An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: sensitivity and specificity analysis

Interrogating data-independent acquisition LC-MS/MS for affinity proteomics

Data-Independent Acquisition (DIA) LC-MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42485-024-00166-4.

Increasing protein identifications in bottom-up proteomics of T. castaneum - Exploiting synergies of protein biochemistry and bioinformatics

Depending on the respective research question, LC-MS/MS based bottom-up proteomics poses challenges from the initial biological sample all the way to data evaluation. The focus of this study was to investigate the influence of sample preparation techniques and data analysis parameters on protein identification in Tribolium castaneum by applying free software proteomics platform Max Quant. Multidimensional protein extraction strategies in combination with electrophoretic or chromatographic off-line protein pre-fractionation were applied to enhance the spectrum of isolated proteins from T. castaneum and reduce the effect of co-elution and ion suppression effects during nano-LC-MS/MS measurements of peptides. For comprehensive data analysis, MaxQuant was used for protein identification and R for data evaluation. A wide range of parameters were evaluated to gain reproducible, reliable, and significant protein identifications. A simple phosphate buffer, pH 8, containing protease and phosphatase inhibitor cocktail and application of gentle extraction conditions were used as a first extraction step for T.castaneum proteins. Furthermore, a two-dimensional extraction procedure in combination with electrophoretic pre-fractionation of extracted proteins and subsequent in-gel digest resulted in almost 100% increase of identified proteins when compared to chromatographic fractionation as well as one-pot-analysis. The additionally identified proteins could be assigned to new molecular functions or cell compartments, emphasizing the positive effect of extended sample preparation in bottom-up proteomics. Besides the number of peptides during post-processing, MaxQuant's Match between Runs exhibited a crucial effect on the number of identified proteins. A maximum relative standard deviation of 2% must be considered for the data analysis. Our work with Tribolium castaneum larvae demonstrates that sometimes - depending on matrix and research question - more complex and time-consuming sample preparation can be advantageous for isolation and identification of additional proteins in bottom-up proteomics.

[Characteristics of behavioral reactions and the profile of brain isatin-binding proteins of rats with the rotenone-induced experimental parkinsonism]

The neurotoxins rotenone and 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (МPTP) are used for modeling Parkinson's disease in animals (PD). They induce the mitochondrial respiratory chain dysfunction, which leads to the dopaminergic (DA) neuron degeneration. The advantage of the rotenone model consists in ability of rotenone to cause neurodegeneration showing symptoms and molecular biological characteristics similar to those of PD. Isatin (indoldione-2,3) is an endogenous regulator found in tissues and biological fluids of humans and animals. It exhibits a broad range of biological activity mediated by numerous isatin-binding proteins. In this work we have investigated behavioral reactions and profiles of brain isatin-binding proteins of rats with Parkinson's syndrome (PS) in comparison with the corresponding parameters of MPTP-induced Parkinsonism in mice. Systemic injection of rotenone caused severe PS comparable with the effect of MPTP injection. It was accompanied by significant body weight loss, death, oligokinesia, muscular rigidity, and postural instability of animals. In spite of the same pathogenic basis of PS caused by rotenone and MPTP, the molecular mechanisms of their action differ. In the case of rotenone-induced PS, the pool of isatin-binding proteins common of the control rats and the rats with PS (146) significantly exceeded the pool of the common proteins of control mice and mice with PS induced by MPTP, whether right after neurotoxin injection (27), or (all the more) in a week after the MPTP injection (14). The comparison of isatin-binding proteins specific of the animals with MPTP-induced PS and with the rotenone-induced PS (as compared with the control animals) revealed total absence of proteins common of these two models of PD. It is to be noted that both neurotoxins particularly affected the proteins participating in the signal transmission and enzyme activity regulation. The changes of the profile of isatin-binding proteins in response to the injection of rotenone suggest that the neuroprotector isatin could also influence positively in the case of the rotenone model of PD.

Nucleos'ID: A New Search Engine Enabling the Untargeted Identification of RNA Post-transcriptional Modifications from Tandem Mass Spectrometry Analyses of Nucleosides

As RNA post-transcriptional modifications are of growing interest, several methods were developed for their characterization. One of them established for their identification, at the nucleosidic level, is the hyphenation of separation methods, such as liquid chromatography or capillary electrophoresis, to tandem mass spectrometry. However, to our knowledge, no software is yet available for the untargeted identification of RNA post-transcriptional modifications from MS/MS data-dependent acquisitions. Thus, very long and tedious manual data interpretations are required. To meet the need of easier and faster data interpretation, a new user-friendly search engine, called Nucleos'ID, was developed for CE-MS/MS and LC-MS/MS users. Performances of this new software were evaluated on CE-MS/MS data from nucleoside analyses of already well-described Saccharomyces cerevisiae transfer RNA and Bos taurus total tRNA extract. All samples showed great true positive, true negative, and false discovery rates considering the database size containing all modified and unmodified nucleosides referenced in the literature. The true positive and true negative rates obtained were above 0.94, while the false discovery rates were between 0.09 and 0.17. To increase the level of sample complexity, untargeted identification of several RNA modifications from Pseudomonas aeruginosa 70S ribosome was achieved by the Nucleos'ID search following CE-MS/MS analysis.

Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry

Single cell proteomics is a powerful tool with potential for markedly enhancing understanding of cellular processes. Here we report the development and application of multiplexed single cell proteomics using trapped ion mobility time-of-flight mass spectrometry. When employing a carrier channel to improve peptide signal, this method allows over 40,000 tandem mass spectra to be acquired in 30 min. Using a KRASG12C model human-derived cell line, we demonstrate the quantification of over 1200 proteins per cell with high relative sequence coverage permitting the detection of multiple classes of post-translational modifications in single cells. When cells were treated with a KRASG12C covalent inhibitor, this approach revealed cell-to-cell variability in the impact of the drug, providing insight missed by traditional proteomics. We provide multiple resources necessary for the application of single cell proteomics to drug treatment studies including tools to reduce cell cycle linked proteomic effects from masking pharmacological phenotypes.

Higher-Energy Collisional Dissociation Mass Spectral Networks for the Rapid, Semi-automated Characterization of Known and Unknown Ribonucleoside Modifications

Higher-energy collisional dissociation (HCD) of modified ribonucleosides generates characteristic and highly reproducible nucleoside-specific tandem mass spectra (MS/MS). Here, we demonstrate the capability of HCD spectra in combination with spectral matching for the semi-automated characterization of ribonucleosides. This process involved the generation of an HCD spectral library and the establishment of a mass spectral network for rapid detection with high sensitivity and specificity in a retention time-independent fashion. Systematic spectral matching analysis of the MS/MS spectra of tRNA hydrolysates from different organisms has helped us to uncover evidence for the existence of novel ribonucleoside modifications such as s²Cm and OHyW-14. Such an untargeted label-free approach has the potential to be integrated with other methods, including those that use isotope labeling, to simplify the characterization of unknown modified ribonucleosides. These findings suggest the compilation of a universal spectral network, for the characterization of known and unknown ribonucleosides, could accelerate discoveries in the epitranscriptome.

