Spectra Of Peptides Research Articles

Mechanistic Insight into the Role of Peptides Secreted from Bacillus clausii and Future Opportunities.

Bacillus clausii is a commercial spore probiotic known to treat multiple diseases. An increased interest in exploring the nutraceutical and probiotic properties of various microorganisms has made researchers explore more about these bacteria. The current trends in the healthcare industry are majorly focused on devising new therapies to avoid drug and pathogen resistance in patients. Antimicrobial peptides have been considered a source of antibiotics for a long time. Still, getting new therapies into the market is a big challenge. Members of the genus Bacillus have been reported to have a broad spectrum of antimicrobial peptides. One of the least explored species under this genus is Bacillus clausii, concerning peptide drug therapy. The applications of Bacillus clausii in treating or preventing gut dysbiosis and respiratory infections have been largely supported in the past two decades. Yet research is lacking in explaining the pathways at molecular levels in targeting pathogens. In this mini-review, we are going to summarise the research that has been reported so far about peptide extraction from Bacillus clausii, their mode of action and advantages to mankind, and the challenges lying in the isolation of peptides.

Development of Peptide Identification System for ToF-SIMS Spectra Using Supervised Machine Learning.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) data interpretation for organic materials is complicated because of various fragment ions produced from each molecule and the overlapping of certain mass peaks from different molecules. Fragmentation mechanisms in SIMS are complex because different sputtering and ionization processes can simultaneously occur. Therefore, a prediction system that can identify materials in a sample is required. A novel prediction system for peptides based on ToF-SIMS and amino-acid-based teaching information (labels) for supervised machine learning was developed. To develop the prediction system for general organic materials, the annotation of materials is crucial to creating effective labels for supervised learning. Peptides are composed of 20 amino acid residues, which can be used as labels. We previously developed a peptide prediction system using Random Forest, a supervised machine-learning method. However, only the amino acids contained in the target peptide were predicted, and the amino acid sequence was unable to be assumed. In this study, the amino acid sequence of the test peptide was determined by adding the information on two adjacent amino acids to the labels. Once the prediction system learned the target peptide spectra, the peptides in the newly obtained ToF-SIMS spectra could be identified. The new prediction system also provides useful information for the identification of unknown peptides. The prediction results indicate that two adjacent permutations of amino acids are effective pieces of teaching information for expressing the amino acid sequence of a peptide.

Mass Spectral Feature Analysis of Ubiquitylated Peptides Provides Insights into Probing the Dark Ubiquitylome.

Ubiquitylation is a structurally and functionally diverse post-translational modification that involves the covalent attachment of the small protein ubiquitin to other protein substrates. Trypsin-based proteomics is the most common approach for globally identifying ubiquitylation sites. However, we estimate that such methods are unable to detect ∼40% of ubiquitylation sites in the human proteome, i.e., "the dark ubiquitylome", including many important for human health and disease. In this meta-analysis of three large ubiquitylomic data sets, we performed a series of bioinformatic analyses to assess experimental features that could aid in uniquely identifying site-specific ubiquitylation events. Spectral predictions from Prosit were compared to experimental spectra of tryptic ubiquitylated peptides, revealing previously uncharacterized fragmentation of the diGly scar. Analysis of the LysC-derived ubiquitylated peptides reveals systematic, multidimensional peptide fragmentation, including diagnostic b-ions from fragmentation of the LysC ubiquitin scar. Comprehensively, these findings provide diagnostic spectral signatures of modification events that could be applied to new analysis methods for nontryptic ubiquitylomics.

