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Abstract Background Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. Results We experimentally annotated the bacterial pathogen Salmonella Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function. Conclusion This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of Salmonella as a resource for systems analysis.

Floodplain soils are vast reservoirs of organic carbon often attributed to anaerobic conditions that impose metabolic constraints on organic matter degradation. What remains elusive is how such metabolic constraints respond to dynamic flooding and drainage cycles characteristic of floodplain soils. Here we show that microbial depolymerization and respiration of organic compounds, two rate-limiting steps in decomposition, vary spatially and temporally with seasonal flooding of mountainous floodplain soils (Gothic, Colorado, USA). Combining metabolomics and -proteomics, we found a lower abundance of oxidative enzymes during flooding coincided with the accumulation of aromatic, high-molecular weight compounds, particularly in surface soils. In subsurface soils, we found that a lower oxidation state of carbon coincided with a greater abundance of chemically reduced, energetically less favorable low-molecular weight metabolites, irrespective of flooding condition. Our results suggest that seasonal flooding temporarily constrains oxidative depolymerization of larger, potentially plant-derived compounds in surface soils; in contrast, energetic constraints on microbial respiration persist in more reducing subsurface soils regardless of flooding. Our work underscores that the potential vulnerability of these distinct anaerobic carbon storage mechanisms to changing flooding dynamics should be considered, particularly as climate change shifts both the frequency and extent of flooding in floodplains globally.

Understanding of how soil organic matter (SOM) chemistry is altered in a changing climate has advanced considerably; however, most SOM components remain unidentified, impeding the ability to characterize a major fraction of organic matter and predict what types of molecules, and from which sources, will persist in soil. We present a novel approach to better characterize SOM extracts by integrating information from three types of analyses, and we deploy this method to characterize decaying root-detritus soil microcosms subjected to either drought or normal conditions. To observe broad differences in composition, we employed direct infusion Fourier-transform ion cyclotron resonance mass spectrometry (DI-FT-ICR MS). We complemented this with liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify components by library matching. Since libraries contain only a small fraction of SOM components, we also used fragment spectral cosine similarity scores to relate unknowns and library matches through molecular networks. This integrated approach allowed us to corroborate DI-FT-ICR MS molecular formulas using library matches, which included fungal metabolites and related polyphenolic compounds. We also inferred structures of unknowns from molecular networks and improved LC-MS/MS annotation rates from ∼5 to 35% by considering DI-FT-ICR MS molecular formula assignments. Under drought conditions, we found greater relative amounts of lignin-like vs condensed aromatic polyphenol formulas and lower average nominal oxidation state of carbon, suggesting reduced decomposition of SOM and/or microbes under stress. Our integrated approach provides a framework for enhanced annotation of SOM components that is more comprehensive than performing individual data analyses in parallel.

Background: Stable isotope probing (SIP) approaches are a critical tool in microbiome research to determine associations between species and substrates, as well as the activity of species. The application of these approaches ranges from studying microbial communities important for global biogeochemical cycling to host-microbiota interactions in the intestinal tract. Current SIP approaches, such as DNA-SIP or nanoSIMS allow to analyze incorporation of stable isotopes with high coverage of taxa in a community and at the single cell level, respectively, however they are limited in terms of sensitivity, resolution or throughput., Results: Here, we present an ultra-sensitive, high-throughput protein-based stable isotope probing approach (Protein-SIP), which cuts cost for labeled substrates by 50-99% as compared to other SIP and Protein-SIP approaches and thus enables isotope labeling experiments on much larger scales and with higher replication. The approach allows for the determination of isotope incorporation into microbiome members with species level resolution using standard metaproteomics liquid chromatography-tandem mass spectrometry (LC-MS/MS) measurements. At the core of the approach are new algorithms to analyze the data, which have been implemented in an open-source software ( https://sourceforge.net/projects/calis-p/ ). We demonstrate sensitivity, precision and accuracy using bacterial cultures and mock communities with different labeling schemes. Furthermore, we benchmark our approach against two existing Protein-SIP approaches and show that in the low labeling range used our approach is the most sensitive and accurate. Finally, we measure translational activity using 18 O heavy water labeling in a 63-species community derived from human fecal samples grown on media simulating two different diets. Activity could be quantified on average for 27 species per sample, with 9 species showing significantly higher activity on a high protein diet, as compared to a high fiber diet. Surprisingly, among the species with increased activity on high protein were several Bacteroides species known as fiber consumers. Apparently, protein supply is a critical consideration when assessing growth of intestinal microbes on fiber, including fiber-based prebiotics., Conclusions: We demonstrate that our Protein-SIP approach allows for the ultra-sensitive (0.01 to 10% label) detection of stable isotopes of elements found in proteins, using standard metaproteomics data., (© 2023. The Author(s).)

Fungi play a critical role in the global carbon cycle by degrading plant polysaccharides to small sugars and metabolizing them as carbon and energy sources. We mapped the well-established sugar metabolic network of Aspergillus niger to five taxonomically distant species ( Aspergillus nidulans , Penicillium subrubescens , Trichoderma reesei , Phanerochaete chrysosporium and Dichomitus squalens ) using an orthology-based approach. The diversity of sugar metabolism correlates well with the taxonomic distance of the fungi. The pathways are highly conserved between the three studied Eurotiomycetes ( A. niger , A. nidulans , P. subrubescens ). A higher level of diversity was observed between the T. reesei and A. niger , and even more so for the two Basidiomycetes. These results were confirmed by integrative analysis of transcriptome, proteome and metabolome, as well as growth profiles of the fungi growing on the corresponding sugars. In conclusion, the establishment of sugar pathway models in different fungi revealed the diversity of fungal sugar conversion and provided a valuable resource for the community, which would facilitate rational metabolic engineering of these fungi as microbial cell factories.

High-resolution mass spectrometry techniques are widely used in the environmental sciences to characterize natural organic matter and, when utilizing these instruments, researchers must make multiple decisions regarding sample pre-treatment and the instrument ionization mode. To identify how these choices alter organic matter characterization and resulting conclusions, we analyzed a collection of 17 riverine samples from East River, CO (USA) under four PPL-based Solid Phase Extraction (SPE) treatment and electrospray ionization polarity ( e.g. , positive and negative) combinations: SPE (+), SPE (-), non-SPE (-), and non-SPE (+). The greatest number of formula assignments were achieved with SPE-treated samples due to the removal of compounds that could interfere with ionization. Furthermore, the SPE (-) treatment captured the most formulas across the widest chemical compound diversity. In addition to a reduced number of assigned formulas, the non-SPE datasets resulted in altered thermodynamic interpretations that could cascade into incomplete assumptions about the availability of organic matter pools for heterotrophic microbial respiration. Thus, we infer that the SPE (-) treatment is the best single method for characterizing environmental organic matter pools unless the focus is on lipid-like compounds, in which case we recommend a combination of SPE (-) and SPE (+) to adequately characterize these molecules.