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep-learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT, and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep-learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

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Computational method selection substantially affects phosphopeptide identification.

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Deep-learning-derived metrics effectively discriminate correct and incorrect PSMs.

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Novel metrics enable computational method comparison on real application data.

Identification of tumor antigens with immunopeptidomics

The identification of actionable tumor antigens is indispensable for the development of several cancer immunotherapies, including T cell receptor-transduced T cells and patient-specific mRNA or peptide vaccines. Most known tumor antigens have been identified through extensive molecular characterization and are considered canonical if they derive from protein-coding regions of the genome. By eluting human leukocyte antigen-bound peptides from tumors and subjecting these to mass spectrometry analysis, the peptides can be identified by matching the resulting spectra against reference databases. Recently, mass-spectrometry-based immunopeptidomics has enabled the discovery of noncanonical antigens-antigens derived from sequences outside protein-coding regions or generated by noncanonical antigen-processing mechanisms. Coupled with transcriptomics and ribosome profiling, this method enables the identification of thousands of noncanonical peptides, of which a substantial fraction may be detected exclusively in tumors. Spectral matching against the immense noncanonical reference may generate false positives. However, sensitive mass spectrometry, analytical validation and advanced bioinformatics solutions are expected to uncover the full landscape of presented antigens and clinically relevant targets.

Extending the Depth of Human Plasma Proteome Coverage Using Simple Fractionation Techniques

Human plasma is one of the most widely used tissues in clinical analysis, and plasma-based biomarkers are used for monitoring patient health status and/or response to medical treatment to avoid unnecessary invasive biopsy. Data-driven plasma proteomics has suffered from a lack of throughput and detection sensitivity, largely due to the complexity of the plasma proteome and in particular the enormous quantitative dynamic range, estimated to be between 9 and 13 orders of magnitude between the lowest and the highest abundance protein. A major challenge is to identify workflows that can achieve depth of plasma proteome coverage while minimizing the complexity of the sample workup and maximizing the sample throughput. In this study, we have performed intensive depletion of high-abundant plasma proteins or enrichment of low-abundant proteins using the Agilent multiple affinity removal liquid chromatography (LC) column-Human 6 (Hu6), the Agilent multiple affinity removal LC column-Human 14 (Hu14), and ProteoMiner followed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS PAGE) and C18 prefractionation techniques. We compared the performance of each of these fractionation approaches to identify the method that satisfies requirements for analysis of clinical samples and to include good plasma proteome coverage in combination with reasonable sample output. In this study, we report that one-dimensional (1D) gel-based prefractionation allows parallel sample processing and no loss of proteome coverage, compared with serial chromatographic separation, and significantly accelerates analysis time, particularly important for large clinical projects. Furthermore, we show that a variety of methodologies can achieve similarly high plasma proteome coverage, allowing flexibility in method selection based on project-specific needs. These considerations are important in the effort to accelerate plasma proteomics research so as to provide efficient, reliable, and accurate diagnoses, population-based health screening, clinical research studies, and other clinical work.

A Viable New Strategy for the Discovery of Peptide Proteolytic Cleavage Products in Plant-Microbe Interactions

Small peptides that are proteolytic cleavage products (PCPs) of less than 100 amino acids are emerging as key signaling molecules that mediate cell-to-cell communication and biological processes that occur between and within plants, fungi, and bacteria. Yet, the discovery and characterization of these molecules is largely overlooked. Today, selective enrichment and subsequent characterization by mass spectrometry-based sequencing offers the greatest potential for their comprehensive characterization, however qualitative and quantitative performance metrics are rarely captured. Herein, we addressed this need by benchmarking the performance of an enrichment strategy, optimized specifically for small PCPs, using state-of-the-art de novo-assisted peptide sequencing. As a case study, we implemented this approach to identify PCPs from different root and foliar tissues of the hybrid poplar Populus × canescens 717-1B4 in interaction with the ectomycorrhizal basidiomycete Laccaria bicolor. In total, we identified 1,660 and 2,870 Populus and L. bicolor unique PCPs, respectively. Qualitative results supported the identification of well-known PCPs, like the mature form of the photosystem II complex 5-kDa protein (approximately 3 kDa). A total of 157 PCPs were determined to be significantly more abundant in root tips with established ectomycorrhiza when compared with root tips without established ectomycorrhiza and extramatrical mycelium of L. bicolor. These PCPs mapped to 64 Populus proteins and 69 L. bicolor proteins in our database, with several of them previously implicated in biologically relevant associations between plant and fungus.

Visualizing the agreement of peptide assignments between different search engines

There is a trend in the analysis of shotgun proteomics data that aims to combine information from multiple search engines to increase the number of peptide annotations in an experiment. Typically, the degree of search engine complementarity and search engine agreement is visually illustrated by means of Venn diagrams that present the findings of a database search on the level of the nonredundant peptide annotations. We argue this practice to be not fit-for-purpose since the diagrams do not take into account and often conceal the information on complementarity and agreement at the level of the spectrum identification. We promote a new type of visualization that provides insight on the peptide sequence agreement at the level of the peptide-spectrum match (PSM) as a measure of consensus between two search engines with nominal outcomes. We applied the visualizations and percentage sequence agreement to an in-house data set of our benchmark organism, Caenorhabditis elegans, and illustrated that when assessing the agreement between search engine, one should disentangle the notion of PSM confidence and PSM identity. The visualizations presented in this manuscript provide a more informative assessment of pairs of search engines and are made available as an R function in the Supporting Information.

QCMS2 as a new method for providing insight into peptide fragmentation: The influence of the side-chain and inter-side-chain interactions

The identification of peptides and proteins from tandem mass spectra is a difficult task and multiple tools have been developed to aid this identification. We present a new method called quantum chemical mass spectrometry for materials science (QCMS² ), which is based on quantum chemical calculations of bond orders, reaction, and transition-state energies at the DFT/B3LYP/6-311+G* level of theory. The method was used to describe the fragmentation pathways of five X-His-Ser tripeptides with X = Asn, Asp, Glu, Ser, and Trp, thereby focusing on the influence of the side chain and inter-side-chain interactions on the fragmentation. The main features in the mass spectra of the five tripeptides were correctly reproduced, and a number of fragments were assigned to fragmentations involving the side chain and the influence of inter-side-chain interactions. Product ion spectra were recorded to evaluate the capabilities and limitations of QCMS² and a number of conventional tools.

Quantum chemical mass spectrometry: Ab initio study of b2 -ion formation mechanisms for the singly protonated Gln-His-Ser tripeptide

RATIONALE

Both amide bond protonation triggering peptide fragmentations and the controversial b₂ -ion structures have been subjects of intense research. The involvement of histidine (H), with its imidazole side chain that induces specific dissociation patterns involving inter-side-chain (ISC) interactions, in b₂ -ion formation was investigated, focusing on the QHS model tripeptide.

