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.

DiagnoMass: A proteomics hub for pinpointing discriminative spectral clusters.

MOTIVATION: There are several well-established paradigms for identifying and pinpointing discriminative peptides/proteins using shotgun proteomic data; examples are peptide-spectrum matching, de novo sequencing, open searches, and even hybrid approaches. Such an arsenal of complementary paradigms can provide deep data coverage, albeit some unidentified discriminative peptides remain. RESULTS: We present DiagnoMass, software tool that groups similar spectra into spectral clusters and then shortlists those clusters that are discriminative for biological conditions. DiagnoMass then communicates with proteomic tools to attempt the identification of such clusters. We demonstrate the effectiveness of DiagnoMass by analyzing proteomic data from Escherichia coli, Salmonella, and Shigella, listing many high-quality discriminative spectral clusters that had thus far remained unidentified by widely adopted proteomic tools. DiagnoMass can also classify proteomic profiles. We anticipate the use of DiagnoMass as a vital tool for pinpointing biomarkers. AVAILABILITY: DiagnoMass and related documentation, including a usage protocol, are available at http://www.diagnomass.com.

Mechanism of high-mannose N-glycan breakdown and metabolism by Bifidobacterium longum.

Bifidobacteria are early colonizers of the human gut and play central roles in human health and metabolism. To thrive in this competitive niche, these bacteria evolved the capacity to use complex carbohydrates, including mammalian N-glycans. Herein, we elucidated pivotal biochemical steps involved in high-mannose N-glycan utilization by Bifidobacterium longum. After N-glycan release by an endo-ß-N-acetylglucosaminidase, the mannosyl arms are trimmed by the cooperative action of three functionally distinct glycoside hydrolase 38 (GH38) α-mannosidases and a specific GH125 α-1,6-mannosidase. High-resolution cryo-electron microscopy structures revealed that bifidobacterial GH38 α-mannosidases form homotetramers, with the N-terminal jelly roll domain contributing to substrate selectivity. Additionally, an α-glucosidase enables the processing of monoglucosylated N-glycans. Notably, the main degradation product, mannose, is isomerized into fructose before phosphorylation, an unconventional metabolic route connecting it to the bifid shunt pathway. These findings shed light on key molecular mechanisms used by bifidobacteria to use high-mannose N-glycans, a perennial carbon and energy source in the intestinal lumen.

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. We introduce Y.A.D.A. 3.0, a fast deconvolution algorithm that can remove peaks with unacceptable mass defects. Our approach is effective for polypeptides with less than 10 kDa, and its essence can be easily incorporated into any deconvolution algorithm. AVAILABILITY AND IMPLEMENTATION: Y.A.D.A. 3.0 is freely available for academic use at http://patternlabforproteomics.org/yada3. SUPPLEMENTARY INFORMATION: Supplementary information is available at Bioinformatics online.

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. PatternLab V (PLV) is the most comprehensive and crucial update so far, the result of intensive interaction with the proteomics community over several years. All PLV modules have been optimized and its graphical user interface has been completely updated for improved user experience. Major improvements were made to all aspects of the software, ranging from boosting the number of protein identifications to faster extraction of ion chromatograms. PLV provides modules for preparing sequence databases, protein identification, statistical filtering and in-depth result browsing for both labeled and label-free quantitation. The PepExplorer module can even pinpoint de novo sequenced peptides not already present in the database. PLV is of broad applicability and therefore suitable for challenging experimental setups, such as time-course experiments and data handling from unsequenced organisms. PLV interfaces with widely adopted software and community initiatives, e.g., Comet, Skyline, PEAKS and PRIDE. It is freely available at http://www.patternlabforproteomics.org .

New Intracellular Peptide Derived from Hemoglobin Alpha Chain Induces Glucose Uptake and Reduces Blood Glycemia.

