Search

Light utilization is the vital first step of photosynthesis for photoautotrophic organisms. Boosting the growth and yield of photosynthetic organisms is critical in sustainable food and biofuel pro...

Selenocysteine is cotranslationally inserted into polypeptide chains by recoding the stop codon UGA. However, selenocysteine has also been found to be misincorporated into a small number of proteins displacing cysteines in previous studies, but such misincorporation has not yet been examined at the proteome level thoroughly. We performed label-free quantitative proteomics analysis on Escherichia coli grown in a high-selenium medium to obtain a fuller picture of selenocysteine misincorporation in its proteome. We found 139 misincorporation sites, including 54 recurred in all biological replicates, suggesting that some cysteine sites are more prone to be misincorporated than others. However, sequence and evolutionary conservation analysis showed no clear pattern among these misincorporation sites. We hypothesize that misincorporations occur randomly throughout the proteome, but the degradation rate of such misincorporated proteins varies depending on the impact of the misincorporation on protein function and stability, leading to the differential detectability of misincorporated sites by proteomics. Our hypothesis is further supported by two observations: (1) cells cultured with severely limited sulfur still retained a substantial proportion of normal cysteine counterparts of all of the found misincorporated proteins and (2) proteins involved in protein folding and proteolysis were highly upregulated in high-selenium culture.

The formation of covalently bound DNA-protein crosslinks (DPCs) is linked to the pathophysiology of cancers and many other degenerative diseases. Knowledge of the proteins that were frequently involved in forming DPCs will improve our understanding of the etiological mechanism of diseases and facilitate the establishment of preventive measures and treatment methods. By using SDS-PAGE and nano-LC coupled Orbitrap LC-MS/MS analyses, we identified, for the first time, that the major DNA-cross-linked proteins in HeLa cells exposed to a methylating agent (methylmethanesulfonate) or hydroxyl free radicals are transcription-associated proteins. In particular, histone H2B3B and poly(rC) binding protein 2 were identified as the most frequent DPC-forming proteins.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000., Genomics, Proteomics and Bioinformatics, 18 (2)

Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to mass spectra deposited to public repositories or contained in public spectral libraries. Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.

Modern shotgun proteomics experiments generate gigabytes of spectra every hour, only a fraction of which were utilized to form biological conclusions. Instead of being stored as flat files in public data repositories, this large amount of data can be better organized to facilitate data reuse. Clustering these spectra by similarity can be helpful in building high-quality spectral libraries, correcting identification errors, and highlighting frequently observed but unidentified spectra. However, large-scale clustering is time-consuming. Here, we present ClusterSheep, a method utilizing Graphics Processing Units (GPUs) to accelerate the process. Unlike previously proposed algorithms for this purpose, our method performs true pairwise comparison of all spectra within a precursor mass-to-charge ratio tolerance, thereby preserving the full cluster structures. ClusterSheep was benchmarked against previously reported clustering tools, MS-Cluster, MaRaCluster, and msCRUSH. The software tool also functions as an interactive visualization tool with a persistent state, enabling the user to explore the resulting clusters visually and retrieve the clustering results as desired.

Biofilm is made by microbes and often offers protection by making them more tolerant, resistant, and resilient to wide-range antimicrobials. Biofilm-related infections account for more than 80% of human bacterial infections and are especially prevalent in chronic tissue and device-related infections. Owing to the great challenge in treating biofilms, novel and effective antibiofilm compounds urgently need to be identified. We herein identified elasnin as a potent biofilm-targeting compound against methicillin-resistant Staphylococcus aureus (MRSA) through bioassay-guided isolation of bioactive compounds from Actinobacteria. Elasnin effectively inhibited biofilm formation and eradicated pre-formed biofilms of MRSA, and it displays low cytotoxicity with a low risk of resistance development. Transcriptomic and proteomic analyses combined with confocal microscopy observation revealed that elasnin destroyed the biofilm matrix in a time-dependent manner and interfered with cell cycle during the exponential phase, primarily by repressing the expression of virulence factors. Moreover, the biofilm cells released from elasnin treatment showed increased sensitivity to penicillin G. Overall, this study identified elasnin as a promising biofilm-eradicating compound against MRSA and shed light on its action mechanism.