METHODS

To identify the effect of histidine on fragmentations issued from ISC interactions, QHS was selected for a comprehensive analysis of the pathways leading to the three possible b₂ -ion structures, using quantum chemical calculations performed at the DFT/B3LYP/6-311+G* level of theory. Electrospray ionization ion trap mass spectrometry allowed the recording of MS² and MS³ tandem mass spectra, whereas the Quantum Chemical Mass Spectrometry for Materials Science (QCMS² ) method was used to predict fragmentation patterns.

RESULTS

Whereas it is very difficult to differentiate among protonated oxazolone, diketopiperazine, or lactam b₂ -ions using MS² and MS³ mass spectra, the calculations indicated that the QH b₂ -ion (detected at m/z 266) is probably a mixture of the lactam and oxazolone structures formed after amide nitrogen protonation, making the formation of diketopiperazine less likely as it requires an additional step for its formation.

CONCLUSIONS

In contrast to glycine-histidine-containing b₂ -ions, known to be issued from the backbone-imidazole cyclization, we found that interactions between the side chains were not obvious to perceive, neither from a thermodynamics nor from a fragmentation perspective, emphasizing the importance of the whole sequence on the dissociation behavior usually demonstrated from simple glycine-containing tripeptides.

The Power of Three in Cannabis Shotgun Proteomics: Proteases, Databases and Search Engines

Cannabis research has taken off since the relaxation of legislation, yet proteomics is still lagging. In 2019, we published three proteomics methods aimed at optimizing protein extraction, protein digestion for bottom-up and middle-down proteomics, as well as the analysis of intact proteins for top-down proteomics. The database of Cannabis sativa proteins used in these studies was retrieved from UniProt, the reference repositories for proteins, which is incomplete and therefore underrepresents the genetic diversity of this non-model species. In this fourth study, we remedy this shortcoming by searching larger databases from various sources. We also compare two search engines, the oldest, SEQUEST, and the most popular, Mascot. This shotgun proteomics experiment also utilizes the power of parallel digestions with orthogonal proteases of increasing selectivity, namely chymotrypsin, trypsin/Lys-C and Asp-N. Our results show that the larger the database the greater the list of accessions identified but the longer the duration of the search. Using orthogonal proteases and different search algorithms increases the total number of proteins identified, most of them common despite differing proteases and algorithms, but many of them unique as well.

The Utility of Genomic and Transcriptomic Data in the Construction of Proxy Protein Sequence Databases for Unsequenced Tree Nuts

As the apparent incidence of tree nut allergies rises, the development of MS methods that accurately identify tree nuts in food is critical. However, analyses are limited by few available tree nut protein sequences. We assess the utility of translated genomic and transcriptomic data for library construction with Juglans regia, walnut, as a model. Extracted walnuts were subjected to nano-liquid chromatography-mass spectrometry (n-LC-MS/MS), and spectra were searched against databases made from a six-frame translation of the genome (6FT), a transcriptome, and three proteomes. Searches against proteomic databases yielded a variable number of peptides (1156-1275), and only ten additional unique peptides were identified in the 6FT database. Searches against a transcriptomic database yielded results similar to those of the National Center for Biotechnology Information (NCBI) proteome (1200 and 1275 peptides, respectively). Performance of the transcriptomic database was improved via the adjustment of RNA-Seq read processing methods, which increased the number of identified peptides which align to seed allergen proteins by ~20%. Together, these findings establish a path towards the construction of robust proxy protein databases for tree nut species and other non-model organisms.

Comparing Peptide Spectra Matches Across Search Engines

Mass spectrometry is extremely efficient for sequencing small peptides generated by, for example, a trypsin digestion of a complex mixture. Current instruments have the capacity to generate 50-100 K MSMS spectra from a single run. Of these ~30-50% is typically assigned to peptide matches on a 1% FDR threshold. The remaining spectra need more research to explain. We address here whether the 30-50% matched spectra provide consensus matches when using different database-dependent search pipelines. Although the majority of the spectra peptide assignments concur across search engines, our conclusion is that database-dependent search engines still require improvements.

An improved scoring method for the identification of endogenous peptides based on the Mascot MS/MS ion search

To identify endogenous peptides using MS/MS analysis and searching against a polypeptide sequence database, a non-enzyme specific (NES) search considering all of the possible proteolytic cleavages is required. However, the use of a NES search generates more false positive hits than an enzyme specific search, and therefore shows lower identification performance. In this study, the use of the sub-ranked matches for improving the identification performance of the Mascot NES search was investigated and a new scoring method was developed that considered the contribution of all sub-ranked random match probabilities, named the contribution score (CS). The CS showed the highest identification sensitivity using the Mascot NES search with a full protein database when compared to the use of the Mascot first ranked score and the delta score (DS). The confident peptides identified by DS and CS were shown to be complementary. When applied to plant endogenous peptide identification, the identification numbers of tomato endogenous peptides using DS and CS were 176.3% and 184.2%, respectively, higher than the use of the first ranked score of Mascot. The combination of DS and CS identified 200.0% and 8.6% more tomato endogenous peptides compared to the use of Mascot and DS, respectively. This method by combining the CS and DS can significantly improve the identification performance of endogenous peptides without complex computational steps and is also able to improve the identification performance of the enzyme specific search. In addition to the application in the plant peptidomics analysis, this method may be applied to the improvement of peptidomics studies in different species. A web interface for calculating the DS and CS based on Mascot search results was developed herein.

[The effect of neurotoxin MPTP administration to mice on the proteomic profile of brain isatin-binding proteins]

Isatin (indole-2,3-dione) is an endogenous indole found in the mammalian brain, peripheral organs and body fluids. It acts as a neuroprotector, which decreases manifestation of locomotor impairments in animal models of Parkinson's disease. A wide range of biological activity of isatin is associated with interaction of this regulator with numerous isatin-binding proteins. The aim of this study was to investigate the profile of brain isatin-binding proteins in mice with MPTP-induced Parkinsonism (90 min and seven days after administration of this neurotoxin). A single dose administration of MPTP (30 mg/kg, ip.) was accompanied by locomotor impairments in the open field test 90 min after administration; seven days after MPTP administration locomotor activity of mice significantly improved but did not reach the control level. Five independent experiments on proteomic profiling of isatin-binding proteins resulted in confident identification of 96±12 proteins. Development of MPTP-induced locomotor impairments was accompanied by a significant decrease in the number of isatin-binding proteins (63±6; n=5; p<0.01). Seven days after MPTP administration the total number of identified proteins increased and reached the control level (132±34; n=4). The profiles of isatin-binding proteins were rather specific for each group of mice: in the control group these proteins (which were not found in both groups of MPTP-treated mice) represented more than 70% of total proteins. In the case of MPTP treated mice this parameter was 60% (90 min after MPTP administration) and >82% (seven days after MPTP administration). The major changes were found in the groups of isatin-binding proteins involved into cytoskeleton formation and exocytosis, regulation of gene expression, cell division and differentiation and also proteins involved in signal transduction.