Intracellular peptides were shown to derive from proteasomal degradation of proteins from mammalian and yeast cells, being suggested to play distinctive roles both inside and outside these cells. Here, the role of intracellular peptides previously identified from skeletal muscle and adipose tissues of C57BL6/N wild type (WT) and neurolysin knockout mice were investigated. In differentiated C2C12 mouse skeletal muscle cells, some of these intracellular peptides like insulin activated the expression of several genes related to muscle contraction and gluconeogenesis. One of these peptides, LASVSTVLTSKYR (Ric4; 600 µg/kg), administrated either intraperitoneally or orally in WT mice, decreased glycemia. Neither insulin (10 nM) nor Ric4 (100 µM) induced glucose uptake in adipose tissue explants obtained from conditional knockout mice depleted of insulin receptor. Ric4 (100 µM) similarly to insulin (100 nM) induced Glut4 translocation to the plasma membrane of C2C12 differentiated cells, and increased GLUT4 mRNA levels in epididymal adipose tissue of WT mice. Ric4 (100 µM) increased both Erk and Akt phosphorylation in C2C12, as well as in epididymal adipose tissue from WT mice; Erk, but not Akt phosphorylation was activated by Ric4 in tibial skeletal muscle from WT mice. Ric4 is rapidly degraded in vitro by WT liver and kidney crude extracts, such a response that is largely reduced by structural modifications such as N-terminal acetylation, C-terminal amidation, and substitution of Leu8 for DLeu8 (Ac-LASVSTV[DLeu]TSKYR-NH2; Ric4-16). Ric4-16, among several Ric4 derivatives, efficiently induced glucose uptake in differentiated C2C12 cells. Among six Ric4-derivatives evaluated in vivo, Ac-LASVSTVLTSKYR-NH2 (Ric4-2; 600 µg/kg) and Ac-LASVSTV[DLeu]TSKYR (Ric4-15; 600 µg/kg) administrated orally efficiently reduced glycemia in a glucose tolerance test in WT mice. The potential clinical application of Ric4 and Ric4-derivatives deserves further attention.

Xyloglucan processing machinery in Xanthomonas pathogens and its role in the transcriptional activation of virulence factors.

Xyloglucans are highly substituted and recalcitrant polysaccharides found in the primary cell walls of vascular plants, acting as a barrier against pathogens. Here, we reveal that the diverse and economically relevant Xanthomonas bacteria are endowed with a xyloglucan depolymerization machinery that is linked to pathogenesis. Using the citrus canker pathogen as a model organism, we show that this system encompasses distinctive glycoside hydrolases, a modular xyloglucan acetylesterase and specific membrane transporters, demonstrating that plant-associated bacteria employ distinct molecular strategies from commensal gut bacteria to cope with xyloglucans. Notably, the sugars released by this system elicit the expression of several key virulence factors, including the type III secretion system, a membrane-embedded apparatus to deliver effector proteins into the host cells. Together, these findings shed light on the molecular mechanisms underpinning the intricate enzymatic machinery of Xanthomonas to depolymerize xyloglucans and uncover a role for this system in signaling pathways driving pathogenesis.

Structural discrimination analysis for constraint selection in protein modeling.

MOTIVATION: Protein structure modeling can be improved by the use of distance constraints between amino acid residues, provided such data reflects-at least partially-the native tertiary structure of the target system. In fact, only a small subset of the native contact map is necessary to successfully drive the model conformational search, so one important goal is to obtain the set of constraints with the highest true-positive rate, lowest redundancy and greatest amount of information. In this work, we introduce a constraint evaluation and selection method based on the point-biserial correlation coefficient, which utilizes structural information from an ensemble of models to indirectly measure the power of each constraint in biasing the conformational search toward consensus structures. RESULTS: Residue contact maps obtained by direct coupling analysis are systematically improved by means of discriminant analysis, reaching in some cases accuracies often seen only in modern deep-learning-based approaches. When combined with an iterative modeling workflow, the proposed constraint classification optimizes the selection of the constraint set and maximizes the probability of obtaining successful models. The use of discriminant analysis for the valorization of the information of constraint datasets is a general concept with possible applications to other constraint types and modeling problems. AVAILABILITY AND IMPLEMENTATION: MSA for the targets in this work is available on https://github.com/m3g/2021_Bottino_Biserial. Modeling data supporting the findings of this study was generated at the Center for Computing in Engineering and Sciences, and is available from the corresponding author LM on request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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. RESULTS: We present QUIN-XL, a software that uses unsupervised clustering to group cross-link identifications by their quantitative profile across multiple samples. QUIN-XL highlights regions of the protein or system presenting changes in its conformation when comparing different biological conditions. We demonstrate our software's usefulness by revisiting the HSP90 protein, comparing three of its different conformers. QUIN-XL's clusters correlate directly to known protein 3D structures of the conformers and therefore validates our software. AVAILABILITYAND IMPLEMENTATION: QUIN-XL and a user tutorial are freely available at http://patternlabforproteomics.org/quinxl for academic users. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Two distinct catalytic pathways for GH43 xylanolytic enzymes unveiled by X-ray and QM/MM simulations.