Persisters are a subpopulation of cells that have enhanced abilities to survive antibiotics and other stressful conditions. Recently, it was found that when persisters were repeatedly regrown and retreated with the same antibiotic for several cycles, the new population will become tolerant to the drug. In this study, we applied such cyclic antibiotic treatment on Escherichia coli populations using different classes of antibiotics (ampicillin, ciprofloxacin, and apramycin) during the exponential phase. After a few cycles, we observed that the evolved populations exhibit high tolerance to the specific class of antibiotic used during the evolution experiments, which are achieved by single-point mutations in one or several genes. Interestingly, all evolved populations show multidrug tolerance at the stationary phase, indicating that they have higher triggered persister fraction. Proteomic analysis and cross-comparison of the regulated proteomes of the tolerant populations during the stationary phase identified protein candidates with similar expression profiles that might be important for the tolerance phenotype. Susceptibility tests of mutants lacking gene coding for these protein candidates showed that they have significantly reduced survival toward antibiotics not only during the stationary phase, but also during the exponential phase. We demonstrated how proteomics, combined with cyclic antibiotic treatment as a means to enrich tolerant populations, is a promising avenue to obtain fresh insights into the phenomenon of persistence.

BackgroundStaphylococcus aureus has acquired resistance to antibiotics in the long-term struggle against antibiotics. Treatment of Staphylococcus aureus infection has become more difficult. In this study, based on nontargeted metabolic figure printing technique, the metabolome of a pair of isogenic methicillin-susceptible and resistant Staphylococcus aureus (MSSA and MRSA) strains treated with the sublethal dose of oxacillin was characterize to investigate the mechanism of antibiotic resistance.ResultsMassive alternations of metabolite expression were observed in both MSSA and MRSA treated with oxacillin. The results of accurate mass and mass fragmentation analysis showed that 7 and 29 metabolites of MRSA and MSSA have changed significantly after oxacillin treatment. The dysregulated metabolites suggested that CoA and fatty acids could help Staphylococcus aureus survive under antibiotic stress. Metabolic pathways engaged in antibiotic resistance were discovered through pathway enrichment analysis. The enriched pathways suggested that DNA repairing and flavin biosynthesis are universal pathways to help MSSA and MRSA survive under antibiotic stress. Compared with MSSA, MRSA systematically and effectively fight against oxacillin through precisely controlling energy producing, PBP2a substrate biosynthesis and antioxidant function. ConclusionsCoenzyme A and fatty acids help both MSSA and MRSA survive under the antibiotic stress. MSSA was susceptible to oxacillin and was forced to response. On the contrary, MRSA systematically and effectively fight against oxacillin. The different metabolome responses of MSSA and MRSA provide us with new insights into how Staphylococcus aureus develops antibiotics resistance.

T cells play crucial roles in our immunity against hematological tumors by inducing sustained immune responses. Flow cytometry-based detection of a limited number of specific protein markers has been routinely applied for basic research and clinical investigation in this area. In this study, we combined flow cytometry with the simple integrated spintip-based proteomics technology (SISPROT) to characterize the proteome of primary T cell subtypes in the peripheral blood (PB) from single multiple myeloma (MM) patients. Taking advantage of the integrated high pH reversed-phase fractionation in the SISPROT device, the global proteomes of CD3+, CD4+ and CD8+ T cells were firstly profiled with a depth of >7 000 protein groups for each cell type. The sensitivity of single-shot proteomic analysis was dramatically improved by optimizing the SISPROT and data-dependent acquisition parameters for nanogram-level samples. Eight subtypes of T cells were sorted from about 4 mL PB of single MM patients, and the individual subtype-specific proteomes with coverage among 1 702 and 3 699 protein groups were obtained from as low as 70 ng and up to 500 ng of cell lysates. In addition, we developed a two-step machine learning-based subtyping strategy for proof-of-concept classifying eight T cell subtypes, independent of their cell numbers and individual differences. Our strategy demonstrates an easy-to-use proteomic analysis on immune cells with the potential to discover novel subtype-specific protein biomarkers from limited clinical samples in future large scale clinical studies.

Highlights • Laboratory evolution under different schemes led to strains with distinct tolerance/resistance phenotypes. • Combination of DAP and RIF applied to a DAP-tolerant strain increase its susceptibility. • Protein network of DAP-tolerant strain is more perturbed than the resistant strain. • Novel tolerance-related proteins revealed by proteomics

Edible bird’s nest (EBN) is a glue-like substance deriving from salivary secretion by specific swiftlets, and protein is considered as the main component of EBN. Accounting over 50% by weight, the exact identities of EBN proteins are still not well understood, due to difficulties of extraction, purification and identification. By using EBN proteins as antigens, 31 monoclonal antibodies specifically against the proteins were generated. The proteins of EBN were subjected to identification by shotgun proteomics. Six protein identities were revealed, including acidic mammalian chitinase (AMCase)-like, mucin 5AC-like and ovoinhibitor-like proteins. In parallel, the monoclonal antibodies were used to immunoprecipitate proteins from EBN extract, and subsequently the precipitated product(s) was identified. AMCase-like protein was most frequently precipitated by the antibodies. The existence of AMCase-like protein in EBN was further verified by: (i) recognition of chicken AMCase by our anti-EBN antibodies; and (ii) recognition of EBN AMCase-like protein by a commercial anti-AMCase antibody. The antibody was highly sensitive and selective to AMCase-like protein in EBN products, with limit of detection at 0.01 μg/mL in ELISA test. Thus, AMCase-like protein, or its antibody, could be used as a new quality control marker for EBN.

