enhancement strategy. The protocol is evaluated in a ligand binding experiment designed to identify the proteins in a yeast cell lysate that bind the well-known enzyme cofactor, ?-nicotinamide adenine dinucleotide (NAD+). The protocol is also used to investigate the protein targets of resveratrol, a biologically active ligand with less well-understood protein targets. A known protein target of resveratrol, cytosolic aldehyde dehydrogenase, was identified in addition to six other potential new proteins targets including four that are associated with the protein translation machinery, which has previously been implicated as a target of resveratrol.

| S-EPMC3208786 | biostudies-literature

Improved Nanoflow RPLC-CZE-MS/MS System with High Peak Capacity and Sensitivity for Nanogram Bottom-up Proteomics.

Project description:Novel mass spectrometry (MS)-based proteomic tools with extremely high sensitivity and high peak capacity are required for comprehensive characterization of protein molecules in mass-limited samples. We reported a nanoRPLC-CZE-MS/MS system for deep bottom-up proteomics of low micrograms of human cell samples in previous work. In this work, we improved the sensitivity of the nanoRPLC-CZE-MS/MS system drastically via employing bovine serum albumin (BSA)-treated sample vials, improving the nanoRPLC fraction collection procedure, and using a short capillary for fast CZE separation. The improved nanoRPLC-CZE produced a peak capacity of 8500 for peptide separation. The improved system identified 6500 proteins from a MCF7 proteome digest starting with only 500 ng of peptides using a Q-Exactive HF mass spectrometer. The system produced a comparable number of protein identifications (IDs) to our previous system and the two-dimensional (2D) nanoRPLC-MS/MS system developed by Mann's group with 10-fold and 4-fold less sample consumption, respectively. We coupled the single-spot solid phase sample preparation (SP3) method to the improved nanoRPLC-CZE-MS/MS for bottom-up proteomics of 5000 HEK293T cells, resulting in 3689 protein IDs with the consumption of a peptide amount that corresponded to only roughly 1000 cells.

| S-EPMC7545725 | biostudies-literature

MSAcquisitionSimulator: data-dependent acquisition simulator for LC-MS shotgun proteomics.

Project description:UnlabelledData-dependent acquisition (DDA) is the most common method used to control the acquisition process of shotgun proteomics experiments. While novel DDA approaches have been proposed, their evaluation is made difficult by the need of programmatic control of a mass spectrometer. An alternative is in silico analysis, for which suitable software has been unavailable. To meet this need, we have developed MSAcquisitionSimulator-a collection of C ++ programs for simulating ground truth LC-MS data and the subsequent application of custom DDA algorithms. It provides an opportunity for researchers to test, refine and evaluate novel DDA algorithms prior to implementation on a mass spectrometer.Availability and implementationThe software is freely available from its Github repository http://www.github.com/DennisGoldfarb/MSAcquisitionSimulator/ which contains further documentation and usage instructions.Contactweiwang@cs.ucla.edu or ben_major@med.unc.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC4894284 | biostudies-literature

Cancer proteomics by quantitative shotgun proteomics.

Project description:A major scientific challenge at the present time for cancer research is the determination of the underlying biological basis for cancer development. It is further complicated by the heterogeneity of cancer's origin. Understanding the molecular basis of cancer requires studying the dynamic and spatial interactions among proteins in cells, signaling events among cancer cells, and interactions between the cancer cells and the tumor microenvironment. Recently, it has been proposed that large-scale protein expression analysis of cancer cell proteomes promises to be valuable for investigating mechanisms of cancer transformation. Advances in mass spectrometry technologies and bioinformatics tools provide a tremendous opportunity to qualitatively and quantitatively interrogate dynamic protein-protein interactions and differential regulation of cellular signaling pathways associated with tumor development. In this review, progress in shotgun proteomics technologies for examining the molecular basis of cancer development will be presented and discussed.

| S-EPMC2352161 | biostudies-other

Improving protein identification sensitivity by combining MS and MS/MS information for shotgun proteomics using LTQ-Orbitrap high mass accuracy data.

Project description:We investigated and compared three approaches for shotgun protein identification by combining MS and MS/MS information using LTQ-Orbitrap high mass accuracy data. In the first approach, we employed a unique mass identifier method where MS peaks matched to peptides predicted from proteins identified from an MS/MS database search are first subtracted before using the MS peaks as unique mass identifiers for protein identification. In the second method, we used an accurate mass and time tag method by building a potential mass and retention time database from previous MudPIT analyses. For the third method, we used a peptide mass fingerprinting-like approach in combination with a randomized database for protein identification. We show that we can improve protein identification sensitivity for low-abundance proteins by combining MS and MS/MS information. Furthermore, "one-hit wonders" from MS/MS database searching can be further substantiated by MS information and the approach improves the identification of low-abundance proteins. The advantages and disadvantages for the three approaches are then discussed.

| S-EPMC3509208 | biostudies-literature

Automated protein turnover calculations from 15N partial metabolic labeling LC/MS shotgun proteomics data.

