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 2.0 introduces four main modules, each providing distinct and new functionalities: 1) Pair Finder, which identifies ion doublets characteristic of cleavable cross-linking experiments; 2) Diagnostic Peak Finder, which locates potential reporter ions associated with a specific cross-linker; 3) Precursor Signal Ratio, which computes the ratio between precursor intensity and the total signal in an MS/MS scan; and 4) Xrea, which evaluates spectral quality by analyzing the heterogeneity of peak intensities within a spectrum. These modules collectively streamline the process of optimizing mass spectrometry data acquisition for both Proteomics and XL-MS experiments. RawVegetable 2.0, along with a comprehensive tutorial is freely accessible for academic use at: http://patternlabforproteomics.org/rawvegetable2.

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. Further open-search attempts via FragPipe yielded an additional ∼20% identifications, suggesting the remaining unidentified spectra likely arise from complex combinations of post-translational modifications and amino-acid substitutions. This highlights the untapped potential of the dataset for future discoveries, particularly given the importance of PTMs, which remain elusive to nucleotide sequencing approaches but are crucial for understanding biological mechanisms. Our innovative approach extends beyond the identifications that are typically subjected to the bias of a search engine; we discern which spectral clusters are differential and subject them to increased scrutiny, akin to spectral library matching by comparing captured spectra to themselves. Our analysis reveals adaptations in the resistant strain, including enhanced detoxification, altered protein synthesis, and metabolic adjustments. SIGNIFICANCE: We present comprehensive proteomic profiles of non-resistant and resistant Acinetobacter baumannii from Brazilian Hospitals strains, and highlight the presence of discriminative and yet unidentified mass spectral clusters. Our work emphasizes the importance of exploring this overlooked data, as it could hold the key to understanding the complex dynamics of antibiotic resistance. This approach not only informs antimicrobial stewardship efforts but also paves the way for the development of innovative diagnostic tools. Thus, our findings have profound implications for the field, as far as methods for providing a new perspective on diagnosing antibiotic resistance as well as classifying proteomes in general.

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. In general, we expect an increase of 10% in spectral identifications when dealing with complex proteomic samples. PLV is freely available at http://patternlabforproteomics.org.

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. Their roles seemed different in PCD, TcMCA5 appears as a proapoptotic protein negatively regulated by its C-terminal sequence, while TcMCA3 is described as a cell cycle regulator. Despite this, the precise role of TcMCA3 and TcMCA5 and their atomic structures remain elusive. Therefore, developing methodologies to allow investigations of those metacaspases is relevant. Herein, we produced full-length and truncated versions of TcMCA5 and applied different strategies for their folded recombinant production from E. coli inclusion bodies. Biophysical assays probed the efficacy of the production method in providing a high yield of folded recombinant TcMCA5. Moreover, we modeled the TcMCA5 protein structure using experimental restraints obtained by XLMS. The experimental design for novel methods and the final protocol provided here can guide studies with other metacaspases. The production of TcMCA5 allows further investigations as protein crystallography, HTS drug discovery to create potential therapeutic in the treatment of Chagas' disease and in the way to clarify how the PCD works in the parasite.

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-Cys1pP0 and p64K-ßAla1pP0) were synthesized using the p64K carrier protein from Neisseria meningitidis produced in Escherichia coli, the same cross-linking reagent, and two analogues of pP0. The SDS-PAGE analysis of p64K-Cys1pP0 showed a heterogeneous conjugate compared to p64K-ßAla1pP0 that was detected as a protein band at 91kDa. The pP0/p64K ratio determined by MALDI-MS for p64K-Cys1pP0 ranged from 1 to 8, being 3-5 the predominant ratio, while in the case of p64K-ßAla1pP0 this ratio was 5-7. Cys1pP0 was partially linked to 35 out of 39 Lys residues and the N-terminal end, while ßAla1pP0 was mostly linked to the six free cysteine residues, to the N-terminal end, and, in a lesser extent, to Lys residues. The assignment of the conjugation sites and side reactions were based on the identification of type 2 peptides. Rabbit immunizations showed the best anti-pP0 titers and the highest efficacy against Rhipicephalus sanguineus ticks when the p64K-Cys1pP0 was used as vaccine antigen. The presence of high molecular mass aggregates observed in the SDS-PAGE analysis of p64K-Cys1pP0 could be responsible for a better immune response against pP0 and consequently for its better efficacy as an anti-tick vaccine. Graphical abstract.

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

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. Thus, failing to consider the anatomical region could lead to differential proteins that are not disease specific. With this as motivation, we compared the proteomic profiles of intestinal and diffuse adenocarcinoma from the same anatomical region, the corpus. To achieve this, we used isobaric labeling (iTRAQ) of peptides, a 10-step HILIC fractionation, and reversed-phase nano-chromatography coupled online with a Q-Exactive Plus mass spectrometer. We updated PatternLab to take advantage of the new Comet-PEFF search engine that enables identifying post-translational modifications and mutations included in neXtProt's PSI Extended FASTA Format (PEFF) metadata. Our pipeline then uses a text-mining tool that automatically extracts PubMed IDs from the proteomic result metadata and drills down keywords from manuscripts related with the biological processes at hand. Our results disclose important proteins such as apolipoprotein B-100, S100 and 14-3-3 proteins, among many others, highlighting the different pathways enriched by each cancer type. SIGNIFICANCE: Gastric cancer is a heterogeneous and multifactorial disease responsible for a significant number of deaths every year. Despite the constant improvement of surgical techniques and multimodal treatments, survival rates are low, mostly due to limited diagnostic techniques and late symptoms. Intestinal and diffuse types of gastric cancer have distinct clinical and pathological characteristics; yet little is known about the molecular mechanisms regulating these two types of gastric tumors. Here we compared the proteomic profile of diffuse and intestinal types of gastric cancer from the same anatomical location, the corpus, from four male patients. This methodological design aimed to eliminate proteomic variations resulting from comparison of tumors from distinct anatomical regions. Our PEFF-tailored proteomic pipeline significantly increased the identifications as when compared to previous versions of PatternLab.