Xylanolytic enzymes from glycoside hydrolase family 43 (GH43) are involved in the breakdown of hemicellulose, the second most abundant carbohydrate in plants. Here, we kinetically and mechanistically describe the non-reducing-end xylose-releasing exo-oligoxylanase activity and report the crystal structure of a native GH43 Michaelis complex with its substrate prior to hydrolysis. Two distinct calcium-stabilized conformations of the active site xylosyl unit are found, suggesting two alternative catalytic routes. These results are confirmed by QM/MM simulations that unveil the complete hydrolysis mechanism and identify two possible reaction pathways, involving different transition state conformations for the cleavage of xylooligosaccharides. Such catalytic conformational promiscuity in glycosidases is related to the open architecture of the active site and thus might be extended to other exo-acting enzymes. These findings expand the current general model of catalytic mechanism of glycosidases, a main reaction in nature, and impact on our understanding about their interaction with substrates and inhibitors.

Molecular Architecture of the Antiophidic Protein DM64 and its Binding Specificity to Myotoxin II From Bothrops asper Venom.

DM64 is a toxin-neutralizing serum glycoprotein isolated from Didelphis aurita, an ophiophagous marsupial naturally resistant to snake envenomation. This 64 kDa antitoxin targets myotoxic phospholipases A2, which account for most local tissue damage of viperid snakebites. We investigated the noncovalent complex formed between native DM64 and myotoxin II, a myotoxic phospholipase-like protein from Bothrops asper venom. Analytical ultracentrifugation (AUC) and size exclusion chromatography indicated that DM64 is monomeric in solution and binds equimolar amounts of the toxin. Attempts to crystallize native DM64 for X-ray diffraction were unsuccessful. Obtaining recombinant protein to pursue structural studies was also challenging. Classical molecular modeling techniques were impaired by the lack of templates with more than 25% sequence identity with DM64. An integrative structural biology approach was then applied to generate a three-dimensional model of the inhibitor bound to myotoxin II. I-TASSER individually modeled the five immunoglobulin-like domains of DM64. Distance constraints generated by cross-linking mass spectrometry of the complex guided the docking of DM64 domains to the crystal structure of myotoxin II, using Rosetta. AUC, small-angle X-ray scattering (SAXS), molecular modeling, and molecular dynamics simulations indicated that the DM64-myotoxin II complex is structured, shows flexibility, and has an anisotropic shape. Inter-protein cross-links and limited hydrolysis analyses shed light on the inhibitor's regions involved with toxin interaction, revealing the critical participation of the first, third, and fifth domains of DM64. Our data showed that the fifth domain of DM64 binds to myotoxin II amino-terminal and beta-wing regions. The third domain of the inhibitor acts in a complementary way to the fifth domain. Their binding to these toxin regions presumably precludes dimerization, thus interfering with toxicity, which is related to the quaternary structure of the toxin. The first domain of DM64 interacts with the functional site of the toxin putatively associated with membrane anchorage. We propose that both mechanisms concur to inhibit myotoxin II toxicity by DM64 binding. The present topological characterization of this toxin-antitoxin complex constitutes an essential step toward the rational design of novel peptide-based antivenom therapies targeting snake venom myotoxins.

Xyloglucan processing machinery in Xanthomonas pathogens and its role in the transcriptional activation of virulence factors

Xyloglucans are highly substituted and recalcitrant polysaccharides found in the primary cell walls of vascular plants, acting as a barrier against pathogens. Here, we reveal that the diverse and economically relevant Xanthomonas bacteria are endowed with a xyloglucan depolymerization machinery that is linked to pathogenesis. Using the citrus canker pathogen as a model organism, we show that this system encompasses distinctive glycoside hydrolases, a modular xyloglucan acetylesterase and specific membrane transporters, demonstrating that plant-associated bacteria employ distinct molecular strategies from commensal gut bacteria to cope with xyloglucans. Notably, the sugars released by this system elicit the expression of several key virulence factors, including the type III secretion system, a membrane-embedded apparatus to deliver effector proteins into the host cells. Together, these findings shed light on the molecular mechanisms underpinning the intricate enzymatic machinery of Xanthomonas to depolymerize xyloglucans and uncover a role for this system in signaling pathways driving pathogenesis.

Toward Increased Reliability, Transparency, and Accessibility in Cross-linking Mass Spectrometry.

Cross-linking mass spectrometry (MS) has substantially matured as a method over the past 2 decades through parallel development in multiple labs, demonstrating its applicability to protein structure determination, conformation analysis, and mapping protein interactions in complex mixtures. Cross-linking MS has become a much-appreciated and routinely applied tool, especially in structural biology. Therefore, it is timely that the community commits to the development of methodological and reporting standards. This white paper builds on an open process comprising a number of events at community conferences since 2015 and identifies aspects of Cross-linking MS for which guidelines should be developed as part of a Cross-linking MS standards initiative.

