Proteome-scale recombinant standards and a robust high-speed search engine to advance cross-linking MS-based interactomics.

Advancing data analysis tools for proteome-wide cross-linking mass spectrometry (XL-MS) requires ground-truth standards that mimic biological complexity. Here we develop well-controlled XL-MS standards comprising hundreds of recombinant proteins that are systematically mixed for cross-linking. We use one standard dataset to guide the development of Scout, a search engine for XL-MS with MS-cleavable cross-linkers.

RawVegetable 2.0: Refining XL-MS Data Acquisition through Enhanced Quality Control.

We present RawVegetable 2.0, a software tailored for assessing mass spectrometry data quality and fine-tuned for cross-linking mass spectrometry (XL-MS) applications. Building upon the capabilities of its predecessor, RawVegetable 2.

Beyond the identifiable proteome: Delving into the proteomics of polymyxin-resistant and non-resistant Acinetobacter baumannii from Brazilian hospitals.

This work discloses a unique, comprehensive proteomic dataset of Acinetobacter baumannii strains, both resistant and non-resistant to polymyxin B, isolated in Brazil generated using Orbitrap Fusion Lumos. From nearly 4 million tandem mass spectra, the software DiagnoMass produced 240,685 quality-filtered mass spectral clusters, of which PatternLab for proteomics identified 44,553 peptides mapping to 3479 proteins. Crucially, DiagnoMass shortlisted 3550 and 1408 unique mass spectral clusters for the resistant and non-resistant strains, respectively, with only about a third with sequences (and PTMs) identified by PatternLab.

PatternLab V Handles Multiplex Spectra in Shotgun Proteomic Searches and Increases Identification.

Complex protein mixtures typically generate many tandem mass spectra produced by different peptides coisolated in the gas phase. Widely adopted proteomic data analysis environments usually fail to identify most of these spectra, succeeding at best in identifying only one of the multiple cofragmenting peptides. We present PatternLab V (PLV), an updated version of PatternLab that integrates the YADA 3 deconvolution algorithm to handle such cases efficiently.

Increasing confidence in proteomic spectral deconvolution through mass defect.

Motivation: Confident deconvolution of proteomic spectra is critical for several applications such as de novo sequencing, cross-linking mass spectrometry and handling chimeric mass spectra.

Results: In general, all deconvolution algorithms may eventually report mass peaks that are not compatible with the chemical formula of any peptide. We show how to remove these artifacts by considering their mass defects.

Simple, efficient and thorough shotgun proteomic analysis with PatternLab V.

Shotgun proteomics aims to identify and quantify the thousands of proteins in complex mixtures such as cell and tissue lysates and biological fluids. This approach uses liquid chromatography coupled with tandem mass spectrometry and typically generates hundreds of thousands of mass spectra that require specialized computational environments for data analysis. PatternLab for proteomics is a unified computational environment for analyzing shotgun proteomic data.

Refolding of metacaspase 5 from Trypanosoma cruzi, structural characterization and the influence of c-terminal in protein recombinant production.

Metacaspases are known to have a fundamental role in apoptosis-like, a programmed cellular death (PCD) in plants, fungi, and protozoans. The last includes several parasites that cause diseases of great interest to public health, mostly without adequate treatment and included in the neglected tropical diseases category. One of them is Trypanosoma cruzi which causes Chagas disease and has two metacaspases involved in its PCD: TcMCA3 and TcMCA5.

Synthesis, LC-MS/MS analysis, and biological evaluation of two vaccine candidates against ticks based on the antigenic P0 peptide from R. sanguineus linked to the p64K carrier protein from Neisseria meningitidis.

A peptide from the P0 acidic ribosomal protein (pP0) of ticks conjugated to keyhole limpet hemocyanin from Megathura crenulata has shown to be effective against different tick species when used in host vaccination. Turning this peptide into a commercial anti-tick vaccine will depend on finding the appropriate, technically and economically feasible way to present it to the host immune system. Two conjugates (p64K-CyspP0 and p64K-βAlapP0) were synthesized using the p64K carrier protein from Neisseria meningitidis produced in Escherichia coli, the same cross-linking reagent, and two analogues of pP0.

Leveraging the partition selection bias to achieve a high-quality clustering of mass spectra.

In proteomics, the identification of peptides from mass spectral data can be mathematically described as the partitioning of mass spectra into clusters (i.e., groups of spectra derived from the same peptide).

Characterizing protein conformers by cross-linking mass spectrometry and pattern recognition.

Motivation: Chemical cross-linking coupled to mass spectrometry (XLMS) emerged as a powerful technique for studying protein structures and large-scale protein-protein interactions. Nonetheless, XLMS lacks software tailored toward dealing with multiple conformers; this scenario can lead to high-quality identifications that are mutually exclusive. This limitation hampers the applicability of XLMS in structural experiments of dynamic protein systems, where less abundant conformers of the target protein are expected in the sample.

RawVegetable - A data assessment tool for proteomics and cross-linking mass spectrometry experiments.

We present RawVegetable, a software for mass spectrometry data assessment and quality control tailored toward shotgun proteomics and cross-linking experiments. RawVegetable provides four main modules with distinct features: (A) The charge state chromatogram that independently displays the ion current for each charge state; useful for optimizing the chromatography for highly charged ions and with lower XIC values such as those typically found in cross-linking experiments. (B) The XL-Artefact determination, which flags possible noncovalently associated peptides.

Mixed-Data Acquisition: Next-Generation Quantitative Proteomics Data Acquisition.

We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features.

A quantitation module for isotope-labeled peptides integrated into PatternLab for proteomics.

We present a new module integrated into the widely adopted PatternLab for proteomics to enable analysis of isotope-labeled peptides produced using dimethyl or SILAC. The accurate quantitation of proteins lies within the heart of proteomics; dimethylation has shown to be reliable, inexpensive, and applicable to any sample type. We validate our algorithm using an M.

Top-Down Garbage Collector: a tool for selecting high-quality top-down proteomics mass spectra.

Motivation: We present the first tool for unbiased quality control of top-down proteomics datasets. Our tool can select high-quality top-down proteomics spectra, serve as a gateway for building top-down spectral libraries and, ultimately, improve identification rates.

Results: We demonstrate that a twofold rate increase for two E.

Comparing intestinal versus diffuse gastric cancer using a PEFF-oriented proteomic pipeline.

Unlabelled: Gastric cancer is the fifth most common malignant neoplasia and the third leading cause of cancer death worldwide. Mac-Cormick et al. recently showed the importance of considering the anatomical region of the tumor in proteomic gastric cancer studies; more differences were found between distinct anatomical regions than when comparing healthy versus diseased tissue.