Publications by authors named "Diogo Borges-Lima"

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.

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The functions of cellular organelles and sub-compartments depend on their protein content, which can be characterized by spatial proteomics approaches. However, many spatial proteomics methods are limited in their ability to resolve organellar sub-compartments, profile multiple sub-compartments in parallel, and/or characterize membrane-associated proteomes. Here, we develop a cross-link assisted spatial proteomics (CLASP) strategy that addresses these shortcomings.

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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.

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Upon activation, vinculin reinforces cytoskeletal anchorage during cell adhesion. Activating ligands classically disrupt intramolecular interactions between the vinculin head and tail domains that bind to actin filaments. Here, we show that Shigella IpaA triggers major allosteric changes in the head domain, leading to vinculin homo-oligomerization.

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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.

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Cross-linking mass spectrometry (XL-MS) is a universal tool for probing structural dynamics and protein-protein interactions and . Although cross-linked peptides are naturally less abundant than their unlinked counterparts, recent experimental advances improved cross-link identification by enriching the cross-linker-modified peptides chemically with the use of enrichable cross-linkers. However, mono-links (, peptides modified with a hydrolyzed cross-linker) still hinder efficient cross-link identification since a large proportion of measurement time is spent on their MS2 acquisition.

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Cross-linking mass spectrometry (XL-MS) is a powerful method for the investigation of protein-protein interactions (PPI) from highly complex samples. XL-MS combined with tandem mass tag (TMT) labeling holds the promise of large-scale PPI quantification. However, a robust and efficient TMT-based XL-MS quantification method has not yet been established due to the lack of a benchmarking dataset and thorough evaluation of various MS parameters.

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Motivation: We present a new software-tool allowing an easy visualization of fragment ions and thus a rapid evaluation of key experimental parameters on the sequence coverage obtained for the MS/MS (tandem mass spectrometry) analysis of intact proteins. Our tool can process data obtained from various deconvolution and fragment assignment software.

Results: We demonstrate that TDFragMapper can rapidly highlight the experimental fragmentation parameters that are critical to the characterization of intact proteins of various size using top-down proteomics.

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Pathogen identification is crucial to confirm bacterial infections and guide antimicrobial therapy. Although MALDI-TOF mass spectrometry (MS) serves as foundation for tools that enable rapid microbial identification, some bacteria remain challenging to identify. We recently showed that top-down proteomics (TDP) could be used to discriminate closely related enterobacterial pathogens (, , and ) that are indistinguishable with tools rooted in the MALDI-TOF MS approach.

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Software tools that allow the visualization and analysis of protein interaction networks are essential for studies in systems biology. One of the most popular network visualization tools in biology is Cytoscape, which offers a great selection of plug-ins for the interpretation of network data. Chemical cross-linking coupled to mass spectrometry (XL-MS) is an increasingly important source for protein interaction data; however, to date, no Cytoscape tools are available to analyze XL-MS results.

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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.

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Disulfide bonds (SS) are post-translational modifications important for the proper folding and stabilization of many cellular proteins with therapeutic uses, including antibodies and other biologics. With budding advances of biologics and biosimilars, there is a mounting need for a robust method for accurate identification of SS. Even though several mass spectrometry methods have emerged for this task, their practical use rests on the broad effectiveness of both sample preparation methods and bioinformatics tools.

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Cross-linking/Mass spectrometry (XLMS) is a consolidated technique for structural characterization of proteins and protein complexes. Despite its success, the cross-linking chemistry currently used is mostly based on N-hydroxysuccinimide (NHS) esters, which react primarily with lysine residues. One way to expand the current applicability of XLMS into several new areas is to increase the number of cross-links obtainable for a target protein.

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Accessing localized proteomic profiles has emerged as a fundamental strategy to understand the biology of diseases, as recently demonstrated, for example, in the context of determining cancer resection margins with improved precision. Here, we analyze a gastric cancer biopsy sectioned into 10 parts, each one subjected to MudPIT analysis. We introduce a software tool, named Shotgun Imaging Analyzer and inspired in MALDI imaging, to enable the overlaying of a protein's expression heat map on a tissue picture.

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