PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for analyzing shotgun proteomic data. PatternLab contains modules for formatting sequence databases, performing peptide spectrum matching, statistically filtering and organizing shotgun proteomic data, extracting quantitative information from label-free and chemically labeled data, performing statistics for differential proteomics, displaying results in a variety of graphical formats, performing similarity-driven studies with de novo sequencing data, analyzing time-course experiments, and helping with the understanding of the biological significance of data in the light of the Gene Ontology. Here we describe PatternLab for proteomics 4.0, which closely knits together all of these modules in a self-contained environment, covering the principal aspects of proteomic data analysis as a freely available and easily installable software package. All updates to PatternLab, as well as all new features added to it, have been tested over the years on millions of mass spectra.

Chemical cross-linking has emerged as a powerful approach for the structural characterization of proteins and protein complexes. However, the correct identification of covalently linked (cross-linked or XL) peptides analyzed by tandem mass spectrometry is still an open challenge. Here we present SIM-XL, a software tool that can analyze data generated through commonly used cross-linkers (e.g., BS3/DSS). Our software introduces a new paradigm for search-space reduction, which ultimately accounts for its increase in speed and sensitivity. Moreover, our search engine is the first to capitalize on reporter ions for selecting tandem mass spectra derived from cross-linked peptides. It also makes available a 2D interaction map and a spectrum-annotation tool unmatched by any of its kind. We show SIM-XL to be more sensitive and faster than a competing tool when analyzing a data set obtained from the human HSP90. The software is freely available for academic use at http://patternlabforproteomics.org/sim-xl. A video demonstrating the tool is available at http://patternlabforproteomics.org/sim-xl/video. SIM-XL is the first tool to support XL data in the mzIdentML format; all data are thus available from the ProteomeXchange consortium (identifier PXD001677). This article is part of a Special Issue entitled: Computational Proteomics.

Cross-linking coupled with mass spectrometry (XL-MS) has emerged as a powerful strategy for the identification of protein-protein interactions, characterization of interaction regions, and obtainment of structural information on proteins and protein complexes. In XL-MS, proteins or complexes are covalently stabilized with cross-linkers and digested, followed by identification of the cross-linked peptides by tandem mass spectrometry (MS/MS). This provides spatial constraints that enable modeling of protein (complex) structures and regions of interaction. However, most XL-MS approaches are not capable of differentiating intramolecular from intermolecular links in multimeric complexes, and therefore they cannot be used to study homodimer interfaces. We have recently developed an approach that overcomes this limitation by stable isotope-labeling of one of the two monomers, thereby creating a homodimer with one 'light' and one 'heavy' monomer. Here, we describe a step-by-step protocol for stable isotope-labeling, followed by controlled denaturation and refolding in the presence of the wild-type protein. The resulting light-heavy dimers are cross-linked, digested, and analyzed by mass spectrometry. We show how to quantitatively analyze the corresponding data with SIM-XL, an XL-MS software with a module tailored toward the MS/MS data from homodimers. In addition, we provide a video tutorial of the data analysis with this protocol. This protocol can be performed in ∼14 d, and requires basic biochemical and mass spectrometry skills.

The current technique used for microbial identification in hospitals is matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). However, it suffers from important limitations, in particular, for closely related species or when the database used for the identification lacks the appropriate reference. In this work, we set up a liquid chromatography (LC)−MS/MS top-down proteomics platform, which aims at discriminating closely related pathogenic bacteria through the identification of specific proteoforms. Using Escherichia coli as a model, all steps of the workflow were optimized: protein extraction, on-line LC separation, MS method, and data analysis. Using optimized parameters, about 220 proteins, corresponding to more than 500 proteoforms, could be identified in a single run. We then used this platform for the discrimination of enterobacterial pathogens undistinguishable by MALDI-TOF, although leading to very different clinical outcomes. For each pathogen, we identified specific proteoforms that could potentially be used as biomarkers. We also improved the characterization of poorly described bacterial strains. Our results highlight the advantage of addressing proteoforms rather than peptides for accurate bacterial characterization and qualify top-down proteomics as a promising tool in clinical microbiology. Data are available via ProteomeXchange with the identifier PXD019247.