PRODUCTION OF RECOMBINANT SALMONELLA ABORTUS EQUI PROTEINS

The cause of mare abortions of salmonellosis etiology is pathogen Salmonella abortusequi. The economic damage caused by this infection is due to the loss of reproductive capacity of the sires, litter failure, reduced productivity and veterinary costs. High level of infection of horses is registered in many countries, including Kazakhstan. The main method of diagnosis is bacteriological, but its disadvantages are low sensitivity and duration of results. The OIE recommends the use of PCR and ELISA for diagnosis of infection, however, due to the high cost of test systems, the use of PCR in veterinary laboratories is not always justified, and the effectiveness of the ELISA method depends largely on the structure and purity of the antigen. Currently, genetic engineering methods make it possible to obtain recombinant antigens of animal pathogens containing separate proteins, which excludes the occurrence of nonspecific reactions. These antigens can be used in tests for serological diagnostics, immunization of laboratory animals, and in the development of vaccines. According to literature data, it is known that diagnostically significant antigens in bacteria of the genus Salmonella are outer membrane proteins. Therefore, the outer membrane protein OmpX of S. abortusequi, which has high immunogenicity and conservativeness was selected as a target. The aim of this work is to obtain recombinant OmpX antigens of S. abortusequi and study their properties. As a result, E. coli strains producing recombinant OmpX protein of S. abortusequi were created. The synthesized genes were cloned into expression vectors and transformed into competent E. coli BL21 cells. The rOmpX of Salmonella abortusequi was produced and purified; electrophoretic analysis showed the purity of the preparation with a molecular mass of 23 kDa. The recombinant protein was analyzed by LC/MC-MC spectrometry, and peptides containing QTTDYPTYKHDTSDYGFSYGAGLQFN amino acid sequences were identified. A search of the peptide spectrum in the SwissProt database showed that they corresponded to the OmpX protein of Salmonella abortusequi. To determine the quality of protein refolding and the conservation of antigenic epitopes of the protein, Western blot was used with control positive sera that specifically reacted with a protein of molecular mass 23kDa. The resulting recombinant OmpX protein was then used as an antigen in an indirect ELISA to detect antibodies in serum samples from aborted mares. The results of n-ELISA showed the presence of antibodies in serum at a titer of 1:1600 (+26.6; - 21.1). Thus, rOmpX of S. abortusequiwith a molecular mass of 23 kDa was obtained and its main properties were studied. The correctness of protein refolding was confirmed by Western blot and ELISA with positive control sera. The specificity of recombinant proteins was proved by interaction with positive serum samples of horses from unfavorable farms. The obtained recombinant antigens can be used in the creation of tests for the diagnosis of salmonellosis abortion of horses in the Republic of Kazakhstan. WPM с добавлением БАП 1,0 мг/л, ИМК 0,1 мг/л, ГК 0,2 мг/л, где было в среднем образовано 7,36 микропобегов на один эксплант.

Dear-PSM: A deep learning-based peptide search engine enables full database search for proteomics.

Peptide spectrum matching is the process of linking mass spectrometry data with peptide sequences. An experimental spectrum can match thousands of candidate peptides with variable modifications leading to an exponential increase in candidates. Completing the search within a limited time is a key challenge. Traditional searches expedite the process by restricting peptide mass errors and variable modifications, but this limits interpretive capability. To address this challenge, we propose Dear-PSM, a peptide search engine that supports full database searching. Dear-PSM does not restrict peptide mass errors, matching each spectrum to all peptides in the database and increasing the number of variable modifications per peptide from the conventional 3-20. Leveraging inverted index technology, Dear-PSM creates a high-performance index table of experimental spectra and utilizes deep learning algorithms for peptide validation. Through these techniques, Dear-PSM achieves a speed breakthrough 7 times faster than mainstream search engines on a regular desktop computer, with a remarkable 240-fold reduction in memory consumption. Benchmark test results demonstrate that Dear-PSM, in full database search mode, can reproduce over 90% of the results obtained by mainstream search engines when handling complex mass spectrometry data collected from different species using various instruments. Furthermore, it uncovers a substantial number of new peptides and proteins. Dear-PSM has been publicly released on the GitHub repository https://github.com/jianweishuai/Dear-PSM.

Peptidomic analysis reveals novel peptide PDLC promotes cell proliferation in hepatocellular carcinoma via Ras/Raf/MEK/ERK pathway

Hepatocellular carcinoma (HCC) still presents poor prognosis with low overall survival rates and limited therapeutic options available. Recently, attention has been drawn to peptidomic analysis, an emerging field of proteomics for the exploration of new potential peptide drugs for the treatment of various diseases. However, research on the potential function of HCC peptides is lacking. Here, we analyzed the peptide spectrum in HCC tissues using peptidomic techniques and explored the potentially beneficial peptides involved in HCC. Changes in peptide profiles in HCC were examined using liquid chromatography-mass spectrometry (LC–MS/MS). Analyze the physicochemical properties and function of differently expressed peptides using bioinformatics. The effect of candidate functional peptides on HCC cell growth and migration was evaluated using the CCK-8, colony formation, and transwell assays. Transcriptome sequencing analysis and western blot were employed to delve into the mode of action of potential peptide on HCC. Peptidomic analysis of HCC tissue yielded a total of 8683 peptides, of which 452 exhibited up-regulation and 362 showed down-regulation. The peptides that were differentially expressed, according to bioinformatic analysis, were closely linked to carbon metabolism and the mitochondrial inner membrane. The peptide functional validation identified a novel peptide, PDLC (peptide derived from liver cancer), which was found to dramatically boost HCC cell proliferation through the Ras/Raf/MEK/ERK signaling cascade. Our research defined the peptide’s properties and pattern of expression in HCC and identified a novel peptide, PDLC, with a function in encouraging HCC progression, offering an entirely new potential therapeutic target the disease.