Characterization of CobB kinetics and inhibition by nicotinamide

Lysine acetylation has emerged as a global protein regulation system in all domains of life. Sirtuins, or Sir2-like enzymes, are a family of histone deacetylases characterized by their employing NAD+ as a co-substrate. Sirtuins can deacetylate several acetylated proteins, but a consensus substrate recognition sequence has not yet been established. Product inhibition of many eukaryotic sirtuins by nicotinamide and its analogues has been studied in vitro due to their potential role as anticancer agents. In this work, the kinetics of CobB, the main Escherichia coli deacetylase, have been characterized. To our knowledge, this is the first kinetic characterization of a sirtuin employing a fully acetylated and natively folded protein as a substrate. CobB deacetylated several acetyl-CoA synthetase acetylated lysines with a single kinetic rate. In addition, in vitro nicotinamide inhibition of CobB has been characterized, and the intracellular nicotinamide concentrations have been determined under different growth conditions. The results suggest that nicotinamide can act as a CobB regulator in vivo. A nicotinamidase deletion strain was thus phenotypically characterized, and it behaved similarly to the ΔcobB strain. The results of this work demonstrate the potential regulatory role of the nicotinamide metabolite in vivo.

Proteomic Cinderella: Customized analysis of bulky MS/MS data in one night

Proteomic challenges, stirred up by the advent of high-throughput technologies, produce large amount of MS data. Nowadays, the routine manual search does not satisfy the "speed" of modern science any longer. In our work, the necessity of single-thread analysis of bulky data emerged during interpretation of HepG2 proteome profiling results for proteoforms searching. We compared the contribution of each of the eight search engines (X!Tandem, MS-GF[Formula: see text], MS Amanda, MyriMatch, Comet, Tide, Andromeda, and OMSSA) integrated in an open-source graphical user interface SearchGUI ( http://searchgui.googlecode.com ) into total result of proteoforms identification and optimized set of engines working simultaneously. We also compared the results of our search combination with Mascot results using protein kit UPS2, containing 48 human proteins. We selected combination of X!Tandem, MS-GF[Formula: see text] and OMMSA as the most time-efficient and productive combination of search. We added homemade java-script to automatize pipeline from file picking to report generation. These settings resulted in rise of the efficiency of our customized pipeline unobtainable by manual scouting: the analysis of 192 files searched against human proteome (42153 entries) downloaded from UniProt took 11[Formula: see text]h.

Quantification of nitrogenous bases, DNA and Collagen type I for the estimation of the postmortem interval in bone remains

Estimating the postmortem interval (PMI) is an important goal in forensic medicine and continues to be one of the most difficult tasks of the forensic investigator. Few accurate methods exist to determine the time since death of skeletonized human remains due to the great number of intrinsic and external factors that may alter the normal course of postmortem change. The purpose of this research was to assess the usefulness of various biochemical parameters, such as nitrogenous bases (adenine, guanine, purines, cytosine, thymine, pyrimidines, hypoxanthine and xanthine), DNA and Collagen Type I peptides to estimate PMI. These parameters were analysed in cortical bone for the establishment of data in a total of 80 long bones of 80 corpses (50 males, 30 females) with a mean age of 68.31 years (S.D.=18.021, range=20-97). The bones were removed from the cement niches of a cemetery in Murcia (south-eastern Spain), where they had lain for between 5 and 47 years (mean time 23.83 years, S.D.=10.85). Our results show a significant decrease in adenine (p=0.0004), guanine (p=0.0001), purines (p=0.0001), cytosine (p=0.0001), thymine (p=0.0226), pyrimidines (p=0.0002) and the number of peptides of Collagen type I (p=0.0053) in those with a PMI≥20 years. In a curvilinear regression analysis the results show that 30.6% of the variable PMI could be explained by guanine concentration, in bones with a PMI<20 years, while in cases of a PMI≥20 years, the variable that best explained membership of this group was adenine (38.0%). In the discriminant analysis applied to the all the variables as a function of PMI when two groups were established, 86.7% of the cases were correctly classified. These results show that the quantification of Collagen type I proteins and nitrogenous bases could be used as a complementary tool, together with other analyses, in the estimation of PMI.

Discovery of gastric cancer specific biomarkers by the application of serum proteomics

Current diagnostic markers for gastric cancer are not sufficiently specific or sensitive for use in clinical practice. The aims of this study are to compare the proteomes of serum samples from patients with gastric cancers and normal controls, and to develop useful tumor markers of gastric cancer by quantitative proteomic analysis. We identified a total of 388 proteins with a ≤1% FDR and with at least two unique peptides from the sera of each group. Among them, 215, 251, and 260 proteins were identified in serum samples of patients in an advanced cancer group, early cancer group, and normal control group, respectively. We selected differentially expressed proteins in cancer patients compared with those of normal controls via semiquantitative analyses comparing the spectral counts of identified proteins. These differentially expressed proteins were successfully verified using an MS-based quantitative assay, multiple reactions monitoring analysis. Four proteins (vitronectin, clusterin isoform 1, thrombospondin 1, and tyrosine-protein kinase SRMS) were shown to have significant changes between the cancer groups and the normal control group. These four serum proteins were able to discriminate gastric cancer patients from normal controls with sufficient specificity and selectivity.

RNAModMapper: RNA Modification Mapping Software for Analysis of Liquid Chromatography Tandem Mass Spectrometry Data

Liquid chromatography tandem mass spectrometry (LC-MS/MS) has proven to be a powerful analytical tool for the characterization of modified ribonucleic acids (RNAs). The typical approach for analyzing modified nucleosides within RNA sequences by mass spectrometry involves ribonuclease digestion followed by LC-MS/MS analysis and data interpretation. Here we describe a new software tool, RNAModMapper (RAMM), to assist in the interpretation of LC-MS/MS data. RAMM is a stand-alone package that requires user-submitted DNA or RNA sequences to create a local database against which collision-induced dissociation (CID) data of modified oligonucleotides can be compared. RAMM can interpret MS/MS data containing modified nucleosides in two modes: fixed and variable. In addition, RAMM can also utilize interpreted MS/MS data for RNA modification mapping back against the input sequence(s). The applicability of RAMM was first tested using total tRNA isolated from Escherichia coli. It was then applied to map modifications found in 16S and 23S rRNA from Streptomyces griseus.

DeepPep: Deep proteome inference from peptide profiles

Protein inference, the identification of the protein set that is the origin of a given peptide profile, is a fundamental challenge in proteomics. We present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a proteomics mixture, given the sequence universe of possible proteins and a target peptide profile. In its core, DeepPep quantifies the change in probabilistic score of peptide-spectrum matches in the presence or absence of a specific protein, hence selecting as candidate proteins with the largest impact to the peptide profile. Application of the method across datasets argues for its competitive predictive ability (AUC of 0.80±0.18, AUPR of 0.84±0.28) in inferring proteins without need of peptide detectability on which the most competitive methods rely. We find that the convolutional neural network architecture outperforms the traditional artificial neural network architectures without convolution layers in protein inference. We expect that similar deep learning architectures that allow learning nonlinear patterns can be further extended to problems in metagenome profiling and cell type inference. The source code of DeepPep and the benchmark datasets used in this study are available at https://deeppep.github.io/DeepPep/.