Project description:Protein turnover is a well-controlled process in which polypeptides are constantly being degraded and subsequently replaced with newly synthesized copies. Extraction of composite spectral envelopes from complex LC/MS shotgun proteomics data can be a challenging task, due to the inherent complexity of biological samples. With partial metabolic labeling experiments this complexity increases as a result of the emergence of additional isotopic peaks. Automated spectral extraction and subsequent protein turnover calculations enable the analysis of gigabytes of data within minutes, a prerequisite for systems biology high throughput studies. Here we present a fully automated method for protein turnover calculations from shotgun proteomics data. The approach enables the analysis of complex shotgun LC/MS 15N partial metabolic labeling experiments. Spectral envelopes of 1419 peptides can be extracted within an hour. The method quantifies turnover by calculating the Relative Isotope Abundance (RIA), which is defined as the ratio between the intensity sum of all heavy (15N) to the intensity sum of all light (14N) and heavy peaks. To facilitate this process, we have developed a computer program based on our method, which is freely available to download at http://promex.pph.univie.ac.at/protover.

| S-EPMC3988089 | biostudies-literature

Adaptive discriminant function analysis and reranking of MS/MS database search results for improved peptide identification in shotgun proteomics.

Project description:Robust statistical validation of peptide identifications obtained by tandem mass spectrometry and sequence database searching is an important task in shotgun proteomics. PeptideProphet is a commonly used computational tool that computes confidence measures for peptide identifications. In this paper, we investigate several limitations of the PeptideProphet modeling approach, including the use of fixed coefficients in computing the discriminant search score and selection of the top scoring peptide assignment per spectrum only. To address these limitations, we describe an adaptive method in which a new discriminant function is learned from the data in an iterative fashion. We extend the modeling framework to go beyond the top scoring peptide assignment per spectrum. We also investigate the effect of clustering the spectra according to their spectrum quality score followed by cluster-specific mixture modeling. The analysis is carried out using data acquired from a mixture of purified proteins on four different types of mass spectrometers, as well as using a complex human serum data set. A special emphasis is placed on the analysis of data generated on high mass accuracy instruments.

| S-EPMC3744223 | biostudies-literature

Analyzing marginal cases in differential shotgun proteomics.

Project description:We present an approach to statistically pinpoint differentially expressed proteins that have quantitation values near the quantitation threshold and are not identified in all replicates (marginal cases). Our method uses a Bayesian strategy to combine parametric statistics with an empirical distribution built from the reproducibility quality of the technical replicates.The software is freely available for academic use at http://pcarvalho.com/patternlab.

| S-EPMC3018820 | biostudies-other

De novo correction of mass measurement error in low resolution tandem MS spectra for shotgun proteomics.

Project description:We report an algorithm designed for the calibration of low resolution peptide mass spectra. Our algorithm is implemented in a program called FineTune, which corrects systematic mass measurement error in 1 min, with no input required besides the mass spectra themselves. The mass measurement accuracy for a set of spectra collected on an LTQ-Velos improved 20-fold from -0.1776?±?0.0010?m/z to 0.0078?±?0.0006?m/z after calibration (avg?±?95 % confidence interval). The precision in mass measurement was improved due to the correction of non-linear variation in mass measurement accuracy across the m/z range.

| S-EPMC3515694 | biostudies-literature

Perfluorooctanoic acid for shotgun proteomics.

Project description:Here, we describe the novel use of a volatile surfactant, perfluorooctanoic acid (PFOA), for shotgun proteomics. PFOA was found to solubilize membrane proteins as effectively as sodium dodecyl sulfate (SDS). PFOA concentrations up to 0.5% (w/v) did not significantly inhibit trypsin activity. The unique features of PFOA allowed us to develop a single-tube shotgun proteomics method that used all volatile chemicals that could easily be removed by evaporation prior to mass spectrometry analysis. The experimental procedures involved: 1) extraction of proteins in 2% PFOA; 2) reduction of cystine residues with triethyl phosphine and their S-alkylation with iodoethanol; 3) trypsin digestion of proteins in 0.5% PFOA; 4) removal of PFOA by evaporation; and 5) LC-MS/MS analysis of the resulting peptides. The general applicability of the method was demonstrated with the membrane preparation of photoreceptor outer segments. We identified 75 proteins from 1 µg of the tryptic peptides in a single, 1-hour, LC-MS/MS run. About 67% of the proteins identified were classified as membrane proteins. We also demonstrate that a proteolytic (18)O labeling procedure can be incorporated after the PFOA removal step for quantitative proteomic experiments. The present method does not require sample clean-up devices such as solid-phase extractions and membrane filters, so no proteins/peptides are lost in any experimental steps. Thus, this single-tube shotgun proteomics method overcomes the major drawbacks of surfactant use in proteomic experiments.

| S-EPMC3012695 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data