Forested land plays an essential role in water supply across the United States (US). Smoldering commonly existing in wildfires contributes significantly to biomass consumption and gas emission, but its influence on source water quality has been rarely studied. Here, we investigated the impact of smoldering temperature (i.e., no burn, 250, 400, and 600 °C) on the nutrients, elements, and dissolved organic matter (DOM) of water extracts from the residues of the leaf needles and woody trunks of pine (Pinus jeffreyi) under the lab-simulated smoldering fire. Results showed the increase of pH and the yields of the dominated exchangeable cations of K + and Mg 2+ , P, PO 4 3- -P, and SO 4 2- with increasing temperature increasing from 250 to 600 °C, whereas significant decreases in the fraction of dissolved organic C in residue C with increasing temperature and the yields of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) after burnings. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) presented consistent results with UV/fluorescence, suggesting that the unburned materials contained more biodegradable tyrosine/tryptophan/soluble microbial byproduct-like compounds with high molecular weight (MW), whereas the 600 °C-smoldering materials composed of more aromatic, humified, fulvic/humic acid-like, and oxidized compounds with a potentially high density of C=C bonds had less reactivity in forming trihalomethanes (THMs) and haloacetonitriles (HANs). Our study indicates the smoldering-dominated prescribed fire as a potential forest management strategy for reducing biomass fuel and disinfection byproducts (DBPs) precursors in source water from forested lands., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Stream and river systems transport and process substantial amounts of dissolved organic matter (DOM) from terrestrial and aquatic sources to the ocean, with global biogeochemical implications. However, the underlying mechanisms affecting the spatiotemporal organization of DOM composition are under-investigated. To understand the principles governing DOM composition, we leverage the recently proposed synthesis of metacommunity ecology and metabolomics, termed 'meta-metabolome ecology.' Applying this novel approach to a freshwater ecosystem, we demonstrated that despite similar molecular properties across metabolomes, metabolite identity significantly diverged due to environmental filtering and variations in putative biochemical transformations. We refer to this phenomenon as 'thermodynamic redundancy,' which is analogous to the ecological concept of functional redundancy. We suggest that under thermodynamic redundancy, divergent metabolomes can support equivalent biogeochemical function just as divergent ecological communities can support equivalent ecosystem function. As these analyses are performed in additional ecosystems, potentially generalizable concepts, like thermodynamic redundancy, can be revealed and provide insight into DOM dynamics., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021. Published by Elsevier B.V.)

Copper-based algaecides are usually used for controlling algae bloom triggered by the elevated levels of nutrients after wildfires, resulting in the promoted reactivity of dissolved organic matter (DOM) in forming disinfectant byproducts (DBPs). To identify the best strategy for handling this source water, we employed Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to characterize the DBPs precursors after 4-d Microcystis aeruginosa bloom cultured with black (BE) and white (WE) ash water extracts under 0, 0.5, and 1.0 mg-Cu/L. The disappeared DOM during disinfections, primarily composed of O 1-14 , N 1 O 1-14 and N 2 O 1-14 , had a higher average molecular weight (MW) and double-bond equivalent (DBE), relative to DOM after incubation, regardless of disinfects and Cu 2+ . This result suggests assigned features with larger MW and more double bonds/rings as preferable DBP precursors. We observed a larger number of disappeared assigned features with low DBE of 1-10 in control without Cu 2+ addition, possibly explaining lower DOM chlorine reactivity in forming carbonaceous and oxygenated DBPs, relative to the treatments with Cu 2+ addition. We found a larger number of O 1-14 and N 1 O 1-14 with DBE=5-16 in the treatments, potentially explaining higher DOM chloramine reactivity in forming N-nitrosodimethylamine (NDMA), compared to the control. Our study suggests removing oxygen- and nitrogen-containing organic compounds with more double bonds/aromatic rings as a preferable strategy for handling source water after controlling post-fire algae blooms with copper sulfate., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Characterization of the metabolic heterogeneity in cell populations requires the analysis of single cells. Most current methods in single-cell analysis rely on cell manipulation, potentially altering the abundance of metabolites in individual cells. A small sample volume and the chemical diversity of metabolites are additional challenges in single-cell metabolomics. Here, we describe the combination of fiber-based laser ablation electrospray ionization (f-LAESI) with 21 T Fourier transform ion cyclotron resonance mass spectrometry (21TFTICR-MS) for in situ single-cell metabolic profiling in plant tissue. Single plant cells infected by bacteria were selected and sampled directly from the tissue without cell manipulation through mid-infrared ablation with a fine optical fiber tip for ionization by f-LAESI. Ultrahigh performance 21T-FTICR-MS enabled the simultaneous capture of isotopic fine structures (IFSs) for 47 known and 11 unknown compounds, thus elucidating their elemental compositions from single cells and providing information on metabolic heterogeneity in the cell population.

We report a novel technical approach for subcritical fluid extraction (SFE) for organic matter characterization in complex matrices such as soil. The custom platform combines on-line SFE with micro-solid phase extraction, nano liquid chromatography (LC), electrospray ionization and Fourier transform mass spectrometry (SFE-LC-FTMS). We demonstrated the utility of SFE-LC-FTMS, including results from both Orbitrap and FTICR MS, for analysis of complex mixtures of organic compounds in a solid matrix by characterizing soil organic matter in peat, a high-carbon soil. For example, in a single experiment, >6000 molecular formulas can be assigned based upon FTICR MS data from 1-50 μL of soil samples (roughly 1-50 mg of soil, dependent on soil density), nearly twice that typically obtained from direct infusion liquid solvent extraction (LSE) from an order of magnitude larger volume of the same soil. The detected species consisted predominately of lipid-like, lignin-like and protein-like compounds, based on their O/C and H/C ratios, with predominantly CHO and CHONP molecular compositions. These results clearly demonstrate that SFE has the potential to effectively extract a variety of molecular species and could become an important member of a suite of extraction methods for studying SOM and other natural organic matter. This is especially true when comprehensive coverage, minimal sample volumes, and high sensitivity are required, or when the presence of organic solvent residue in residual soil is problematic. The SFE based extraction protocol could potentially enable spatially resolved characterization of organic matter in soil with a resolution of ∼1 mm3 to facilitate studies probing the spatial heterogeneity of soil.

van Krevelen diagrams (O/C vs H/C ratios of elemental formulas) have been widely used in studies to obtain an estimation of the main compound categories present in environmental samples. However, the limits defining a specific compound category based solely on O/C and H/C ratios of elemental formulas have never been accurately listed or proposed to classify metabolites in biological samples. Furthermore, while O/C vs H/C ratios of elemental formulas can provide an overview of the compound categories, such classification is inefficient because of the large overlap among different compound categories along both axes. We propose a more accurate compound classification for biological samples analyzed by high-resolution mass spectrometry based on an assessment of the C/H/O/N/P stoichiometric ratios of over 130 000 elemental formulas of compounds classified in 6 main categories: lipids, peptides, amino sugars, carbohydrates, nucleotides, and phytochemical compounds (oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC) constraints showed a highly accurate categorization of elemental formulas to the main compound categories in biological samples with over 98% of accuracy representing a substantial improvement over any classification based on the classic van Krevelen diagram. This method represents a signficant step forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies, by providing a novel and robust tool to improve our understanding of the ecosystem structure and function through the chemical characterization of biological samples.