The accurate identification of proteins in a proteomics sample, called the protein inference problem, is a fundamental challenge in biomedical sciences. Current approaches are based on applications of traditional neural networks, linear optimization and Bayesian techniques. We here present DeepPep, a deep-convolutional neural network framework that predicts the protein set from a standard proteomics mixture, given all protein sequences and a peptide profile. Comparison to leading methods shows that DeepPep has most robust performance with various instruments and datasets. Our results provide evidence that using sequence-level location information of a peptide in the context of proteome sequence can result in more accurate and robust protein inference. We conclude that Deep Learning on protein sequence leads to superior platforms for protein inference that can be further refined with additional features and extended for far reaching applications.

Mass spectrometry of oligopeptides in the presence of large amounts of alkali halides using desorption/ionization induced by neutral cluster impact

Oligopeptides in the presence of large amounts of salt were desorbed and ionized using desorption/ionization induced by neutral clusters (DINeC) for further analysis by means of mass spectrometry (MS). Using oligopeptides in alkali halide solutions as a model system, DINeC was shown to yield clear and fragmentation free mass spectra of the biomolecules even from environments with a large excess of salt. The results were traced back to a phase separation between salt and biomolecules during sample preparation. The ratio between alkali metal complexes [M+A](+) and bare biomolecules [M+H](+) was controlled using different preparation schemes. DINeC was applied to the products of a tryptic digest of bovine serum albumin in the presence of sodium chloride; the results of a mass fingerprint analysis did not show a major difference for the spectra with and without salt in the original solution. The metal-ion/peptide interaction was further investigated by means of tandem-MS.

DNA Replication Stress Phosphoproteome Profiles Reveal Novel Functional Phosphorylation Sites on Xrs2 in Saccharomyces cerevisiae

In response to replication stress, a phospho-signaling cascade is activated and required for coordination of DNA repair and replication of damaged templates (intra-S-phase checkpoint) . How phospho-signaling coordinates the DNA replication stress response is largely unknown. We employed state-of-the-art liquid chromatography tandem-mass spectrometry (LC-MS/MS) approaches to generate high-coverage and quantitative proteomic and phospho-proteomic profiles during replication stress in yeast, induced by continuous exposure to the DNA alkylating agent methyl methanesulfonate (MMS) . We identified 32,057 unique peptides representing the products of 4296 genes and 22,061 unique phosphopeptides representing the products of 3183 genes. A total of 542 phosphopeptides (mapping to 339 genes) demonstrated an abundance change of greater than or equal to twofold in response to MMS. The screen enabled detection of nearly all of the proteins known to be involved in the DNA damage response, as well as many novel MMS-induced phosphorylations. We assessed the functional importance of a subset of key phosphosites by engineering a panel of phosphosite mutants in which an amino acid substitution prevents phosphorylation. In total, we successfully mutated 15 MMS-responsive phosphorylation sites in seven representative genes including APN1 (base excision repair); CTF4 and TOF1 (checkpoint and sister-chromatid cohesion); MPH1 (resolution of homologous recombination intermediates); RAD50 and XRS2 (MRX complex); and RAD18 (PRR). All of these phosphorylation site mutants exhibited MMS sensitivity, indicating an important role in protecting cells from DNA damage. In particular, we identified MMS-induced phosphorylation sites on Xrs2 that are required for MMS resistance in the absence of the MRX activator, Sae2, and that affect telomere maintenance.

Changes in the Proteome of Langat-Infected Ixodes scapularis ISE6 Cells: Metabolic Pathways Associated with Flavivirus Infection

Background

Ticks (Family Ixodidae) transmit a variety of disease causing agents to humans and animals. The tick-borne flaviviruses (TBFs; family Flaviviridae) are a complex of viruses, many of which cause encephalitis and hemorrhagic fever, and represent global threats to human health and biosecurity. Pathogenesis has been well studied in human and animal disease models. Equivalent analyses of tick-flavivirus interactions are limited and represent an area of study that could reveal novel approaches for TBF control.

Methodology/Principal Findings

High resolution LC-MS/MS was used to analyze the proteome of Ixodes scapularis (Lyme disease tick) embryonic ISE6 cells following infection with Langat virus (LGTV) and identify proteins associated with viral infection and replication. Maximal LGTV infection of cells and determination of peak release of infectious virus, was observed at 36 hours post infection (hpi). Proteins were extracted from ISE6 cells treated with LGTV and non-infectious (UV inactivated) LGTV at 36 hpi and analyzed by mass spectrometry. The Omics Discovery Pipeline (ODP) identified thousands of MS peaks. Protein homology searches against the I. scapularis IscaW1 genome assembly identified a total of 486 proteins that were subsequently assigned to putative functional pathways using searches against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. 266 proteins were differentially expressed following LGTV infection relative to non-infected (mock) cells. Of these, 68 proteins exhibited increased expression and 198 proteins had decreased expression. The majority of the former were classified in the KEGG pathways: “translation”, “amino acid metabolism”, and “protein folding/sorting/degradation”. Finally, Trichostatin A and Oligomycin A increased and decreased LGTV replication in vitro in ISE6 cells, respectively.

Conclusions/Significance

Proteomic analyses revealed ISE6 proteins that were differentially expressed at the peak of LGTV replication. Proteins with increased expression following infection were associated with cellular metabolic pathways and glutaminolysis. In vitro assays using small molecules implicate malate dehydrogenase (MDH2), the citrate cycle, cellular acetylation, and electron transport chain processes in viral replication. Proteins were identified that may be required for TBF infection of ISE6 cells. These proteins are candidates for functional studies and targets for the development of transmission-blocking vaccines and drugs.

High-throughput proteomics offers an approach to evaluate changes in cell protein levels following arboviral infection. Research to understand the molecular basis of human-flavivirus interactions has advanced significantly over the past decade, but comparatively little is known regarding interactions between ticks and tick-borne flaviviruses (TBFs). Here, we employed a proteomics approach using an I. scapularis ISE6 cell line infected with the TBF Langat virus (LGTV) to identify proteins and biochemical pathways affected by viral infection. An LC-MS/MS approach was used to identify proteins that were subsequently assigned to putative cellular pathways based on orthology to proteins in the KEGG database. Biochemical pathways common among arthropods in response to infection with flavivirus and possibly unique to tick-flavivirus interactions, were identified. In vitro cellular assays using small molecules suggest the involvement of the ISE6 proteins, malate dehydrogenase (MDH2), and mitochondria in viral replication. These analyses provide a basis for further studies to identify tick proteins associated with viral replication that could be targeted to disrupt TBF transmission.

