Spectra Of Peptides Research Articles

Different Responses of ESBL Indicative Peptide Spectra to Various β- Lactam Exposures Among Community Acquired Urinary Tract Infected Escherichia coli by Using the MALDI-TOF Technique

Abstract The study aims to identify Escherichia coli specific and Extended Spectrum β- Lactamase (ESBL) indicative peptide spectra when co-exposed with ceftazidime or other beta-lactams with clavulanic acid in ESBL producing isolates in community acquired urinary tract infected E. coli by Matrix-Assisted Laser Desorption/Ionization Time - of - Flight Mass Spectrometry (MALDI-TOF) technique. Among 100 tested E. coli isolates, 13 antibiotics were used to profile the resistant isolates. They were mainly demonstrated the resistance to ampicillin, cefoxitin, cefotaxime, ceftazidime, amoxicillin/clavulanic acid, and tetracycline. The high-, low-, and non-ESBL producers, 43.86%, 40.35% and 15.79%, were classified from these resistant isolates according to the ratio of MIC fold between ceftazidime and amoxicillin/clavulanic acid by E-test. Using the MALDI-TOF technique, 5 E. coli -specific peptide spectra were identified which were located at 4362, 4531, 5380, 6254, and 9063 Da using a cut-off value of 60% of frequency. The significant intensity reduction of spectra at 10477, and 5096 Da were recorded and suspected as ESBL indicative peptide spectra after the exposure to ceftazidime or cefotaxime alone and concomitantly with amoxicillin/clavulanic acid in the high and low ESBL producers, respectively. The further significant spectra located at 2548, and 8371 and 9713 Da were exclusively related to ceftazidime hydrolysis in the low and high ESBL producers, respectively. There were 3 and 4 significant peptide spectra which were located at 5968, 7153, 9713 and 10477 Da, and 2548, 5096, and 9537 Da were also suspected as being ESBL spectra after exposure to other tested β-lactams in the high and low-ESBL producers, respectively. Keywords: Extended Spectrum β-Lactamase; E. coli; MALDI-TOF, Peptide spectra; β-lactams

A complete workflow for discovering small bioactive peptides in foods by LC-MS/MS: A case study on almonds

Identification of bioactive peptides is an increasingly important target for food chemists, particularly in consideration of the widespread application of proteolytic enzymes in food processing. Because the characterization of small peptides by LC-MS/MS is challenging, we optimized a dimethyl labeling technique to facilitate small peptide identification, using almond proteins as a model. The method was validated by comparing the MS/MS spectra of standards and almond-derived peptides in their nonderivatized and derivatized forms. Signal enhancement of a1 ions was proved to effectively aid in the full-length sequencing of small peptides. We further validated this method using two industrially-relevant protein-rich extracts from almond flour: 1737 medium-sized peptides (5–39 amino acids) and 843 small peptides (2–4 amino acids) were identified. The use of an online bioactive peptide database, complemented by the existing literature, allowed the discovery of 208 small bioactive peptides, whereas for medium-sized peptides, only one was reported being bioactive.

Immunodominant Cytomegalovirus Epitopes Suppress Subdominant Epitopes in the Generation of High-Avidity CD8 T Cells

CD8+ T-cell responses to pathogens are directed against infected cells that present pathogen-encoded peptides on MHC class-I molecules. Although natural responses are polyclonal, the spectrum of peptides that qualify for epitopes is remarkably small even for pathogens with high coding capacity. Among those few that are successful at all, a hierarchy exists in the magnitude of the response that they elicit in terms of numbers of CD8+ T cells generated. This led to a classification into immunodominant and non-immunodominant or subordinate epitopes, IDEs and non-IDEs, respectively. IDEs are favored in the design of vaccines and are chosen for CD8+ T-cell immunotherapy. Using murine cytomegalovirus as a model, we provide evidence to conclude that epitope hierarchy reflects competition on the level of antigen recognition. Notably, high-avidity cells specific for non-IDEs were found to expand only when IDEs were deleted. This may be a host’s back-up strategy to avoid viral immune escape through antigenic drift caused by IDE mutations. Importantly, our results are relevant for the design of vaccines based on cytomegaloviruses as vectors to generate high-avidity CD8+ T-cell memory specific for unrelated pathogens or tumors. We propose the deletion of vector-encoded IDEs to avoid the suppression of epitopes of the vaccine target.