Ultrahigh resolution mass spectrometry, such as Fourier transform ion cyclotron resonance mass spectrometry (FT ICR MS), can resolve thousands of molecular ions in complex organic matrices. A Compound Identification Algorithm (CIA) was previously developed for automated elemental formula assignment for natural organic matter (NOM). In this work, we describe software Formularity with a user-friendly interface for CIA function and newly developed search function Isotopic Pattern Algorithm (IPA). While CIA assigns elemental formulas for compounds containing C, H, O, N, S, and P, IPA is capable of assigning formulas for compounds containing other elements. We used halogenated organic compounds (HOC), a chemical class that is ubiquitous in nature as well as anthropogenic systems, as an example to demonstrate the capability of Formularity with IPA. A HOC standard mix was used to evaluate the identification confidence of IPA. Tap water and HOC spike in Suwannee River NOM were used to assess HOC identification in complex environmental samples. Strategies for reconciliation of CIA and IPA assignments were discussed. Software and sample databases with documentation are freely available.

Despite high-resolution mass spectrometers are becoming accessible for more and more laboratories, tandem (MS/MS) mass spectra are still often collected at a low resolution. And even if acquired at a high resolution, software tools used for their processing do not tend to benefit from that in full, and an ability to specify a relative mass tolerance in this case often remains the only feature the respective algorithms take advantage of. We argue that a more efficient way to analyze high-resolution MS/MS spectra should be with methods more explicitly accounting for the precision level, and sustain this claim through demonstrating that a de novo sequencing framework originally developed for (high-resolution) top-down MS/MS data is perfectly suitable for processing high-resolution bottom-up datasets, even though a top-down like deconvolution performed as the first step will leave in many spectra at most a few peaks., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Top-down proteomics, the analysis of intact proteins in their endogenous form, preserves valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down liquid chromatography-tandem MS (LC-MS/MS) data sets is rapidly increasing on account of advances in instrumentation and sample-processing protocols. However, top-down mass spectra are substantially more complex than conventional bottom-up data. New algorithms and software tools for confident proteoform identification and quantification are needed. Here we present Informed-Proteomics, an open-source software suite for top-down proteomics analysis that consists of an LC-MS feature-finding algorithm, a database search algorithm, and an interactive results viewer. We compare our tool with several other popular tools using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.

A vast number of organic compounds are present in soil organic matter (SOM) and play an important role in the terrestrial carbon cycle, facilitate interactions between organisms, and represent a sink for atmospheric CO 2 . The diversity of different SOM compounds and their molecular characteristics is a function of the organic source material and biogeochemical history. By understanding how SOM composition changes with sources and the processes by which it is biogeochemically altered in different terrestrial ecosystems, it may be possible to predict nutrient and carbon cycling, response to system perturbations, and impact of climate change will have on SOM composition. In this study, a sequential chemical extraction procedure was developed to reveal the diversity of organic matter (OM) in different ecosystems and was compared to the previously published protocol using parallel solvent extraction (PSE). We compared six extraction methods using three sample types, peat soil, spruce forest soil and river sediment, so as to select the best method for extracting a representative fraction of organic matter from soils and sediments from a wide range of ecosystems. We estimated the extraction yield of dissolved organic carbon (DOC) by total organic carbon analysis, and measured the composition of extracted OM using high resolution mass spectrometry. This study showed that OM composition depends primarily on soil and sediment characteristics. Two sequential extraction protocols, progressing from polar to non-polar solvents, were found to provide the highest number and diversity of organic compounds extracted from the soil and sediments. Water (H 2 O) is the first solvent used for both protocols followed by either co-extraction with methanol-chloroform (MeOH-CHCl 3 ) mixture, or acetonitrile (ACN) and CHCl 3 sequentially. The sequential extraction protocol developed in this study offers improved sensitivity, and requires less sample compared to the PSE workflow where a new sample is used for each solvent type. Furthermore, a comparison of SOM composition from the different sample types revealed that our sequential protocol allows for ecosystem comparisons based on the diversity of compounds present, which in turn could provide new insights about source and processing of organic compounds in different soil and sediment types., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Separation of proteoforms for global intact protein analysis (i.e. top-down proteomics) has lagged well behind what is achievable for peptides in traditional bottom-up proteomic approach and is becoming a true bottle neck for top-down proteomics. Herein, we report use of long (≥1M) columns containing short alkyl (C1-C4) bonded phases to achieve high-resolution RPLC for separation of proteoforms. At a specific operation pressure limit (i.e., 96.5MPa or 14Kpsi used in this work), column length was found to be the most important factor for achieving maximal resolution separation of proteins when 1.5-5μm particles were used as packings and long columns provided peak capacities greater than 400 for proteoforms derived from a global cell lysate with molecular weights below 50kDa. Larger proteoforms (50-110kDa) were chromatographed on long RPLC columns and detected by MS; however, they cannot be identified yet by tandem mass spectrometry. Our experimental data further demonstrated that long alkyl (e.g., C8 and C18) bonded particles provided high-resolution RPLC for <10kDa proteoforms, not efficient for separation of global proteoforms. Reversed-phase particles with porous, nonporous, and superficially porous surfaces were systematically investigated for high-resolution RPLC. Pore size (200-400Å) and the surface structure (porous and superficially porous) of particles was found to have minor influences on high-resolution RPLC of proteoforms. RPLC presented herein enabled confident identification of ∼900 proteoforms (1% FDR) for a low-microgram quantity of proteomic samples using a single RPLC-MS/MS analysis. The level of RPLC performance attained in this work is close to that typically realized in bottom-up proteomics, and broadly useful when applying e.g., the single-stage MS accurate mass tag approach, but less effective when combined with current tandem MS. Our initial data indicate that MS detection and fragmentation inefficiencies provided by current high-resolution mass spectrometers are key challenges for characterization of larger proteoforms., (Copyright © 2017. Published by Elsevier B.V.)