Sequence protein identification by randomized sequence database and transcriptome mass spectrometry (SPIDER-TMS): from manual to automatic application of a 'de novo sequencing' approach

Sequence protein identification by a randomized sequence database and transcriptome mass spectrometry software package has been developed at the University of Basilicata in Potenza (Italy) and designed to facilitate the determination of the amino acid sequence of a peptide as well as an unequivocal identification of proteins in a high-throughput manner with enormous advantages of time, economical resource and expertise. The software package is a valid tool for the automation of a de novo sequencing approach, overcoming the main limits and a versatile platform useful in the proteomic field for an unequivocal identification of proteins, starting from tandem mass spectrometry data. The strength of this software is that it is a user-friendly and non-statistical approach, so protein identification can be considered unambiguous.

An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64

In biological mass spectrometry, crude instrumental data need to be converted into meaningful theoretical models. Several data processing and data evaluation steps are required to come to the final results. These operations are often difficult to reproduce, because of too specific computing platforms. This effect, known as 'workflow decay', can be diminished by using a standardized informatic infrastructure. Thus, we compiled an integrated platform, which contains ready-to-use tools and workflows for mass spectrometry data analysis. Apart from general unit operations, such as peak picking and identification of proteins and metabolites, we put a strong emphasis on the statistical validation of results and Data Mining. MASSyPup64 includes e.g., the OpenMS/TOPPAS framework, the Trans-Proteomic-Pipeline programs, the ProteoWizard tools, X!Tandem, Comet and SpiderMass. The statistical computing language R is installed with packages for MS data analyses, such as XCMS/metaXCMS and MetabR. The R package Rattle provides a user-friendly access to multiple Data Mining methods. Further, we added the non-conventional spreadsheet program teapot for editing large data sets and a command line tool for transposing large matrices. Individual programs, console commands and modules can be integrated using the Workflow Management System (WMS) taverna. We explain the useful combination of the tools by practical examples: (1) A workflow for protein identification and validation, with subsequent Association Analysis of peptides, (2) Cluster analysis and Data Mining in targeted Metabolomics, and (3) Raw data processing, Data Mining and identification of metabolites in untargeted Metabolomics. Association Analyses reveal relationships between variables across different sample sets. We present its application for finding co-occurring peptides, which can be used for target proteomics, the discovery of alternative biomarkers and protein-protein interactions. Data Mining derived models displayed a higher robustness and accuracy for classifying sample groups in targeted Metabolomics than cluster analyses. Random Forest models do not only provide predictive models, which can be deployed for new data sets, but also the variable importance. We demonstrate that the later is especially useful for tracking down significant signals and affected pathways in untargeted Metabolomics. Thus, Random Forest modeling supports the unbiased search for relevant biological features in Metabolomics. Our results clearly manifest the importance of Data Mining methods to disclose non-obvious information in biological mass spectrometry . The application of a Workflow Management System and the integration of all required programs and data in a consistent platform makes the presented data analyses strategies reproducible for non-expert users. The simple remastering process and the Open Source licenses of MASSyPup64 (http://www.bioprocess.org/massypup/) enable the continuous improvement of the system.

The identification and characterization of non-coding and coding RNAs and their modified nucleosides by mass spectrometry

The analysis of ribonucleic acids (RNA) by mass spectrometry has been a valuable analytical approach for more than 25 years. In fact, mass spectrometry has become a method of choice for the analysis of modified nucleosides from RNA isolated out of biological samples. This review summarizes recent progress that has been made in both nucleoside and oligonucleotide mass spectral analysis. Applications of mass spectrometry in the identification, characterization and quantification of modified nucleosides are discussed. At the oligonucleotide level, advances in modern mass spectrometry approaches combined with the standard RNA modification mapping protocol enable the characterization of RNAs of varying lengths ranging from low molecular weight short interfering RNAs (siRNAs) to the extremely large 23 S rRNAs. New variations and improvements to this protocol are reviewed, including top-down strategies, as these developments now enable qualitative and quantitative measurements of RNA modification patterns in a variety of biological systems.

On the Extent of Tyrosine Phosphorylation in Chloroplasts

Reanalysis of published mass spectrometry data on Tyr-phosphorylated chloroplast proteins indicates that the majority of peptide spectrum matches reporting Tyr phosphorylation are ambiguous.

The Protein Acetyltransferase PatZ from Escherichia coli Is Regulated by Autoacetylation-induced Oligomerization

Lysine acetylation is an important post-translational modification in the metabolic regulation of both prokaryotes and eukaryotes. In Escherichia coli, PatZ (formerly YfiQ) is the only known acetyltransferase protein and is responsible for acetyl-CoA synthetase acetylation. In this study, we demonstrated PatZ-positive cooperativity in response to acetyl-CoA and the regulation of acetyl-CoA synthetase activity by the acetylation level. Furthermore, functional analysis of an E809A mutant showed that the conserved glutamate residue is not relevant for the PatZ catalytic mechanism. Biophysical studies demonstrated that PatZ is a stable tetramer in solution and is transformed to its octameric form by autoacetylation. Moreover, this modification is reversed by the sirtuin CobB. Finally, an in silico PatZ tetramerization model based on hydrophobic and electrostatic interactions is proposed and validated by three-dimensional hydrodynamic analysis. These data reveal, for the first time, the structural regulation of an acetyltransferase by autoacetylation in a prokaryotic organism.

Identification of best indicators of peptide-spectrum match using a permutation resampling approach

Various indicators of observed-theoretical spectrum matches were compared and the resulting statistical significance was characterized using permutation resampling. Novel decoy databases built by resampling the terminal positions of peptide sequences were evaluated to identify the conditions for accurate computation of peptide match significance levels. The methodology was tested on real and manually curated tandem mass spectra from peptides across a wide range of sizes. Spectra match indicators from complementary database search programs were profiled and optimal indicators were identified. The combination of the optimal indicator and permuted decoy databases improved the calculation of the peptide match significance compared to the approaches currently implemented in the database search programs that rely on distributional assumptions. Permutation tests using p-values obtained from software-dependent matching scores and E-values outperformed permutation tests using all other indicators. The higher overlap in matches between the database search programs when using end permutation compared to existing approaches confirmed the superiority of the end permutation method to identify peptides. The combination of effective match indicators and the end permutation method is recommended for accurate detection of peptides.

ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity

ProLuCID, a new algorithm for peptide identification using tandem mass spectrometry and protein sequence databases has been developed. This algorithm uses a three tier scoring scheme. First, a binomial probability is used as a preliminary scoring scheme to select candidate peptides. The binomial probability scores generated by ProLuCID minimize molecular weight bias and are independent of database size. A modified cross-correlation score is calculated for each candidate peptide identified by the binomial probability. This cross-correlation scoring function models the isotopic distributions of fragment ions of candidate peptides which ultimately results in higher sensitivity and specificity than that obtained with the SEQUEST XCorr. Finally, ProLuCID uses the distribution of XCorr values for all of the selected candidate peptides to compute a Z score for the peptide hit with the highest XCorr. The ProLuCID Z score combines the discriminative power of XCorr and DeltaCN, the standard parameters for assessing the quality of the peptide identification using SEQUEST, and displays significant improvement in specificity over ProLuCID XCorr alone. ProLuCID is also able to take advantage of high resolution MS/MS spectra leading to further improvements in specificity when compared to low resolution tandem MS data. A comparison of filtered data searched with SEQUEST and ProLuCID using the same false discovery rate as estimated by a target-decoy database strategy, shows that ProLuCID was able to identify as many as 25% more proteins than SEQUEST. ProLuCID is implemented in Java and can be easily installed on a single computer or a computer cluster. This article is part of a Special Issue entitled: Computational Proteomics.