MS_Piano: A Software Tool for Annotating Peaks in CID Tandem Mass Spectra of Peptides and N-Glycopeptides.

Annotating product ion peaks in tandem mass spectra is essential for evaluating spectral quality and validating peptide identification. This task is more complex for glycopeptides and is crucial for the confident determination of glycosylation sites in glycoproteins. MS_Piano (Mass Spectrum Peptide Annotation) software was developed for reliable annotation of peaks in collision induced dissociation (CID) tandem mass spectra of peptides or N-glycopeptides for given peptide sequences, charge states, and optional modifications. The program annotates each peak in high or low resolution spectra with possible product ion(s) and the mass difference between the measured and theoretical m/z values. Spectral quality is measured by two major parameters: the ratio between the sum of unannotated vs all peak intensities in the top 20 peaks, and the intensity of the highest unannotated peak. The product ions of peptides, glycans, and glycopeptides in spectra are labeled in different class-type colors to facilitate interpretation. MS_Piano assists validating peptide and N-glycopeptide identification from database and library searches and provides quality control and optimizes search reliability in custom developed peptide mass spectral libraries. The software is freely available in .exe and .dll formats for the Windows operating system.

Evaluation of mass spectrometry MS/MS spectra for the presence of isopeptide crosslinked peptides.

Isopeptide crosslinked proteins can be the product of transglutaminase or of exposure to organophosphorus toxicants (OP). Transglutaminase links glutamine to lysine with loss of ammonia. OP toxicants induce a link between glutamic acid and lysine with loss of water. Our goal was to establish criteria to distinguish real from false isopeptide crosslinks reported by software searches of mass spectrometry data. We used fragmentation spectra of tryptic peptides from MAP-rich tubulin Sus scrofa as a test system for detection of naturally-occurring isopeptide crosslinks. Data were analyzed with Protein Prospector. Criteria for the assignments included the presence of at least 1 crosslink specific product ion, fragment ions from both peptides, Protein Prospector scores ≥20, and best fit of the MS/MS data to the crosslinked peptide as opposed to a linear peptide. Out of 301,364 spectra, 15 potential transglutaminase-type crosslinked peptide candidates were identified. Manual evaluation of these MS/MS spectra reduced the number to 1 valid crosslink between Q112 of NFH and K368 of Tau. Immunopurification with anti-isopeptide 81D1C2 confirmed that MAP-rich tubulin contained only one isopeptide. Support for this isopeptide bond was obtained by showing that transglutaminase was capable of incorporating dansyl-aminohexyl -QQIV into K368. A model of the KIETHK-QLEAHNR isopeptide was synthesized with the aid of transglutaminase. MS/MS spectra of the model validated our interpretation of the native isopeptide. An OP-induced isopeptide bond between K163 of tubulin alpha-1A and E158 of tubulin beta-4B was induced by treating MAP-rich tubulin with 100 μM chlorpyrifos oxon. This crosslink was supported by the criteria described above and by the presence of diethoxyphospho-lysine 163 in the tubulin alpha-1A peptide. The information obtained in this work is valuable for future studies that aim to understand why exposure to OP is associated with increased risk of neurodegenerative disease.