Secondary organic aerosol (SOA), formed in the photooxidation of diesel fuel, biodiesel fuel, and 20% biodiesel fuel/80% diesel fuel mixture, are prepared under high-NO x conditions in the presence and absence of sulfur dioxide (SO 2 ), ammonia (NH 3 ), and relative humidity (RH). The composition of condensed-phase organic compounds in SOA is measured using several complementary techniques including aerosol mass spectrometry (AMS), high-resolution nanospray desorption electrospray ionization mass spectrometry (nano-DESI/HRMS), and ultrahigh resolution and mass accuracy 21T Fourier transform ion cyclotron resonance mass spectrometry (21T FT-ICR MS). Results demonstrate that sulfuric acid and condensed organosulfur species formed in photooxidation experiments with SO 2 are present in the SOA particles. Fewer organosulfur species are formed in the high humidity experiments, performed at RH 90%, in comparison with experiments done under dry conditions. There is a strong overlap of organosulfur species observed in this study with previous field and chamber studies of SOA. Many MS peaks of organosulfates (R-OS(O) 2 OH) previously designated as biogenic or of unknown origin in field studies might have originated from anthropogenic sources, such as photooxidation of hydrocarbons present in diesel and biodiesel fuel.

We provide the initial performance evaluation of a 21 Tesla Fourier transform ion cyclotron resonance mass spectrometer operating at the Environmental Molecular Sciences Laboratory at the Pacific Northwest National Laboratory. The spectrometer constructed for the 21T system employs a commercial dual linear ion trap mass spectrometer coupled to a FTICR spectrometer designed and built in-house. Performance gains from moving to higher magnetic field strength are exemplified by the measurement of peptide isotopic fine structure, complex natural organic matter mixtures, and large proteins. Accurate determination of isotopic fine structure was demonstrated for doubly charged Substance P with minimal spectral averaging, and 8158 molecular formulas assigned to Suwannee River Fulvic Acid standard with root-mean-square (RMS) error of 10 ppb. We also demonstrated superior performance for intact proteins; namely, broadband isotopic resolution of the entire charge state distribution of apo-transferrin (78 kDa) and facile isotopic resolution of monoclonal antibody under a variety of acquisition parameters (e.g., 6 s time-domains with absorption mode processing yielded resolution of approximately 1 M at m/z = 2700). Graphical Abstract ᅟ.

Motivation: Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data., Results: We describe a method for analysis of protein samples from top-down tandem mass spectrometry data, which capitalizes on de novo sequencing of fragments of the proteins present in the sample. Our algorithm takes as input a set of de novo amino acid strings derived from the given mass spectra using the recently proposed Twister approach, and combines them into aggregated strings endowed with offsets. The former typically constitute accurate sequence fragments of sufficiently well-represented proteins from the sample being analyzed, while the latter indicate their location in the protein sequence, and also bear information on post-translational modifications and fragmentation patterns., Availability and Implementation: Freely available on the web at http://bioinf.spbau.ru/en/twister, Contact: vyatkina@spbau.ru or ppevzner@ucsd.edu, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Various proteoforms may be generated from a single gene due to primary structure alterations (PSAs) such as genetic variations, alternative splicing, and post-translational modifications (PTMs). Top-down mass spectrometry is capable of analyzing intact proteins and identifying patterns of multiple PSAs, making it the method of choice for studying complex proteoforms. In top-down proteomics, proteoform identification is often performed by searching tandem mass spectra against a protein sequence database that contains only one reference protein sequence for each gene or transcript variant in a proteome. Because of the incompleteness of the protein database, an identified proteoform may contain unknown PSAs compared with the reference sequence. Proteoform characterization is to identify and localize PSAs in a proteoform. Although many software tools have been proposed for proteoform identification by top-down mass spectrometry, the characterization of proteoforms in identified proteoform-spectrum matches still relies mainly on manual annotation. We propose to use the Modification Identification Score (MIScore), which is based on Bayesian models, to automatically identify and localize PTMs in proteoforms. Experiments showed that the MIScore is accurate in identifying and localizing one or two modifications.

Wetlands attenuate the migration of many contaminants through a wide range of biogeochemical reactions. Recent research has shown that the rhizosphere, the zone near plant roots, in wetlands is especially effective at promoting contaminant attenuation. The objective of this study was to compare the soil organic matter (OM) composition and microbial communities of a rhizosphere soil (primarily an oxidized environment) to that of the bulk wetland soil (primarily a reduced environment). The rhizosphere had elevated C, N, Mn, and Fe concentrations and total bacteria, including Anaeromyxobacter, counts (as identified by qPCR). Furthermore, the rhizosphere contained several organic molecules that were not identified in the nonrhizosphere soil (54% of the >2200 ESI-FTICR-MS identified compounds). The rhizosphere OM molecules generally had (1) greater overall molecular weights, (2) less aromaticity, (3) more carboxylate and N-containing COO functional groups, and (4) a greater hydrophilic character. These latter two OM properties typically promote metal binding. This study showed for the first time that not only the amount but also the molecular characteristics of OM in the rhizosphere may in part be responsible for the enhanced immobilization of contaminants in wetlands. These finding have implications on the stewardship and long-term management of contaminated wetlands.

The ability to visualize biochemical interactions between microbial communities using MALDI MSI has provided tremendous insights into a variety of biological fields. Matrix application using a sieve proved to be incredibly useful, but it has many limitations that include uneven matrix coverage and limitation in the types of matrices that could be employed in studies. Recently, there has been a concerted effort to improve matrix application for studying agar plated microbial cultures, many of which utilized automated matrix sprayers. Here, we describe the usefulness of using a robotic sprayer for matrix application. The robotic sprayer has two-dimensional control over where matrix is applied, and a heated capillary that allows for rapid drying of the applied matrix. This method provided a significant increase in MALDI sensitivity over the sieve method, as demonstrated by FT-ICR MS analysis, facilitating the ability to gain higher lateral resolution MS images of Bacillus subtilis than previously reported. This method also allowed for the use of different matrices to be applied to the culture surfaces.

De novo sequencing of proteins and peptides is one of the most important problems in mass spectrometry-driven proteomics. A variety of methods have been developed to accomplish this task from a set of bottom-up tandem (MS/MS) mass spectra. However, a more recently emerged top-down technology, now gaining more and more popularity, opens new perspectives for protein analysis and characterization, implying a need for efficient algorithms to process this kind of MS/MS data. Here, we describe a method that allows for the retrieval, from a set of top-down MS/MS spectra, of long and accurate sequence fragments of the proteins contained in the sample. To this end, we outline a strategy for generating high-quality sequence tags from top-down spectra, and introduce the concept of a T-Bruijn graph by adapting to the case of tags the notion of an A-Bruijn graph widely used in genomics. The output of the proposed approach represents the set of amino acid strings spelled out by optimal paths in the connected components of a T-Bruijn graph. We illustrate its performance on top-down data sets acquired from carbonic anhydrase 2 (CAH2) and the Fab region of alemtuzumab.