SpecEncoder: deep metric learning for accurate peptide identification in proteomics.

Tandem mass spectrometry (MS/MS) is a crucial technology for large-scale proteomic analysis. The protein database search or the spectral library search are commonly used for peptide identification from MS/MS spectra, which, however, may face challenges due to experimental variations between replicated spectra and similar fragmentation patterns among distinct peptides. To address this challenge, we present SpecEncoder, a deep metric learning approach to address these challenges by transforming MS/MS spectra into robust and sensitive embedding vectors in a latent space. The SpecEncoder model can also embed predicted MS/MS spectra of peptides, enabling a hybrid search approach that combines spectral library and protein database searches for peptide identification. We evaluated SpecEncoder on three large human proteomics datasets, and the results showed a consistent improvement in peptide identification. For spectral library search, SpecEncoder identifies 1%-2% more unique peptides (and PSMs) than SpectraST. For protein database search, it identifies 6%-15% more unique peptides than MSGF+ enhanced by Percolator, Furthermore, SpecEncoder identified 6%-12% additional unique peptides when utilizing a combined library of experimental and predicted spectra. SpecEncoder can also identify more peptides when compared to deep-learning enhanced methods (MSFragger boosted by MSBooster). These results demonstrate SpecEncoder's potential to enhance peptide identification for proteomic data analyses. The source code and scripts for SpecEncoder and peptide identification are available on GitHub at https://github.com/lkytal/SpecEncoder. Contact: hatang@iu.edu.

Comparative analysis of plasma affinity depletion methods: Impact on protein composition and phosphopeptide abundance in human plasma.

Affinity-based protein depletion and TiO2 enrichment methods play a crucial role in detection of low-abundant proteins and phosphopeptides enrichment, respectively. Here, we assessed the effectiveness of HSA/IgG (HU2) and Human 7 (HU7) depletion methods and their impact on phosphopeptides coverage through comparative proteome analysis, utilizing in-solution digestion and nano-LC-Orbitrap mass spectrometry (MS). Our results demonstrated that both HU2 and HU7 affinity depletion significantly decreased high-abundant proteins by 1.5-7.8-fold (p<0.001). A total of 1491 proteins were identified, with 48 proteins showing significant expression in the depleted groups. Notably, cadherin-13, neutrophil defensin 1, APM1, and desmoplakin variant protein were exclusively detected in the HU2/HU7-depleted groups. Furthermore, study on effect of depletion on phosphopeptides revealed an increase in tandem MS spectral counts with notable decrease (∼50%) in peptide spectrum matching in depleted groups, which was attributed to significant reduction in protein counts. Our post translation modification workflow for phosphoproteomics detected 42 phosphorylated peptides, corresponding to 12 phosphoproteins with unique peptide match ≥2 (high false discovery rates confidence). Among them, 10 phosphorylated proteins are highly expressed in depleted groups. Overall, these findings offer valuable insights in selection of protein depletion methods for comprehensive plasma proteomics analysis.

Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification

Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.

