Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein-protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.

Mass spectrometry-based proteomics

A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here we describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used commercial search engine, as judged by sensitivity and specificity analysis based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables analysis of large data sets in a simple analysis workflow on a desktop computer. For searching individual spect...

https://pubs.acs.org/doi/pdfplus/10.1021/pr101065j?src=recsys

Andromeda: a peptide search engine integrated into the MaxQuant environment

In-gel digestion of proteins isolated by gel electrophoresis is a cornerstone of mass spectrometry (MS)-driven proteomics. The 10-year-old recipe by Shevchenko et al. has been optimized to increase the speed and sensitivity of analysis. The protocol is for the in-gel digestion of both silver and Coomassie-stained protein spots or bands and can be followed by MALDI-MS or LC-MS/MS analysis to identify proteins at sensitivities better than a few femtomoles of protein starting material.

/pdf/in-gel-digestion-for-mass-spectrometric-characterization-of-aua600g7gw.pdf

In-gel digestion for mass spectrometric characterization of proteins and proteomes

Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using Amine-reactive Isobaric Tagging Reagents

https://cseweb.ucsd.edu/classes/sp10/cse283/notes/silac.pdf

Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

Experimental Peptide Identification Repository (EPIR) An Integrated Peptide-Centric Platform for Validation and Mining of Tandem Mass Spectrometry Data

Peptide mapping by liquid chromatography mass spectrometry (LC-MS) and the related multi-attribute method (MAM) are well-established analytical tools for verification of the primary structure and mapping/quantitation of co- and post-translational modifications (PTMs) or product quality attributes in biopharmaceutical development. Proteolytic digestion is a key step in peptide mapping workflows, which traditionally is labor-intensive, involving multiple manual steps. Recently, simple high-temperature workflows with automatic digestion were introduced, which facilitate robustness and reproducibility across laboratories. Here, a modified workflow with an automatic digestion step is presented, which includes a two-step digestion at high and low temperatures, as opposed to the original one-step digestion at a high temperature. The new automatic digestion workflow significantly reduces the number of missed cleavages, obtaining a more complete digestion profile. In addition, we describe how chromatographic peak tailing and carry-over is dramatically reduced for hydrophobic peptides by switching from the traditional C18 reversed-phase (RP) column chemistry used for peptide mapping to a less retentive C4 column chemistry. No negative impact is observed on MS/MS-derived sequence coverage when switching to a C4 column chemistry. Overall, the new peptide mapping workflow significantly reduces the number of missed cleavages, yielding more robust and simple data interpretation, while providing dramatically reduced tailing and carry-over of hydrophobic peptides.

Optimized Multi-Attribute Method Workflow Addressing Missed Cleavages and Chromatographic Tailing/Carry-Over of Hydrophobic Peptides

The biotechnology and pharmaceutical industries are faced with the serious challenge of consolidating the enormous quantities of data that have been generated from high-throughput proteomic applications. The bottleneck of data validation and placement of the information obtained into sound biological context urgently needs to be addressed. Here, we review the issues that arise when analysing large quantities of data generated by liquid chromatography mass spectrometry, offer potential solutions for data management and predict the future direction of large-scale data analysis by mass spectrometry.

Analysis of large-scale MS data sets: the dramas and the delights

Peptide mapping of antibodies is an essential method to monitor peptide modifications in antibody lots that could affect the safety and efficacy of the product. Conventional protocols rely on protein digestion using proteases, such as trypsin, before mapping with mass spectrometry (MS). However, trypsin digestion may cause incomplete mapping of peptides, especially those that include highly hydrophobic peptides. Here, we show how pepsin can be used as an alternative and complementary protease for digestion that allows for improved sequence coverage, especially in proteins with highly hydrophobic regions. We also show that using guanidine hydrochloride (GuHCl) post-digestion improves peptide mapping results. Overall, these two methods—pepsin digestion and GuHCl post-digestion—can be used to provide more comprehensive antibody peptide maps, thereby enabling more thorough quality checking of biopharmaceutical products.

/pdf/overcoming-incomplete-peptide-mapping-of-antibody-3pk834xl.pdf

Dan B. Kristensen

Papers

Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics.

Experimental Peptide Identification Repository (EPIR) An Integrated Peptide-Centric Platform for Validation and Mining of Tandem Mass Spectrometry Data

Optimized Multi-Attribute Method Workflow Addressing Missed Cleavages and Chromatographic Tailing/Carry-Over of Hydrophobic Peptides

Analysis of large-scale MS data sets: the dramas and the delights

Overcoming Incomplete Peptide Mapping of Antibody Complementarity-Determining Regions with Alternate Digestion Workflows