RoboOligo: software for mass spectrometry data to support manual and de novo sequencing of post-transcriptionally modified ribonucleic acids

Ribosomal ribonucleic acid (RNA), transfer RNA and other biological or synthetic RNA polymers can contain nucleotides that have been modified by the addition of chemical groups. Traditional Sanger sequencing methods cannot establish the chemical nature and sequence of these modified-nucleotide containing oligomers. Mass spectrometry (MS) has become the conventional approach for determining the nucleotide composition, modification status and sequence of modified RNAs. Modified RNAs are analyzed by MS using collision-induced dissociation tandem mass spectrometry (CID MS/MS), which produces a complex dataset of oligomeric fragments that must be interpreted to identify and place modified nucleosides within the RNA sequence. Here we report the development of RoboOligo, an interactive software program for the robust analysis of data generated by CID MS/MS of RNA oligomers. There are three main functions of RoboOligo: (i) automated de novo sequencing via the local search paradigm. (ii) Manual sequencing with real-time spectrum labeling and cumulative intensity scoring. (iii) A hybrid approach, coined 'variable sequencing', which combines the user intuition of manual sequencing with the high-throughput sampling of automated de novo sequencing.

Analysis of peptides and protein digests by reversed phase high performance liquid chromatography-electrospray ionisation mass spectrometry using neutral pH elution conditions

In this study, the advantages of carrying out the analysis of peptides and tryptic digests of proteins under gradient elution conditions at pH 6.5 by reversed-phase liquid chromatography (RP-HPLC) and in-line electrospray ionisation mass spectrometry (ESI-MS) are documented. For these RP separations, a double endcapped, bidentate anchored n-octadecyl wide pore silica adsorbent was employed in a capillary column format. Compared to the corresponding analysis of the same peptides and protein tryptic digests using low pH elution conditions for their RP-HPLC separation, this alternative approach provides improved selectivity and more efficient separation of these analytes, thus allowing a more sensitive identification of proteins at different abundance levels, i.e. more tryptic peptides from the same protein could be confidently identified, enabling higher sequence coverage of the protein to be obtained. This approach was further evaluated with very complex tryptic digests derived from a human plasma protein sample using an online two-dimensional (2D) strong cation-exchange (SCX)-RP-HPLC-ESI-MS/MS system. Again, at pH 6.5, with mobile phases of different compositions, improved chromatographic selectivities were obtained, concomitant with more sensitive on-line electrospray ionisation tandem mass spectrometric (ESI-MS/MS) analysis. As a consequence, more plasma proteins could be confidently identified, highlighting the potential of these RP-HPLC methods with elution at pH 6.5 to extend further the scope of proteomic investigations.

Proteomic analysis of three Borrelia burgdorferi sensu lato native species and disseminating clones: relevance for Lyme vaccine design

Lyme borreliosis is the most important vector-borne disease in the Northern hemisphere. It is caused by Borrelia burgdorferi sensu lato bacteria transmitted to humans by the bite of hard ticks, Ixodes spp. Although antibiotic treatments are efficient in the early stage of the infection, a significant number of patients develop disseminated manifestations (articular, neurological, and cutaneous) due to unnoticed or absence of erythema migrans, or to inappropriate treatment. Vaccine could be an efficient approach to decrease Lyme disease incidence. We have developed a proteomic approach based on a one dimensional gel electrophoresis followed by LC-MS/MS strategy to identify new vaccine candidates. We analyzed a disseminating clone and the associated wild-type strain for each major pathogenic Borrelia species: B. burgdorferi sensu stricto, B. garinii, and B. afzelii. We identified specific proteins and common proteins to the disseminating clones of the three main species. In parallel, we used a spectral counting strategy to identify upregulated proteins common to the clones. Finally, 40 proteins were found that could potentially be involved in bacterial virulence and of interest in the development of a new vaccine. We selected the three proteins specifically detected in the disseminating clones of the three Borrelia species and checked by RT-PCR whether they are expressed in mouse skin upon B. burgdorferi ss inoculation. Interestingly, BB0566 appears as a potential vaccine candidate. All MS data have been deposited in the ProteomeXchange with identifier PXD000876 (http://proteomecentral.proteomexchange.org/dataset/PXD000876).

Applying Arginylation for Bottom-Up Proteomics

Arginylation is an enzymatic reaction in which arginyl-tRNA protein transferase 1 (ATE1, EC 2.3.2.8) conjugates a single arginyl moiety from aminoacylated tRNA(Arg) onto a target polypeptide. We established arginylation for in vitro labeling of peptides with N-terminal acidic amino acids. Consistent with prior knowledge, arginylated peptides flanked by basic amino acids result in rich redundant MS/MS fragment spectra using various precursor fragmentation modes. Arginylation carried out by ATE1 is a fast method for labeling peptides. Sequence-specific proteolytic digest of proteins is best carried out using a double digest of proteins by Lys-C and Asp-N to generate peptides with a basic amino acid on the C-terminus and an acidic amino acid on the N-terminus. Under these conditions, arginylation is specific for N-terminal acidic amino acids and results in a near 2× sequence coverage in the MS/MS spectrum are achieved.

High-resolution mass spectrometry associated with data mining tools for the detection of pollutants and chemical characterization of honey samples

Analytical methods for food control are mainly focused on restricted lists of well-known contaminants. This paper shows that liquid chromatography-high-resolution mass spectrometry (LC/ESI-HRMS) associated with the data mining tools developed for metabolomics can address this issue by enabling (i) targeted analyses of pollutants, (ii) detection of untargeted and unknown xenobiotics, and (iii) detection of metabolites useful for the characterization of food matrices. A proof-of-concept study was performed on 76 honey samples. Targeted analysis indicated that 35 of 83 targeted molecules were detected in the 76 honey samples at concentrations below regulatory limits. Furthermore, untargeted metabolomic-like analyses highlighted 12 chlorinated xenobiotics, 1 of which was detected in lavender honey samples and identified as 2,6-dichlorobenzamide, a metabolite of dichlobenil, a pesticide banned in France since 2010. Lastly, multivariate statistical analyses discriminated honey samples according to their floral origin, and six discriminating metabolites were characterized thanks to the MS/MS experiments.