Gene expression of antimicrobial peptides in rat intestine under conditions of chronic stress

BACKGROUND: Severe stress causes an array of dysfunctions in the immune, neuroendocrine, cardiovascular, digestive and other systems, resulting in an emergence of various types of pathology. Common manifestations of a chronic stress are the disorders in the gastrointestinal tract, such as irritable bowel syndrome, functional dyspepsia, biliary dyskinesia, dysbiosis, inflammatory processes that determine the development of gastritis and one of the most widespread post-stress pathologies of the gastrointestinal tract — stomach ulcers. The disclosure of the molecular mechanisms of a pathogenesis of diseases associated with gastrointestinal dysfunction related to chronic stress as well as a search for new ways to correct these disorders are important tasks of fundamental and clinical medicine. The present work is focused on evaluating a participation of molecular factors of the innate immunity in intestine, such as antimicrobial peptides secreted by intestinal epithelial cells upon infection, in a response to the chronic stress. AIM: The aim of the study was to estimate the gene expression of a number of antimicrobial peptides: intestinal α- and β-defensins of laboratory animals (rats) under chronic stress conditions. MATERIALS AND METHODS: Modeling of a chronic stress was performed by daily forced swimming of laboratory animals in cold water. An expression of α- and β-defensin genes was evaluated using a real-time polymerase chain reaction. RESULTS: We found an increase in the level of expression of the rat α-defensin-5 and β-defensin-3 genes in response to chronic stress, while the expression of β-defensin-2 gene was not changed compared to the control. CONCLUSIONS: Considering that changes in the concentration and spectrum of peptides with antibacterial activity, caused by prolonged stress, can contribute to modification of the composition of the intestinal microbiota, the data obtained can expand our understanding of the molecular basis of the pathogenesis of diseases associated with disorders in the composition of microbiota under stress.

Dual role of the peptide-loading complex as proofreader and limiter of MHC-I presentation

Antigen presentation via major histocompatibility complex class I (MHC-I) molecules is essential for surveillance by the adaptive immune system. Central to this process is the peptide-loading complex (PLC), which translocates peptides from the cytosol to the endoplasmic reticulum and catalyzes peptide loading and proofreading of peptide-MHC-I (pMHC-I) complexes. Despite its importance, the impact of individual PLC components on the presented pMHC-I complexes is still insufficiently understood. Here, we used stoichiometrically defined antibody-nanobody complexes and engineered soluble T cell receptors (sTCRs) to quantify different MHC-I allomorphs and defined pMHC-I complexes, respectively. Thereby, we uncovered distinct effects of individual PLC components on the pMHC-I surface pool. Knockouts of components of the PLC editing modules, namely tapasin, ERp57, or calreticulin, changed the MHC-I surface composition to a reduced proportion of HLA-A*02:01 presentation compensated by a higher ratio of HLA-B*40:01 molecules. Intriguingly, these knockouts not only increased the presentation of suboptimally loaded HLA-A*02:01 complexes but also elevated the presentation of high-affinity peptides overexpressed in the cytosol. Our findings suggest that the components of the PLC editing module serve a dual role, acting not only as peptide proofreaders but also as limiters for abundant peptides. This dual function ensures the presentation of a broad spectrum of antigenic peptides.

FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells.

The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.

Mass spectrometry imaging protocol for spatial mapping of lipids, N-glycans and peptides in murine lung tissue.

The application of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to murine lungs is challenging due to the spongy nature of the tissue. Lungs consist of interconnected air sacs (alveoli) lined by a single layer of flattened epithelial cells, which requires inflation to maintain its natural structure. Therefore, a protocol that is compatible with both lung instillation and high spatial resolution is essential to enable multi-omic studies on murine lung disease models using MALDI-MSI. To maintain the structural integrity of the tissue, murine lungs were inflated with 8% (w/v) gelatin for lipid MSI of fresh frozen tissues or 4% (v/v) paraformaldehyde neutral buffer for N-glycan and peptide MSI of FFPE tissues. Tissues were sectioned and prepared for enzymatic digestion and/or matrix deposition. Glycerol-free PNGase F was applied for N-glycan MSI, while Trypsin Gold was applied for peptide MSI using the iMatrixSpray and ImagePrep Station, respectively. For lipid, N-glycan and peptide MSI, α-cyano-4-hydroxycinnamic acid matrix was deposited using the iMatrixSpray. MS data were acquired with 20 μm spatial resolution using a timsTOF fleX MS instrument followed by MS fragmentation of lipids, N-glycans and peptides. For lipid MSI, trapped ion mobility spectrometry was used to separate isomeric/isobaric lipid species. SCiLS™ Lab was used to visualize all MSI data. For analyte identification, MetaboScape®, GlycoMod and Mascot were used to annotate MS fragmentation spectra of lipids, N-glycans and tryptic peptides, respectively. Our protocol provides instructions on sample preparation for high spatial resolution MALDI-MSI, MS/MS data acquisition and lipid, N-glycan and peptide annotation and identification from murine lungs. This protocol will allow non-biased analyses of diseased lungs from preclinical murine models and provide further insight into disease models.