Soil organic matter (SOM), a complex, heterogeneous mixture of above and belowground plant litter and animal and microbial residues at various degrees of decomposition, is a key reservoir for carbon (C) and nutrient biogeochemical cycling in soil based ecosystems. A limited understanding of the molecular composition of SOM limits the ability to routinely decipher chemical processes within soil and accurately predict how terrestrial carbon fluxes will respond to changing climatic conditions and land use. To elucidate the molecular-level structure of SOM, we selectively extracted a broad range of intact SOM compounds by a combination of different organic solvents from soils with a wide range of C content. Our use of electrospray ionization (ESI) coupled with Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) and a suite of solvents with varying polarity significantly expands the inventory of the types of organic molecules present in soils. Specifically, we found that hexane is selective for lipid-like compounds with very low O/C ratios (<0.1); water (H2O) was selective for carbohydrates with high O/C ratios; acetonitrile (ACN) preferentially extracts lignin, condensed structures, and tannin polyphenolic compounds with O/C > 0.5; methanol (MeOH) has higher selectivity toward compounds characterized with low O/C < 0.5; and hexane, MeOH, ACN, and H2O solvents increase the number and types of organic molecules extracted from soil for a broader range of chemically diverse soil types. Our study of SOM molecules by ESI FTICR MS revealed new insight into the molecular-level complexity of organics contained in soils. We present the first comparative study of the molecular composition of SOM from different ecosystems using ultra high-resolution mass spectrometry.

There are two approaches for de novo protein sequencing: Edman degradation and mass spectrometry (MS). Existing MS-based methods characterize a novel protein by assembling tandem mass spectra of overlapping peptides generated from multiple proteolytic digestions of the protein. Because each tandem mass spectrum covers only a short peptide of the target protein, the key to high coverage protein sequencing is to find spectral pairs from overlapping peptides in order to assemble tandem mass spectra to long ones. However, overlapping regions of peptides may be too short to be confidently identified. High-resolution mass spectrometers have become accessible to many laboratories. These mass spectrometers are capable of analyzing molecules of large mass values, boosting the development of top-down MS. Top-down tandem mass spectra cover whole proteins. However, top-down tandem mass spectra, even combined, rarely provide full ion fragmentation coverage of a protein. We propose an algorithm, TBNovo, for de novo protein sequencing by combining top-down and bottom-up MS. In TBNovo, a top-down tandem mass spectrum is utilized as a scaffold, and bottom-up tandem mass spectra are aligned to the scaffold to increase sequence coverage. Experiments on data sets of two proteins showed that TBNovo achieved high sequence coverage and high sequence accuracy.

There are several notable challenges inherent for fully characterizing the entirety of the human saliva proteome using bottom-up approaches, including polymorphic isoforms, PTMs, unique splice variants, deletions, and truncations. To address these challenges, we have developed a top-down based LC-MS/MS approach, which cataloged 20 major human salivary proteins with a total of 83 proteoforms, containing a broad range of PTMs. Among these proteins, several previously reported disease biomarker proteins were identified at the intact protein level, such as beta-2 microglobulin. In addition, intact glycosylated proteoforms of several saliva proteins were also characterized, including intact N-glycosylated protein prolactin inducible protein and O-glycosylated acidic protein rich protein. These characterized proteoforms constitute an intact saliva proteoform database, which was used for quantitative comparison of intact salivary proteoforms among six healthy individuals. Human parotid and submandibular/sublingual gland secretion samples (2 μg of protein each) from six healthy individuals were compared using RPLC coupled with the 12T FT-ICR mass spectrometer. Significantly different proteoform profiles were resolved with high reproducibility between parotid secretion and submandibular/sublingual glands. The results from this study provide further insight into the potential mechanisms of PTM pathways in oral glandular secretion, expanding our knowledge of this complex yet easily accessible fluid. Intact protein LC-MS approach presented herein can potentially be applied for rapid and accurate identification of biomarkers from only a few microliters of human glandular saliva., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We have previously shown that different individuals exposed to the same antigen produce antibodies with identical mutations in their complementarity determining regions (CDR), suggesting that CDR tryptic peptides can serve as biomarkers for disease diagnosis and prognosis. Complete Fabs derived from disease specific antibodies have even higher potential; they could potentially be used for disease treatment and are required to identify the antigens toward which the antibodies are directed. However, complete Fab sequence characterization via LC-MS analysis of tryptic peptides (i.e. bottom-up) has proven to be impractical for mixtures of antibodies. To tackle this challenge, we have developed an integrated bottom-up and top-down MS approach, employing 2D chromatography coupled with Fourier transform mass spectrometry (FTMS), and applied this approach for full characterization of the variable parts of two pharmaceutical monoclonal antibodies with sensitivity comparable to the bottom-up standard. These efforts represent an essential step toward the identification of disease specific antibodies in patient samples with potentially significant clinical impact., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Pilot Project #1--the identification and characterization of human histone H4 proteoforms by top-down MS--is the first project launched by the Consortium for Top-Down Proteomics (CTDP) to refine and validate top-down MS. Within the initial results from seven participating laboratories, all reported the probability-based identification of human histone H4 (UniProt accession P62805) with expectation values ranging from 10(-13) to 10(-105). Regarding characterization, a total of 74 proteoforms were reported, with 21 done so unambiguously; one new PTM, K79ac, was identified. Inter-laboratory comparison reveals aspects of the results that are consistent, such as the localization of individual PTMs and binary combinations, while other aspects are more variable, such as the accurate characterization of low-abundance proteoforms harboring >2 PTMs. An open-access tool and discussion of proteoform scoring are included, along with a description of general challenges that lie ahead including improved proteoform separations prior to mass spectrometric analysis, better instrumentation performance, and software development., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Simultaneous elucidation of the glycan structure and the glycosylation site are needed to reveal the biological function of protein glycosylation. In this study, we employed a recent type of fragmentation termed higher energy collisional dissociation (HCD) to examine fragmentation patterns of intact glycopeptides generated from a mixture of standard glycosylated proteins. The normalized collisional energy (NCE) value for HCD was varied from 30 to 60% to evaluate the optimal conditions for the fragmentation of peptide backbones and glycoconjugates. Our results indicated that HCD with lower NCE values preferentially fragmented the sugar chains attached to the peptides to generate a ladder of neutral loss of monosaccharides, thereby enabling the putative glycan structure characterization. In addition, detection of the oxonium ions enabled unambiguous differentiation of glycopeptides from non-glycopeptides. In contrast, HCD with higher NCE values preferentially fragmented the peptide backbone and, thus, provided information needed for confident peptide identification. We evaluated the HCD approach with alternating NCE parameters for confident characterization of intact N- and O-linked glycopeptides in a single liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. In addition, we applied a novel data analysis pipeline, so-called GlycoFinder, to form a basis for automated data analysis. Overall, 38 unique intact glycopeptides corresponding to eight glycosylation sites (six N-linked and two O-linked sites) were confidently identified from a standard protein mixture. This approach provided concurrent characterization of both the peptide and the glycan, thereby enabling comprehensive structural characterization of glycoproteins in a single LC-MS/MS analysis., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Post-translational modifications (PTMs) play an important role in various biological processes through changing protein structure and function. Some ultramodified proteins (like histones) have multiple PTMs forming PTM patterns that define the functionality of a protein. While bottom-up mass spectrometry (MS) has been successful in identifying individual PTMs within short peptides, it is unable to identify PTM patterns spreading along entire proteins in a coordinated fashion. In contrast, top-down MS analyzes intact proteins and reveals PTM patterns along the entire proteins. However, while recent advances in instrumentation have made top-down MS accessible to many laboratories, most computational tools for top-down MS focus on proteins with few PTMs and are unable to identify complex PTM patterns. We propose a new algorithm, MS-Align-E, that identifies both expected and unexpected PTMs in ultramodified proteins. We demonstrate that MS-Align-E identifies many proteoforms of histone H4 and benchmark it against the currently accepted software tools.