MassSpecBlocks: a web-based tool to create building blocks and sequences of nonribosomal peptides and polyketides for tandem mass spectra analysis

Nonribosomal peptides and polyketides are natural products commonly synthesized by microorganisms. They are widely used in medicine, agriculture, environmental protection, and other fields. The structures of natural products are often analyzed by high-resolution tandem mass spectrometry, which becomes more popular with its increasing availability. However, the characterization of nonribosomal peptides and polyketides from tandem mass spectra is a nontrivial task because they are composed of many uncommon building blocks in addition to proteinogenic amino acids. Moreover, many of them have cyclic and branch-cyclic structures. Here, we introduce MassSpecBlocks – an open-source and web-based tool that converts the input chemical structures in SMILES format into sequences of building blocks. The structures can be searched in public databases PubChem, ChemSpider, ChEBI, NP Atlas, COCONUT, and Norine and edited in a user-friendly graphical interface. Although MassSpecBlocks can serve as a stand-alone database, our primary goal was to enable easy construction of custom sequence and building block databases, which can be used to annotate mass spectra in CycloBranch software. CycloBranch is an open-source, cross-platform, and stand-alone tool that we recently released for annotating spectra of linear, cyclic, branched, and branch-cyclic nonribosomal peptides and polyketide siderophores. The sequences and building blocks created in MassSpecBlocks can be easily exported into a plain text format used by CycloBranch. MassSpecBlocks is available online or can be installed entirely offline. It offers a REST API to cooperate with other tools.

The Human Melanoma Proteome Atlas-Complementing the melanoma transcriptome.

The MM500 meta‐study aims to establish a knowledge basis of the tumor proteome to serve as a complement to genome and transcriptome studies. Somatic mutations and their effect on the transcriptome have been extensively characterized in melanoma. However, the effects of these genetic changes on the proteomic landscape and the impact on cellular processes in melanoma remain poorly understood. In this study, the quantitative mass‐spectrometry‐based proteomic analysis is interfaced with pathological tumor characterization, and associated with clinical data. The melanoma proteome landscape, obtained by the analysis of 505 well‐annotated melanoma tumor samples, is defined based on almost 16 000 proteins, including mutated proteoforms of driver genes. More than 50 million MS/MS spectra were analyzed, resulting in approximately 13,6 million peptide spectrum matches (PSMs). Altogether 13 176 protein‐coding genes, represented by 366 172 peptides, in addition to 52 000 phosphorylation sites, and 4 400 acetylation sites were successfully annotated. This data covers 65% and 74% of the predicted and identified human proteome, respectively. A high degree of correlation (Pearson, up to 0.54) with the melanoma transcriptome of the TCGA repository, with an overlap of 12 751 gene products, was found.Mapping of the expressed proteins with quantitation, spatiotemporal localization, mutations, splice isoforms, and PTM variants was proven not to be predicted by genome sequencing alone. The melanoma tumor molecular map was complemented by analysis of blood protein expression, including data on proteins regulated after immunotherapy. By adding these key proteomic pillars, the MM500 study expands the knowledge on melanoma disease.

INFERYS rescoring: Boosting peptide identifications and scoring confidence of database search results.

Database search engines for bottom-up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these predictions to calculate additional intensity-based scores helps to overcome this drawback. Here, we describe a processing workflow termed INFERYS™ rescoring for the intensity-based rescoring of Sequest HT search engine results in Thermo Scientific™ Proteome Discoverer™ 2.5 software. The workflow is based on the deep learning platform INFERYS capable of predicting fragment ion intensities, which runs on personal computers without the need for graphics processing units. This workflow calculates intensity-based scores comparing peptide spectrum matches from Sequest HT and predicted spectra. Resulting scores are combined with classical search engine scores for input to the false discovery rate estimation tool Percolator. We demonstrate the merits of this approach by analyzing a classical HeLa standard sample and exemplify how this workflow leads to a better separation of target and decoy identifications, in turn resulting in increased peptide spectrum match, peptide and protein identification numbers. On an immunopeptidome dataset, this workflow leads to a 50% increase in identified peptides, emphasizing the advantage of intensity-based scores when analyzing low-intensity spectra or analytes with very similar physicochemical properties that require vast search spaces. Overall, the end-to-end integration of INFERYS rescoring enables simple and easy access to a powerful enhancement to classical database search engines, promising a deeper, more confident and more comprehensive analysis of proteomic data from any organism by unlocking the intensity dimension of tandem mass spectra for identification and more confident scoring.