Comprehensive shotgun proteomic characterization and virulence factors of seafood spoilage bacteria

This article summarizes the characterization, by shotgun proteomics, of 11 bacterial strains identified as responsible for seafood spoilage. A total of 4455 peptide spectrum matches, corresponding to 4299 peptides and 3817 proteins were identified. Analyses of data determined the functional pathways they are involved in. The proteins identified were integrated into a protein-protein network that involves 371 nodes and 3016 edges. Those proteins are implicated in energy pathways, peptidoglycan biosynthesis, spermidine/putrescine metabolism. An additional 773 peptides were characterized as virulence factors, that participates in bacterial pathogenesis; while 14 peptides were defined as biomarkers, as they can be used to differentiate the bacterial species present. This report represents the most extensive proteomic repository available in the field of seafood spoilage bacteria; the data substantially advances the understanding of seafood decay, as well as provides fundamental bases for the recognition of the bacteria existent in seafood that cause spoilage during food processing/storage.

Prediction of glycopeptide fragment mass spectra by deep learning

Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.

Challenges in the discovery of tumor-specific alternative splicing-derived cell-surface antigens in glioma

Despite advancements in cancer immunotherapy, solid tumors remain formidable challenges. In glioma, profound inter- and intra-tumoral heterogeneity of antigen landscape hampers therapeutic development. Therefore, it is critical to consider alternative sources to expand the repertoire of targetable (neo-)antigens and improve therapeutic outcomes. Accumulating evidence suggests that tumor-specific alternative splicing (AS) could be an untapped reservoir of antigens. In this study, we investigated tumor-specific AS events in glioma, focusing on those predicted to generate major histocompatibility complex (MHC)-presentation-independent, cell-surface antigens that could be targeted by antibodies and chimeric antigen receptor-T cells. We systematically analyzed bulk RNA-sequencing datasets comparing 429 tumor samples (from The Cancer Genome Atlas) and 9166 normal tissue samples (from the Genotype-Tissue Expression project), and identified 13 AS events in 7 genes predicted to be expressed in more than 10% of the patients, including PTPRZ1 and BCAN, which were corroborated by an external RNA-sequencing dataset. Subsequently, we validated our predictions and elucidated the complexity of the isoforms using full-length transcript amplicon sequencing on patient-derived glioblastoma cells. However, analyses of the RNA-sequencing datasets of spatially mapped and longitudinally collected clinical tumor samples unveiled remarkable spatiotemporal heterogeneity of the candidate AS events. Furthermore, proteomics analysis did not reveal any peptide spectra matching the putative antigens. Our investigation illustrated the diverse characteristics of the tumor-specific AS events and the challenges of antigen exploration due to their notable spatiotemporal heterogeneity and elusive nature at the protein levels. Redirecting future efforts toward intracellular, MHC-presented antigens could offer a more viable avenue.

Unlocking Antibacterial Potential: Key-Site-Based Regulation of Antibacterial Spectrum of Peptides.

In the pursuit of combating multidrug-resistant bacteria, antimicrobial peptides (AMPs) have emerged as promising agents; however, their application in clinical settings still presents challenges. Specifically, the exploration of crucial structural parameters that influence the antibacterial spectrum of AMPs and the subsequent development of tailored variants with either broad- or narrow-spectrum characteristics to address diverse clinical therapeutic needs has been overlooked. This study focused on investigating the effects of amino acid sites and hydrophobicity on the peptide's antibacterial spectrum through Ala scanning and fixed-point hydrophobic amino acid substitution techniques. The findings revealed that specific amino acid sites played a pivotal role in determining the antibacterial spectrum of AMPs and confirmed that broadening the spectrum could be achieved only by increasing hydrophobicity at certain positions. In conclusion, this research provided a theoretical basis for future precise regulation of an antimicrobial peptide's spectrum by emphasizing the intricate balance between amino acid sites and hydrophobicity.

Discovery of Species-Specific Proteotypic Peptides To Establish a Spectral Library Platform for Identification of Nontuberculosis Mycobacteria from Mass Spectrometry-Based Proteomics.