Cell surfaces are decorated by a variety of proteins that facilitate interactions with their environments and support cell stability. These secreted proteins are anchored to the cell by mechanisms that are diverse, and, in archaea, poorly understood. Recently published in silico data suggest that in some species a subset of secreted euryarchaeal proteins, which includes the S-layer glycoprotein, is processed and covalently linked to the cell membrane by enzymes referred to as archaeosortases. In silico work led to the proposal that an independent, sortase-like system for proteolysis-coupled, carboxy-terminal lipid modification exists in bacteria (exosortase) and archaea (archaeosortase). Here, we provide the first in vivo characterization of an archaeosortase in the haloarchaeal model organism Haloferax volcanii. Deletion of the artA gene (HVO&#95;0915) resulted in multiple biological phenotypes: (a) poor growth, especially under low-salt conditions, (b) alterations in cell shape and the S-layer, (c) impaired motility, suppressors of which still exhibit poor growth, and (d) impaired conjugation. We studied one of the ArtA substrates, the S-layer glycoprotein, using detailed proteomic analysis. While the carboxy-terminal region of S-layer glycoproteins, consisting of a putative threonine-rich O-glycosylated region followed by a hydrophobic transmembrane helix, has been notoriously resistant to any proteomic peptide identification, we were able to identify two overlapping peptides from the transmembrane domain present in the ΔartA strain but not in the wild-type strain. This clearly shows that ArtA is involved in carboxy-terminal post-translational processing of the S-layer glycoprotein. As it is known from previous studies that a lipid is covalently attached to the carboxy-terminal region of the S-layer glycoprotein, our data strongly support the conclusion that archaeosortase functions analogously to sortase, mediating proteolysis-coupled, covalent cell surface attachment., (© 2013 John Wiley & Sons Ltd.)

The periplasm of Gram-negative bacteria is a dynamic and physiologically important subcellular compartment where the constant exposure to potential environmental insults amplifies the need for proper protein folding and modifications. Top-down proteomics analysis of the periplasmic fraction at the intact protein level provides unrestricted characterization and annotation of the periplasmic proteome, including the post-translational modifications (PTMs) on these proteins. Here, we used single-dimension ultra-high pressure liquid chromatography coupled with the Fourier transform mass spectrometry (FTMS) to investigate the intact periplasmic proteome of Novosphingobium aromaticivorans. Our top-down analysis provided the confident identification of 55 proteins in the periplasm and characterized their PTMs including signal peptide removal, N-terminal methionine excision, acetylation, glutathionylation, pyroglutamate, and disulfide bond formation. This study provides the first experimental evidence for the expression and periplasmic localization of many hypothetical and uncharacterized proteins and the first unrestrictive, large-scale data on PTMs in the bacterial periplasm.

Post-translational modifications (PTMs) of core histones work synergistically to fine tune chromatin structure and function, generating a so-called histone code that can be interpreted by a variety of chromatin interacting proteins. We report a novel online two-dimensional liquid chromatography-tandem mass spectrometry (2D LC-MS/MS) platform for high-throughput and sensitive characterization of histone PTMs at the intact protein level. The platform enables unambiguous identification of 708 histone isoforms from a single 2D LC-MS/MS analysis of 7.5 µg purified core histones. The throughput and sensitivity of comprehensive histone modification characterization is dramatically improved compared with more traditional platforms.

MS dissociation methods, including collision induced dissociation (CID), high energy collision dissociation (HCD), and electron transfer dissociation (ETD), can each contribute distinct peptidome identifications using conventional peptide identification methods (Shen et al. J. Proteome Res. 2011), but such samples still pose significant informatics challenges. In this work, we explored utilization of high accuracy fragment ion mass measurements, in this case provided by Fourier transform MS/MS, to improve peptidome peptide data set size and consistency relative to conventional descriptive and probabilistic scoring methods. For example, we identified 20-40% more peptides than SEQUEST, Mascot, and MS&#95;GF scoring methods using high accuracy fragment ion information and the same false discovery rate (FDR) from CID, HCD, and ETD spectra. Identified species covered >90% of the collective identifications obtained using various conventional peptide identification methods, which significantly addresses the common issue of different data analysis methods generating different peptide data sets. Choice of peptide dissociation and high-precision measurement-based identification methods presently available for degradomic-peptidomic analyses needs to be based on the coverage and confidence (or specificity) afforded by the method, as well as practical issues (e.g., throughput). By using accurate fragment information, >1000 peptidome components can be identified from a single human blood plasma analysis with low peptide-level FDRs (e.g., 0.6%), providing an improved basis for investigating potential disease-related peptidome components.