Differential Neuropeptidomes of Dense Core Secretory Vesicles (DCSV) Produced at Intravesicular and Extracellular pH Conditions by Proteolytic Processing.

Neuropeptides mediate cell–cell signaling in the nervous and endocrine systems. The neuropeptidome is the spectrum of peptides generated from precursors by proteolysis within dense core secretory vesicles (DCSV). DCSV neuropeptides and contents are released to the extracellular environment where further processing for neuropeptide formation may occur. To assess the DCSV proteolytic capacity for production of neuropeptidomes at intravesicular pH 5.5 and extracellular pH 7.2, neuropeptidomics, proteomics, and protease assays were conducted using chromaffin granules (CG) purified from adrenal medulla. CG are an established model of DCSV. The CG neuropeptidome consisted of 1239 unique peptides derived from 15 proneuropeptides that were colocalized with 64 proteases. Distinct CG neuropeptidomes were generated at the internal DCSV pH of 5.5 compared to the extracellular pH of 7.2. Class-specific protease inhibitors differentially regulated neuropeptidome production involving aspartic, cysteine, serine, and metallo proteases. The substrate cleavage properties of CG proteases were assessed by multiplex substrate profiling by mass spectrometry (MSP-MS) that uses a synthetic peptide library containing diverse cleavage sites for endopeptidases and exopeptidases. Parallel inhibitor-sensitive cleavages for neuropeptidome production and peptide library proteolysis led to elucidation of six CG proteases involved in neuropeptidome production, represented by cathepsins A, B, C, D, and L and carboxypeptidase E (CPE). The MSP-MS profiles of these six enzymes represented the majority of CG proteolytic cleavages utilized for neuropeptidome production. These findings provide new insight into the DCSV proteolytic system for production of distinct neuropeptidomes at the internal CG pH of 5.5 and at the extracellular pH of 7.2.

Regulation of transgelin and GST-pi proteins in the tissues of hamsters exposed to sodium arsenite

Regulation of transgelin and GST-pi proteins in the tissues of hamsters exposed to sodium arsenite

Does and duration of exposure time dependent regulation of proteins in the urinary bladder of hamsters exposed to sodium arsenite

Does and duration of exposure time dependent regulation of proteins in the urinary bladder of hamsters exposed to sodium arsenite

Mutational Landscape of the Proglucagon-Derived Peptides.

Strong efforts have been placed on understanding the physiological roles and therapeutic potential of the proglucagon peptide hormones including glucagon, GLP-1 and GLP-2. However, little is known about the extent and magnitude of variability in the amino acid composition of the proglucagon precursor and its mature peptides. Here, we identified 184 unique missense variants in the human proglucagon gene GCG obtained from exome and whole-genome sequencing of more than 450,000 individuals across diverse sub-populations. This provides an unprecedented source of population-wide genetic variation data on missense mutations and insights into the evolutionary constraint spectrum of proglucagon-derived peptides. We show that the stereotypical peptides glucagon, GLP-1 and GLP-2 display fewer evolutionary alterations and are more likely to be functionally affected by genetic variation compared to the rest of the gene products. Elucidating the spectrum of genetic variations and estimating the impact of how a peptide variant may influence human physiology and pathophysiology through changes in ligand binding and/or receptor signalling, are vital and serve as the first important step in understanding variability in glucose homeostasis, amino acid metabolism, intestinal epithelial growth, bone strength, appetite regulation, and other key physiological parameters controlled by these hormones.

Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics

Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune-oncology. Still, the identification of non-tryptic peptides presents substantial computational challenges. To address these, we synthesized and analyzed >300,000 peptides by multi-modal LC-MS/MS within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN. The resulting data enabled training of a single model using the deep learning framework Prosit, allowing the accurate prediction of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved up to 7-fold, that 87% of the proposed proteasomally spliced HLA peptides may be incorrect and that dozens of additional immunogenic neo-epitopes can be identified from patient tumors in published data. Together, the provided peptides, spectra and computational tools substantially expand the analytical depth of immunopeptidomics workflows.

