Fast Proteome Identification and Quantification from Data-Dependent Acquisition–Tandem Mass Spectrometry (DDA MS/MS) Using Free Software Tools

Jesse Meyer

doi:10.3390/mps2010008

Abstract

The identification of nearly all proteins in a biological system using data-dependent acquisition (DDA) tandem mass spectrometry has become routine for organisms with relatively small genomes such as bacteria and yeast. Still, the quantification of the identified proteins may be a complex process and often requires multiple different software packages. In this protocol, I describe a flexible strategy for the identification and label-free quantification of proteins from bottom-up proteomics experiments. This method can be used to quantify all the detectable proteins in any DDA dataset collected with high-resolution precursor scans and may be used to quantify proteome remodeling in response to drug treatment or a gene knockout. Notably, the method is statistically rigorous, uses the latest and fastest freely-available software, and the entire protocol can be completed in a few hours with a small number of data files from the analysis of yeast.

Highlights

Tandem mass spectrometry is currently the best method for unbiased, high-throughput protein identification [1]
The quantification of proteome remodeling can be a slow and difficult process, and many options are available for the multiple steps of the analysis [4,5,6]
The main aim of this protocol is to identify and quantify proteins starting from raw mass spectrometry data

Summary

Introduction

Tandem mass spectrometry is currently the best method for unbiased, high-throughput protein identification [1]. If samples are processed in parallel with randomization and are analyzed on the same column at a similar period in time, label-free quantification can be more effective in detecting large protein changes than isotope labeling methods This protocol describes an analysis workflow for label-free quantification, the software tools presented are compatible with the analysis of isotope labeling data. The peak areas could be output for each sample, filtered for an arbitrary number of top N peptides, and input into MSstats or mapDIA [14] This protocol uses MS-Fragger for peptide identification, which means it can be adapted to find unexpected protein modifications [15]. The benefits of the described strategy far outweigh the limitations, and in the expected results section we highlight one case where this strategy enabled easy verification of a discrepancy between this protocol and MaxQuant’s results

Experimental Design

Methods and Protoc

Procedure

Prepare the Organism-Specific Database Using Philosopher

Statistical Testing with MSstats