To quantify proteins in shotgun proteomics, we quantify the
peptides that uniquely match proteins. Peptides identified by
nLC-MS/MS are quantified using the area under the curve of the
peptide’s intact mass/charge over chromatographic elution time
(Fig. 1b) [6]. The quantities of peptides for each protein are then
combined into a single protein quantity (Fig. 1c). Finally, we use
statistical tests to determine whether protein quantities are different
across conditions (assessed by a p-value) and the magnitude of that
difference (assessed by fold change). Significance and magnitude of
changes are often visualized simultaneously using a volcano plot
(Fig. 1d).
This chapter describes a fast and statistically rigorous workflow
for analysis of peptides from data-dependent acquisition proteomics data. This analysis includes standard protein quantitation and
statistical testing to find differences. This chapter also describes and
demonstrates one downstream strategy for analysis of the protein
changes induced by biological treatments, gene ontology
(GO) term enrichment analysis. These analyses are demonstrated
using public data from proteomic analysis of primary microglia
from mouse brain across controls, ethanol, and lipopolysaccharide
(LPS) stresses [7], but the protocol herein can be used to analyze
any data collected with the same common DDA strategy.
2 Materials
This protocol requires a computer running windows 10 with a
multi-core 64-bit processor (at least quad core i5 recommended)
and at least 8 GB of RAM with at least 50 GB of free disk space.
2.1 Peptide
Identification by
Database Search
1. Tutorial data files (.RAW from Pride repository PXD014466 [8],
https://www.ebi.ac.uk/pride/archive/projects/PXD014466,
original publication [7]).
2. MSconvert.exe (part of proteowizard, latest version, used version
3.0.19039 here, http://proteowizard.sourceforge.net/) [9].
3. FragPipe.exe (version 12.1, https://github.com/Nesvilab/
FragPipe/releases/tag/12.1).
4. MSfragger.jar (version 2.2, http://msfragger.nesvilab.org/) [10].
5. Philosopher.exe (version 2.0, contains PeptideProphet, ProteinProphet, and iProphet, https://philosopher.nesvilab.org/
, web address) [5, 11, 12].
2.2 Quantification
of Peptides
and Detection
of Protein Changes
1. Skyline-Daily Software (latest version, used version 20.1 here,
https://skyline.ms) [13].
2. R statistical computing software (version 3.5.1, base version:
www.r-project.org, RStudio suggested: https://rstudio.
com/) [14].
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Jesse G. Meyer
peptides that uniquely match proteins. Peptides identified by
nLC-MS/MS are quantified using the area under the curve of the
peptide’s intact mass/charge over chromatographic elution time
(Fig. 1b) [6]. The quantities of peptides for each protein are then
combined into a single protein quantity (Fig. 1c). Finally, we use
statistical tests to determine whether protein quantities are different
across conditions (assessed by a p-value) and the magnitude of that
difference (assessed by fold change). Significance and magnitude of
changes are often visualized simultaneously using a volcano plot
(Fig. 1d).
This chapter describes a fast and statistically rigorous workflow
for analysis of peptides from data-dependent acquisition proteomics data. This analysis includes standard protein quantitation and
statistical testing to find differences. This chapter also describes and
demonstrates one downstream strategy for analysis of the protein
changes induced by biological treatments, gene ontology
(GO) term enrichment analysis. These analyses are demonstrated
using public data from proteomic analysis of primary microglia
from mouse brain across controls, ethanol, and lipopolysaccharide
(LPS) stresses [7], but the protocol herein can be used to analyze
any data collected with the same common DDA strategy.
2 Materials
This protocol requires a computer running windows 10 with a
multi-core 64-bit processor (at least quad core i5 recommended)
and at least 8 GB of RAM with at least 50 GB of free disk space.
2.1 Peptide
Identification by
Database Search
1. Tutorial data files (.RAW from Pride repository PXD014466 [8],
https://www.ebi.ac.uk/pride/archive/projects/PXD014466,
original publication [7]).
2. MSconvert.exe (part of proteowizard, latest version, used version
3.0.19039 here, http://proteowizard.sourceforge.net/) [9].
3. FragPipe.exe (version 12.1, https://github.com/Nesvilab/
FragPipe/releases/tag/12.1).
4. MSfragger.jar (version 2.2, http://msfragger.nesvilab.org/) [10].
5. Philosopher.exe (version 2.0, contains PeptideProphet, ProteinProphet, and iProphet, https://philosopher.nesvilab.org/
, web address) [5, 11, 12].
2.2 Quantification
of Peptides
and Detection
of Protein Changes
1. Skyline-Daily Software (latest version, used version 20.1 here,
https://skyline.ms) [13].
2. R statistical computing software (version 3.5.1, base version:
www.r-project.org, RStudio suggested: https://rstudio.
com/) [14].
300
Jesse G. Meyer