Statistical Characterization of the Multi-Charged Fragment Ions in the CID and HCD Spectrum

Collision-induced dissociation (CID) and higher-energy collisional dissociation (HCD) are the widely used fragmentation technique in mass spectrometry-based proteomics studies. Understanding the fragmentation pattern from the tandem mass spectra using statistical methods helps to implement efficient spectrum analysis algorithms. The study characterizes the frequency of occurrence of multi-charged fragment ions and their neutral loss events of doubly and triply charged peptides in the CID and HCD spectrum. The dependency of the length of the fragment ion on the occurrence of multi-charged fragment ion is characterized here. Study shows that the singly charged fragment ions are generally dominated in the doubly charged peptide spectrum. However, as the length of the product ion increases, the frequency of occurrence of charge 2 fragment ions increases. The y- ions have more tendencies to generate charge 2 fragment ions than b- ions, both in CID and HCD spectrum. The frequency of occurrence of charge 2 fragment ion peaks is prominent upon the dissociation of the triply charged peptides. For triply charged peptides, product ion of higher length occurred in multiple charge states in CID spectrum. The neutral loss peaks mostly exist in charge 2 states in the triply charged peptide spectrum. The b-ions peaks are observed in much less frequency than y-ions in HCD spectrum as the length of the fragment increases. Isotopic peaks are occurred in charge 2 state both in doubly and triply charged peptide’s HCD spectrum.

Towards the characterization of the hidden world of small proteins in Staphylococcus aureus, a proteogenomics approach.

Small proteins play essential roles in bacterial physiology and virulence, however, automated algorithms for genome annotation are often not yet able to accurately predict the corresponding genes. The accuracy and reliability of genome annotations, particularly for small open reading frames (sORFs), can be significantly improved by integrating protein evidence from experimental approaches. Here we present a highly optimized and flexible bioinformatics workflow for bacterial proteogenomics covering all steps from (i) generation of protein databases, (ii) database searches and (iii) peptide-to-genome mapping to (iv) visualization of results. We used the workflow to identify high quality peptide spectrum matches (PSMs) for small proteins (≤ 100 aa, SP100) in Staphylococcus aureus Newman. Protein extracts from S. aureus were subjected to different experimental workflows for protein digestion and prefractionation and measured with highly sensitive mass spectrometers. In total, 175 proteins with up to 100 aa (SP100) were identified. Out of these 24 (ranging from 9 to 99 aa) were novel and not contained in the used genome annotation.144 SP100 are highly conserved and were found in at least 50% of the publicly available S. aureus genomes, while 127 are additionally conserved in other staphylococci. Almost half of the identified SP100 were basic, suggesting a role in binding to more acidic molecules such as nucleic acids or phospholipids.

Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection.

Discovery of variant peptides such as a single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA sequences and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false-positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.

On the scope of the double Ugi multicomponent stapling to produce helical peptides

The stabilization of helical structures by peptide stapling approaches is now a mature technology capable to provide a variety of biomedical applications. Recently, it was shown that multicomponent macrocyclization is not only an effective way to introduce conformational constraints but it also allows to incorporate additional functionalities to the staple moiety in a one-pot process. This work investigates the scope of the double Ugi multicomponent stapling approach in its capacity to produce helical peptides from unstructured sequences. For this, three different stapling combinations were implemented and the CD spectra of the cyclic peptides were measured to determine the effect of the multicomponent macrocyclization on the resulting secondary structure. A new insight into some structural factors influencing the helicity type and content is provided, along with new prospects on the utilization of this methodology to diversify the molecular tethers linking the amino acid side chains.