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. (C) The TopN density estimation, for detecting retention time intervals of under or over-sampling, and (D) The chromatography reproducibility module, which provides pairwise comparisons between multiple experiments. RawVegetable, a tutorial, and the example data are freely available for academic use at: http://patternlabforproteomics.org/rawvegetable. SIGNIFICANCE: Chromatography optimization is a critical step for any shotgun proteomic or cross-linking mass spectrometry experiment. Here, we present a nifty solution with several key features, such as displaying individual charge state chromatograms, highlighting chromatographic regions of under- or over-sampling and checking for reproducibility.

Publisher Correction: Structural insights into ß-1,3-glucan cleavage by a glycoside hydrolase family.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.

Structural insights into ß-1,3-glucan cleavage by a glycoside hydrolase family.

The fundamental and assorted roles of ß-1,3-glucans in nature are underpinned on diverse chemistry and molecular structures, demanding sophisticated and intricate enzymatic systems for their processing. In this work, the selectivity and modes of action of a glycoside hydrolase family active on ß-1,3-glucans were systematically investigated combining sequence similarity network, phylogeny, X-ray crystallography, enzyme kinetics, mutagenesis and molecular dynamics. This family exhibits a minimalist and versatile (α/ß)-barrel scaffold, which can harbor distinguishing exo or endo modes of action, including an ancillary-binding site for the anchoring of triple-helical ß-1,3-glucans. The substrate binding occurs via a hydrophobic knuckle complementary to the canonical curved conformation of ß-1,3-glucans or through a substrate conformational change imposed by the active-site topology of some fungal enzymes. Together, these findings expand our understanding of the enzymatic arsenal of bacteria and fungi for the breakdown and modification of ß-1,3-glucans, which can be exploited for biotechnological applications.

The Relevance of Thimet Oligopeptidase in the Regulation of Energy Metabolism and Diet-Induced Obesity.

Thimet oligopeptidase (EC 3.4.24.15; EP24.15; THOP1) is a potential therapeutic target, as it plays key biological functions in processing biologically functional peptides. The structural conformation of THOP1 provides a unique restriction regarding substrate size, in that it only hydrolyzes peptides (optimally, those ranging from eight to 12 amino acids) and not proteins. The proteasome activity of hydrolyzing proteins releases a large number of intracellular peptides, providing THOP1 substrates within cells. The present study aimed to investigate the possible function of THOP1 in the development of diet-induced obesity (DIO) and insulin resistance by utilizing a murine model of hyperlipidic DIO with both C57BL6 wild-type (WT) and THOP1 null (THOP1-/-) mice. After 24 weeks of being fed a hyperlipidic diet (HD), THOP1-/- and WT mice ingested similar chow and calories; however, the THOP1-/- mice gained 75% less body weight and showed neither insulin resistance nor non-alcoholic fatty liver steatosis when compared to WT mice. THOP1-/- mice had increased adrenergic-stimulated adipose tissue lipolysis as well as a balanced level of expression of genes and microRNAs associated with energy metabolism, adipogenesis, or inflammation. Altogether, these differences converge to a healthy phenotype of THOP1-/- fed a HD. The molecular mechanism that links THOP1 to energy metabolism is suggested herein to involve intracellular peptides, of which the relative levels were identified to change in the adipose tissue of WT and THOP1-/- mice. Intracellular peptides were observed by molecular modeling to interact with both pre-miR-143 and pre-miR-222, suggesting a possible novel regulatory mechanism for gene expression. Therefore, we successfully demonstrated the previously unanticipated relevance of THOP1 in energy metabolism regulation. It was suggested that intracellular peptides were responsible for mediating the phenotypic differences that are described herein by a yet unknown mechanism of action.

Structural complementarity of distance constraints obtained from chemical cross-linking and amino acid coevolution.

The analysis of amino acid coevolution has emerged as a practical method for protein structural modeling by providing structural contact information from alignments of amino acid sequences. In parallel, chemical cross-linking/mass spectrometry (XLMS) has gained attention as a universally applicable method for obtaining low-resolution distance constraints to model the quaternary arrangements of proteins, and more recently even protein tertiary structures. Here, we show that the structural information obtained by XLMS and coevolutionary analysis are effectively complementary: the distance constraints obtained by each method are almost exclusively associated with non-coincident pairs of residues, and modeling results obtained by the combination of both sets are improved relative to considering the same total number of constraints of a single type. The structural rationale behind the complementarity of the distance constraints is discussed and illustrated for a representative set of proteins with different sizes and folds.