Nontuberculous mycobacteria are opportunistic bacteria pulmonary and extra-pulmonary infections in humans that closely resemble Mycobacterium tuberculosis. Although genome sequencing strategies helped determine NTMs, a common assay for the detection of coinfection by multiple NTMs with M. tuberculosis in the primary attempt of diagnosis is still elusive. Such a lack of efficiency leads to delayed therapy, an inappropriate choice of drugs, drug resistance, disease complications, morbidity, and mortality. Although a high-resolution LC-MS/MS-based multiprotein panel assay can be developed due to its specificity and sensitivity, it needs a library of species-specific peptides as a platform. Toward this, we performed an analysis of proteomes of 9 NTM species with more than 20 million peptide spectrum matches gathered from 26 proteome data sets. Our metaproteomic analyses determined 48,172 species-specific proteotypic peptides across 9 NTMs. Notably, M. smegmatis (26,008), M. abscessus (12,442), M. vaccae (6487), M. fortuitum (1623), M. avium subsp. paratuberculosis (844), M. avium subsp. hominissuis (580), and M. marinum (112) displayed >100 species-specific proteotypic peptides. Finally, these peptides and corresponding spectra have been compiled into a spectral library, FASTA, and JSON formats for future reference and validation in clinical cohorts by the biomedical community for further translation.

Mass spectrometry-based quantification of immunostimulatory gliadin proteins and peptides in coloured wheat varieties: Implications for Celiac Disease

Pigmented wheat varieties (Triticum aestivum spp.) are getting increasingly popular in modern nutrition and thoroughly researched for their functional and nutraceutical value. The colour of these wheat grains is caused by the expression of natural pigments, including carotenoids and anthocyanins, that can be restricted to either the endosperm, pericarp and/or aleurone layers. While contrasts in phytochemical synthesis give rise to variations among purple, blue, dark and yellow grain’s antioxidant and radical scavenging capacities, little is known about their influence on gluten proteins expression, digestibility and immunogenic potential in a Celiac Disease (CD) framework. Herein, it has been found that the expression profile and immunogenic properties of gliadin proteins in pigmented wheat grains might be affected by anthocyanins and carotenoids upregulation, and that the spectra of peptide released upon simulated gastrointestinal digestion is also significantly different. Interestingly, anthocyanin accumulation, as opposed to carotenoids, correlated with a lower immunogenicity and toxicity of gliadins at both protein and peptide levels. Altogether, this study provides first-level evidence on the impact modern breeding practices, seeking higher expression levels of health promoting phytochemicals at the grain level, may have on wheat crops functionality and CD tolerability.

Structure and dynamics of the proton-selective histidine and the gating tryptophan in an inward rectifying hybrid influenza B and A virus M2 proton channel.

The M2 proteins of influenza A and B viruses form acid-activated proton channels that are essential for the virus lifecycle. Proton selectivity is achieved by a transmembrane (TM) histidine whereas gating is achieved by a tryptophan residue. Although this functional apparatus is conserved between AM2 and BM2 channels, AM2 conducts protons exclusively inward whereas BM2 conducts protons in either direction depending on the pH gradient. Previous studies showed that in AM2, mutations of D44 abolished inward rectification of AM2, suggesting that the tryptophan gate is destabilized. To elucidate how charged residues C-terminal to the tryptophan regulates channel gating, here we investigate the structure and dynamics of H19 and W23 in a BM2 mutant, GDR-BM2, in which three BM2 residues are mutated to the corresponding AM2 residues, S16G, G26D and H27R. Whole-cell electrophysiological data show that GDR-BM2 conducts protons with inward rectification, identical to wild-type (WT) AM2 but different from WT-BM2. Solid-state NMR 15N and 13C spectra of H19 indicate that the mutant BM2 channel contains higher populations of cationic histidine and neutral τ tautomers compared to WT-BM2 at acidic pH. Moreover, 19F NMR spectra of 5-19F-labeled W23 resolve three peaks at acidic pH, suggesting three tryptophan sidechain conformations. Comparison of these spectra with the tryptophan spectra of other M2 peptides suggests that these indole sidechain conformations arise from interactions with the C-terminal charged residues and with the N-terminal cationic histidine. Taken together, these solid-state NMR data show that inward rectification in M2 proton channels is accomplished by tryptophan interactions with charged residues on both its C-terminal and N-terminal sides. Gating of these M2 proton channels is thus accomplished by a multi-residue complex with finely tuned electrostatic and aromatic interactions.