Universal Spectrum Explorer: A Standalone (Web-)Application for Cross-Resource Spectrum Comparison

Here, we present the Universal Spectrum Explorer (USE), a web-based tool based on IPSA for cross-resource (peptide) spectrum visualization and comparison (https://www.proteomicsdb.org/use/). Mass spectra under investigation can be either provided manually by the user (table format) or automatically retrieved from online repositories supporting access to spectral data via the universal spectrum identifier (USI), or requested from other resources and services implementing a newly designed REST interface. As a proof of principle, we implemented such an interface in ProteomicsDB thereby allowing the retrieval of spectra acquired within the ProteomeTools project or real-time prediction of tandem mass spectra from the deep learning framework Prosit. Annotated mirror spectrum plots can be exported from the USE as editable scalable high-quality vector graphics. The USE was designed and implemented with minimal external dependencies allowing local usage and integration into other web sites (https://github.com/kusterlab/universal_spectrum_explorer).

A rigorous evaluation of optimal peptide targets for MS-based clinical diagnostics of Coronavirus Disease 2019 (COVID-19)

BackgroundThe Coronavirus Disease 2019 (COVID-19) global pandemic has had a profound, lasting impact on the world's population. A key aspect to providing care for those with COVID-19 and checking its further spread is early and accurate diagnosis of infection, which has been generally done via methods for amplifying and detecting viral RNA molecules. Detection and quantitation of peptides using targeted mass spectrometry-based strategies has been proposed as an alternative diagnostic tool due to direct detection of molecular indicators from non-invasively collected samples as well as the potential for high-throughput analysis in a clinical setting; many studies have revealed the presence of viral peptides within easily accessed patient samples. However, evidence suggests that some viral peptides could serve as better indicators of COVID-19 infection status than others, due to potential misidentification of peptides derived from human host proteins, poor spectral quality, high limits of detection etc.MethodsIn this study we have compiled a list of 636 peptides identified from Sudden Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) samples, including from in vitro and clinical sources. These datasets were rigorously analyzed using automated, Galaxy-based workflows containing tools such as PepQuery, BLAST-P, and the Multi-omic Visualization Platform as well as the open-source tools MetaTryp and Proteomics Data Viewer (PDV).ResultsUsing PepQuery for confirming peptide spectrum matches, we were able to narrow down the 639-peptide possibilities to 87 peptides that were most robustly detected and specific to the SARS-CoV-2 virus. The specificity of these sequences to coronavirus taxa was confirmed using Unipept and BLAST-P. Through stringent p-value cutoff combined with manual verification of peptide spectrum match quality, 4 peptides derived from the nucleocapsid phosphoprotein and membrane protein were found to be most robustly detected across all cell culture and clinical samples, including those collected non-invasively.ConclusionWe propose that these peptides would be of the most value for clinical proteomics applications seeking to detect COVID-19 from patient samples. We also contend that samples harvested from the upper respiratory tract and oral cavity have the highest potential for diagnosis of SARS-CoV-2 infection from easily collected patient samples using mass spectrometry-based proteomics assays.

MS Amanda 2.0: Advancements in the standalone implementation.

RationaleDatabase search engines are the preferred method to identify peptides in mass spectrometry data. However, valuable software is in this context not only defined by a powerful algorithm to separate correct from false identifications, but also by constant maintenance and continuous improvements.MethodsIn 2014, we presented our peptide identification algorithm MS Amanda, showing its suitability for identifying peptides in high‐resolution tandem mass spectrometry data and its ability to outperform widely used tools to identify peptides. Since then, we have continuously worked on improvements to enhance its usability and to support new trends and developments in this fast‐growing field, while keeping the original scoring algorithm to assess the quality of a peptide spectrum match unchanged.ResultsWe present the outcome of these efforts, MS Amanda 2.0, a faster and more flexible standalone version with the original scoring algorithm. The new implementation has led to a 3–5× speedup, is able to handle new ion types and supports standard data formats. We also show that MS Amanda 2.0 works best when using only the most common ion types in a particular search instead of all possible ion types.ConclusionsMS Amanda is available free of charge from https://ms.imp.ac.at/index.php?action=msamanda.