We report on the effectiveness of CID, HCD, and ETD for LC-FT MS/MS analysis of peptides using a tandem linear ion trap-Orbitrap mass spectrometer. A range of software tools and analysis parameters were employed to explore the use of CID, HCD, and ETD to identify peptides (isolated from human blood plasma) without the use of specific "enzyme rules". In the evaluation of an FDR-controlled SEQUEST scoring method, the use of accurate masses for fragments increased the number of identified peptides (by ~50%) compared to the use of conventional low accuracy fragment mass information, and CID provided the largest contribution to the identified peptide data sets compared to HCD and ETD. The FDR-controlled Mascot scoring method provided significantly fewer peptide identifications than SEQUEST (by 1.3-2.3 fold) and CID, HCD, and ETD provided similar contributions to identified peptides. Evaluation of de novo sequencing and the UStags method for more intense fragment ions revealed that HCD afforded more contiguous residues (e.g., ≥ 7 amino acids) than either CID or ETD. Both the FDR-controlled SEQUEST and Mascot scoring methods provided peptide data sets that were affected by the decoy database used and mass tolerances applied (e.g., identical peptides between data sets could be limited to ~70%), while the UStags method provided the most consistent peptide data sets (>90% overlap). The m/z ranges in which CID, HCD, and ETD contributed the largest number of peptide identifications were substantially overlapping. This work suggests that the three peptide ion fragmentation methods are complementary and that maximizing the number of peptide identifications benefits significantly from a careful match with the informatics tools and methods applied. These results also suggest that the decoy strategy may inaccurately estimate identification FDRs.

Background: Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. However, determining protein-coding genes for most new genomes is almost completely performed by inference using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function., Results: We experimentally annotated the bacterial pathogen Salmonella Typhimurium 14028, using "shotgun" proteomics to accurately uncover the translational landscape and post-translational features. The data provide protein-level experimental validation for approximately half of the predicted protein-coding genes in Salmonella and suggest revisions to several genes that appear to have incorrectly assigned translational start sites, including a potential novel alternate start codon. Additionally, we uncovered 12 non-annotated genes missed by gene prediction programs, as well as evidence suggesting a role for one of these novel ORFs in Salmonella pathogenesis. We also characterized post-translational features in the Salmonella genome, including chemical modifications and proteolytic cleavages. We find that bacteria have a much larger and more complex repertoire of chemical modifications than previously thought including several novel modifications. Our in vivo proteolysis data identified more than 130 signal peptide and N-terminal methionine cleavage events critical for protein function., Conclusion: This work highlights several ways in which application of proteomics data can improve the quality of genome annotations to facilitate novel biological insights and provides a comprehensive proteome map of Salmonella as a resource for systems analysis.

Integrated top-down bottom-up proteomics combined with on-line digestion has great potential to improve the characterization of protein isoforms in biological systems and is amendable to high throughput proteomics experiments. Bottom-up proteomics ultimately provides the peptide sequences derived from the tandem MS analyses of peptides after the proteome has been digested. Top-down proteomics conversely entails the MS analyses of intact proteins for more effective characterization of genetic variations and/or post-translational modifications. Herein, we describe recent efforts toward efficient integration of bottom-up and top-down LC-MS-based proteomics strategies. Since most proteomics separations utilize acidic conditions, we exploited the compatibility of pepsin (where the optimal digestion conditions are at low pH) for integration into bottom-up and top-down proteomics work flows. Pressure-enhanced pepsin digestions were successfully performed and characterized with several standard proteins in either an off-line mode using a Barocycler or an on-line mode using a modified high pressure LC system referred to as a fast on-line digestion system (FOLDS). FOLDS was tested using pepsin and a whole microbial proteome, and the results were compared against traditional trypsin digestions on the same platform. Additionally, FOLDS was integrated with a RePlay configuration to demonstrate an ultrarapid integrated bottom-up top-down proteomics strategy using a standard mixture of proteins and a monkey pox virus proteome.

Bottom-up and top-down strategies are two commonly used methods for mass spectrometry (MS) based protein identification; each method has its own advantages and disadvantages. In this chapter, we describe an integrated top-down and bottom-up approach facilitated by concurrent liquid chromatography-mass spectrometry (LC-MS) analysis and fraction collection for comprehensive high-throughput intact protein profiling. The approach employs a high resolution reversed phase (RP) LC separation coupled with LC eluent fraction collection and concurrent on-line MS with a high field (12 T) Fourier-transform ion cyclotron resonance (FTICR) mass spectrometer. Protein elusion profiles and tentative modified protein identification are made using detected intact protein mass in conjunction with bottom-up protein identifications from the enzymatic digestion and analysis of corresponding LC fractions. Specific proteins of biological interest are incorporated into a target ion list for subsequent off-line gas-phase fragmentation that uses an aliquot of the original collected LC fraction, an aliquot of which was also used for bottom-up analysis.

Background: Cancer invasion and metastasis are closely associated with activities within the degradome; however, little is known about whether these activities can be detected in the blood of cancer patients., Methodology and Principal Findings: The peptidome-degradome profiles of pooled blood plasma sampled from 15 breast cancer patients (BCP) and age, race, and menopausal status matched control healthy persons (HP) were globally characterized using advanced comprehensive separations combined with tandem Fourier transform mass spectrometry and new data analysis approaches that facilitated top-down peptidomic analysis. The BCP pool displayed 71 degradome protein substrates that encompassed 839 distinct peptidome peptides. In contrast, the HP 50 degradome substrates found encompassed 425 peptides. We find that the ratios of the peptidome peptide relative abundances can vary as much as >4000 fold between BCP and HP. The experimental results also show differential degradation of substrates in the BCP sample in their functional domains, including the proteolytic and inhibitory sites of the plasmin-antiplasmin and thrombin-antithrombin systems, the main chains of the extracellular matrix protection proteins, the excessive degradation of innate immune system key convertases and membrane attack complex components, as well as several other cancer suppressor proteins., Conclusions: Degradomics-peptidomics profiling of blood plasma is highly sensitive to changes not evidenced by conventional bottom-up proteomics and potentially provides unique signatures of possible diagnostic utility.

An online metal-free weak cation exchange-hydrophilic interaction LC/RPLC system has been developed for sensitive, high-throughput top-down MS. Here, we report results for analyzing PTMs of core histones, with a focus on histone H4, using this system. With just ∼24 μg on-column of core histones (H4, H2B, H2A, and H3) purified from human fibroblasts, 41 H4 isoforms were identified, with the type and location of PTMs unambiguously mapped for 20 of these variants. Compared to corresponding offline studies reported previously, the online weak cation exchange-hydrophilic interaction LC/RPLC platform offers significant improvement in sensitivity, with several orders of magnitude reduction in sample requirements and a reduction in the overall analysis time. To the best of our knowledge, this study represents the first online 2-D LC-MS/MS characterization of core histone mixture at the intact protein level.