Accurate assignment of significance to neuropeptide identifications using Monte Carlo k-permuted decoy databases

In support of accurate neuropeptide identification in mass spectrometry experiments, novel Monte Carlo permutation testing was used to compute significance values. Testing was based on k-permuted decoy databases, where k denotes the number of permutations. These databases were integrated with a range of peptide identification indicators from three popular open-source database search software (OMSSA, Crux, and X! Tandem) to assess the statistical significance of neuropeptide spectra matches. Significance p-values were computed as the fraction of the sequences in the database with match indicator value better than or equal to the true target spectra. When applied to a test-bed of all known manually annotated mouse neuropeptides, permutation tests with k-permuted decoy databases identified up to 100% of the neuropeptides at p-value < 10(-5). The permutation test p-values using hyperscore (X! Tandem), E-value (OMSSA) and Sp score (Crux) match indicators outperformed all other match indicators. The robust performance to detect peptides of the intuitive indicator "number of matched ions between the experimental and theoretical spectra" highlights the importance of considering this indicator when the p-value was borderline significant. Our findings suggest permutation decoy databases of size 1×105 are adequate to accurately detect neuropeptides and this can be exploited to increase the speed of the search. The straightforward Monte Carlo permutation testing (comparable to a zero order Markov model) can be easily combined with existing peptide identification software to enable accurate and effective neuropeptide detection. The source code is available at http://stagbeetle.animal.uiuc.edu/pepshop/MSMSpermutationtesting.

Overview of peptide and protein analysis by mass spectrometry

Mass spectrometry is an indispensable tool for peptide and protein analysis owing to its speed, sensitivity, and versatility. It can be used to determine amino acid sequences of peptides, and to characterize a wide variety of post-translational modifications such as phosphorylation and glycosylation. Mass spectrometry can also be used to determine absolute and relative protein quantities, and can identify and quantify thousands of proteins from complex samples, which makes it an extremely powerful tool for systems biology studies. The main goals of this unit are to familiarize peptide and protein chemists and biologists with the types of mass spectrometers that are appropriate for the majority of their analytical needs, to describe the kinds of experiments that can be performed with these instruments on a routine basis, and to discuss the kinds of information that these experiments provide.

JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from "mixture spectra" to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.

Fit-for-purpose curated database application in mass spectrometry-based targeted protein identification and validation

Background

Mass spectrometry (MS) is a very sensitive and specific method for protein identification, biomarker discovery, and biomarker validation. Protein identification is commonly carried out by comparing MS data with public databases. However, with the development of high throughput and accurate genomic sequencing technology, public databases are being overwhelmed with new entries from different species every day. The application of these databases can also be problematic due to factors such as size, specificity, and unharmonized annotation of the molecules of interest. Current databases representing liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based searches focus on enzyme digestion patterns and sequence information and consequently, important functional information can be missed within the search output. Protein variants displaying similar sequence homology can interfere with database identification when only certain homologues are examined. In addition, recombinant DNA technology can result in products that may not be accurately annotated in public databases. Curated databases, which focus on the molecule of interest with clearer functional annotation and sequence information, are necessary for accurate protein identification and validation. Here, four cases of curated database application have been explored and summarized.

Findings

The four presented curated databases were constructed with clear goals regarding application and have proven very useful for targeted protein identification and biomarker application in different fields. They include a sheeppox virus database created for accurate identification of proteins with strong antigenicity, a custom database containing clearly annotated protein variants such as tau transcript variant 2 for accurate biomarker identification, a sheep-hamster chimeric prion protein (PrP) database constructed for assay development of prion diseases, and a custom Escherichia coli (E. coli) flagella (H antigen) database produced for MS-H, a new H-typing technique. Clearly annotating the proteins of interest was essential for highly accurate, specific, and sensitive sequence identification, and searching against public databases resulted in inaccurate identification of the sequence of interest, while combining the curated database with a public database reduced both the confidence and sequence coverage of the protein search.

Conclusion

Curated protein sequence databases incorporating clear annotations are very useful for accurate protein identification and fit-for-purpose application through MS-based biomarker validation.

Desorption/ionization induced by neutral cluster impact as a soft and efficient ionization source for ion trap mass spectrometry of biomolecules

RATIONALE

Desorption and ionization induced by neutral clusters (DINeC) using SO2 as cluster constituents was previously shown to produce clear and fragmentation-free spectra with low background from samples prepared with standard oligopeptides. Here we demonstrate a more general applicability of this method based on examples from different classes of (bio-)molecules. In order to make better use of the ions generated during the millisecond cluster-pulse, the DINeC source was combined with an ion trap mass spectrometer.

METHODS

Desorption and ionization was induced by neutral SO2 clusters with a mean size of 10(3) to 10(4) molecules seeded in a pulsed He beam. The desorbed ions were accumulated in an ion trap over the whole pulse duration prior to mass spectrometric analysis. Samples were prepared by simply drop casting the respective aqueous solution of biomolecules on Si/SiO2 substrates.

RESULTS

Clear and fragmentation-free spectra of oligopeptides were detected in single pulse operation mode. The very soft nature of the desorption process was demonstrated for phosphopeptides. DINeC spectra from bovine serum albumin samples after tryptic digest led to a clear identification of the original sequence using mass fingerprinting analysis. MS(n) capability was illustrated with two types of rhodamine dyes.

CONCLUSIONS

Desorption and ionization induced by neutral clusters can efficiently be combined with ion trap mass spectrometry since the pulse width and repetition rate of a typical pulsed cluster beam correspond well to the discontinuous accumulation time as well as the spectral rate of the ion trap. Clear mass spectra were obtained with such a setup for a variety of biosamples demonstrating the wider applicability of the DINeC process.

TiO2 photocatalysis damages lipids and proteins in Escherichia coli

This study investigates the mechanisms of UV-A (315 to 400 nm) photocatalysis with titanium dioxide (TiO2) applied to the degradation of Escherichia coli and their effects on two key cellular components: lipids and proteins. The impact of TiO2 photocatalysis on E. coli survival was monitored by counting on agar plate and by assessing lipid peroxidation and performing proteomic analysis. We observed through malondialdehyde quantification that lipid peroxidation occurred during the photocatalytic process, and the addition of superoxide dismutase, which acts as a scavenger of the superoxide anion radical (O2·(-)), inhibited this effect by half, showing us that O2·(-) radicals participate in the photocatalytic antimicrobial effect. Qualitative analysis using two-dimensional electrophoresis allowed selection of proteins for which spot modifications were observed during the applied treatments. Two-dimensional electrophoresis highlighted that among the selected protein spots, 7 and 19 spots had already disappeared in the dark in the presence of 0.1 g/liter and 0.4 g/liter TiO2, respectively, which is accounted for by the cytotoxic effect of TiO2. Exposure to 30 min of UV-A radiation in the presence of 0.1 g/liter and 0.4 g/liter TiO2 increased the numbers of missing spots to 14 and 22, respectively. The proteins affected by photocatalytic oxidation were strongly heterogeneous in terms of location and functional category. We identified several porins, proteins implicated in stress response, in transport, and in bacterial metabolism. This study reveals the simultaneous effects of O2·(-) on lipid peroxidation and on the proteome during photocatalytic treatment and therefore contributes to a better understanding of molecular mechanisms in antibacterial photocatalytic treatment.