Disulfide bonds are a form of post-translational modification that often determines protein structure(s) and function(s). In this work, we report a mass spectrometry method for identification of disulfides in degradation products of proteins, specifically endogenous peptides in the human blood plasma peptidome. LC-Fourier transform tandem mass spectrometry (FT MS/MS) was used for acquiring mass spectra that were de novo sequenced and then searched against the IPI human protein database. Through the use of unique sequence tags (UStags), we unambiguously correlated the spectra to specific database proteins. Examination of the UStags' prefix and/or suffix sequences that contain cysteine(s) in conjunction with sequences of the UStags-specified database proteins is shown to enable the unambigious determination of disulfide bonds. Using this method, we identified the intermolecular and intramolecular disulfides in human blood plasma peptidome peptides that have molecular weights of up to approximately 10 kDa.

Herein we describe a strategy for degradomic-peptidomic analyses. The human blood peptidome was isolated through application of AC/SEC, which enriched its components by >300-fold. The isolated peptidome components were separated by long column HRLC providing a peak capacity of approximately 300 for species having MWs of up to 20 kDa. The separated species were identified by the FT MS/MS-UStags sequencing method. We identified >200 peptidome components that originated from 29 protein substrates from the blood plasma of a single healthy person. The peptidome peptides identified had MWs range of 0.5-14 kDa and identifications were achieved with extremely low (near zero) false discovery rates through searching the IPI human protein database (approximately 70,000 entries). Some of the peptidome peptides identified have mutations and modifications such as acetylation, acetylhexosamine, amidation, cysteinylation, didehydro, oxidation, and pyro-glu. The capabilities described enable the global analysis of the peptidome peptides to identify degradome targets such as degradome proteases, proteases inhibitors, and other relevant substrates, the cleavage specificities for the degradation of individual substrates, as well as a potential basis for using the various extents of substrate degradation for diagnostic purposes.

Chemical cross-linking combined with mass spectrometry can be a powerful approach for the identification of protein-protein interactions and for providing constraints on protein structures. However, enrichment of cross-linked peptides is crucial to reduce sample complexity before mass spectrometric analysis. In addition compact cross-linkers are often preferred to provide short spacer lengths, surface accessibility to the protein complexes, and must have reasonable solubility under conditions where the native complex structure is stable. In this study, we present a novel compact cross-linker that contains two distinct features: (1) an alkyne tag and (2) a small molecule detection tag (NO(2)) to maintain reasonable solubility in water. The alkyne tag enables enrichment of the cross-linked peptides after proteolytic cleavage and coupling of an affinity tag using alkyne-azido click chemistry. Neutral loss of the small NO(2) moiety provides a secondary means of detecting cross-linked peptides in MS/MS analyses, providing additional confidence in peptide identifications. We show the labeling efficiency of this cross-linker, which we termed CLIP (click-enabled linker for interacting proteins) using ubiquitin. The enrichment capability of CLIP is demonstrated for cross-linked ubiquitin in highly complex E. coli cell lysates. Sequential collision-induced dissociation tandem mass spectrometry (CID-MS/MS) and electron transfer dissociation (ETD)-MS/MS of intercross-linked peptides (two peptides connected with a cross-linker) are also demonstrated for improved automated identification of cross-linked peptides.

The phosphorylation of any site on a given protein can affect its activity, degradation rate, ability to dock with other proteins or bind divalent cations, and/or its localization. These effects can operate within the same protein; in fact, multisite phosphorylation is a key mechanism for achieving signal integration in cells. Hence, knowing the overall phosphorylation signature of a protein is essential for understanding the "state" of a cell. However, current technologies to monitor the phosphorylation status of proteins are inefficient at determining the relative stoichiometries of phosphorylation at multiple sites. Here we report a new capability for comprehensive liquid chromatography mass spectrometry (LC/MS) analysis of intact phosphoproteins. The technology platform builds upon an integration of bottom-up and top-down approaches that is facilitated by intact protein reversed-phase (RP)LC concurrently coupled with Fourier transform ion cyclotron resonance (FTICR) MS and fraction collection. As the use of conventional RPLC systems for phosphopeptide identification has proven challenging due to the formation of metal ion complexes at various metal surfaces during LC/MS and ESI-MS analysis, we have developed a "metal-free" RPLC-ESI-MS platform for phosphoprotein characterization. This platform demonstrated a significant sensitivity enhancement for phosphorylated casein proteins enriched from a standard protein mixture and revealed the presence of over 20 casein isoforms arising from genetic variants with varying numbers of phosphorylation sites. The integrated workflow was also applied to an enriched yeast phosphoproteome to evaluate the feasibility of this strategy for characterizing complex biological systems and revealed approximately 16% of the detected yeast proteins to have multiple phosphorylation isoforms. The intact protein LC/MS platform for characterization of combinatorial post-translational modifications (PTMs), with special emphasis on multisite phosphorylation, holds great promise to significantly extend our understanding of the roles of multiple PTMs on signaling components that control the cellular responses to various stimuli.

Here, we report a new approach that integrates pulsed Q dissociation (PQD) and electron transfer dissociation (ETD) techniques for confident and quantitative identification of iTRAQ-labeled phosphopeptides. The use of isobaric tags for relative and absolute quantification enables a high-throughput quantification of peptides via reporter ion signals in the low m/z range of tandem mass spectra. PQD, a form of ion trap collision activated dissociation, allows for detection of low mass-to-charge fragment ions, and electron transfer dissociation is especially useful for sequencing peptides that contain post-translational modifications. Analysis of the phosphoproteome of human fibroblast cells using a sensitive linear ion trap mass spectrometer demonstrated that this hybrid approach improves both identification and quantification of phosphopeptides. ETD improved phosphopeptide identification, while PQD provides improved quantification of iTRAQ-labeled phosphopeptides.

We present an integrated top-down and bottom-up approach that is facilitated by concurrent liquid chromatography-mass spectrometry (LC-MS) analysis and fraction collection for comprehensive high-throughput intact protein profiling. The approach employs high-resolution, reversed-phase (RP) LC separations coupled on-line with a 12 T Fourier transform ion cyclotron resonance (FTICR) mass spectrometer to profile and tentatively identify modified proteins, using detected intact protein masses in conjunction with bare protein identifications from the bottom-up analysis of the corresponding LC fractions. Selected identifications are incorporated into a target ion list for subsequent off-line gas-phase fragmentation that uses an aliquot of the original fraction used for bottom-up analysis. In a proof-of-principle demonstration, this comprehensive strategy was applied to identify protein isoforms arising from various amino acid modifications (e.g., acetylation, phosphorylation) and genetic variants (e.g., single nucleotide polymorphisms, SNPs). This strategy overcomes major limitations of traditional bottom-up (e.g., inability to characterize multiple unexpected protein isoforms and genetic variants) and top-down (e.g., low throughput) approaches.