Ethnopharmacological relevance In the principle of traditional Chinese medicine (TCM), clinical usage is based on drug attributes of the herbal medicine. The cold and hot properties of TCM are classified accordingly to their pharmacological effects, such as temperature change. Herbal medicine has been used as food supplements in our daily life, and the thermogenetic regulation is one of their primary applications. However, the underlying mechanism of “cold” or “hot” stimulating effect of herbal medicine has not been fully identified. Aim of the study Thermogenetic regulation and classification of herbal medicine of hot/cold herbs were determined by rat model of yeast-induced fever. Materials and methods Here, a novel method in classifying and characterizing cold- and hot-herbal medicines was established by analyses of mass spectrometry (MS)-based metabolomics and lipidomics from the serum of herbal extract-treated rats. The yeast-induced inflammatory rats were used as the model system, which were subjected to the treatments of cold- or hot-herbal medicine. Results The multi-omics approach identified the clustering of metabolites from cold and hot herb-treated rat serum by using partial least squares discriminant analysis (PLS-DA), and which subsequently identified that the 8-h treatment was the metabolic perturbation point of herb-mediated thermogenesis. Meanwhile, the levels of identified metabolites in the serum, i.e. lysoPE, lysoPC and carnitine, showed a positive relationship with the regulation of body temperature; while the levels of amino acid, fatty acid and bile acid were contrary correlated with the temperature change. In addition, the differential expressed metabolites were subjected to pathway enrichment and network analyses in revealing the possible action mechanism of herbal medicines in relating to thermogenetic regulation. Conclusion The developed MS-based omics provides a new insight in characterizing the properties of cold/hot herbal medicine.

Summary: Recent discoveries indicate that tolerance and resistance could rapidly evolve in bacterial populations under intermittent antibiotic treatment. In the present study, we applied antibiotic combinations in laboratory experiments to generate novel methicillin-resistant Staphylococcus aureus strains with distinct phenotypes (tolerance, resistance, and suppressed tolerance), and compared their proteome profiles to uncover the adaptation mechanisms. While the tolerant strains have very different proteomes than the susceptible ancestral strain, the resistant strain largely resembles the ancestral in terms of their proteomes. Our proteomics data and other assays support the connection between the detected mutations to the observed phenotypes, confirming the general understanding of tolerance and resistance mechanisms. While resistance directly counteracts the action mechanism of the antibiotic, tolerance involves complex substantial changes in the cells’ biological process to achieve survival advantages. Overall, this study provides insights into the existence of diverse evolutionary pathways for tolerance and resistance development under different treatment scenarios.

Recently, data-independent acquisition (DIA) MS has gained popularity as a qualitative-quantitative workflow for proteomics. One outstanding problem in the analysis of DIA-MS data is alignment of chromatographic retention times across multiple samples, which facilitates peptide identification and accurate quantification. Here, we present a novel hybrid (profile-based and feature-based) algorithm for LC-MS alignment and test it on sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH) (a type of DIA) data. Our algorithm uses a profile-based dynamic time warping algorithm to obtain a coarse alignment and corrects large retention time shifts, and then uses a feature-based bipartite matching algorithm to match feature to feature at a fine scale. We evaluated our method by comparing our aligned feature pairs to peptide identification results of pseudo-MS2 spectra exported by DIA-Umpire, a recently reported tool for deconvoluting DIA-MS data. We proposed that our method can be used to align DIA-MS data prior to identification, and the alignment can be used to delete noise peaks or screen for differentially changed features. We found that a simple alignment-enabled denoising scheme can reduce the number of pseudo-MS2 spectra exported by DIA-Umpire by up to around 40%, while retaining a comparable number of identifications. Finally, we demonstrated the utility of our tool for accurate label-free relative quantification across multiple SWATH runs.

Proteomics is a rapidly maturing field aimed at the high-throughput identification and quantification of all proteins in a biological system. The cornerstone of proteomic technology is tandem mass spectrometry of peptides resulting from the digestion of protein mixtures. The fragmentation pattern of each peptide ion is captured in its tandem mass spectrum, which enables its identification and acts as a fingerprint for the peptide. Spectral libraries are simply searchable collections of these fingerprints, which have taken on an increasingly prominent role in proteomic data analysis. This review describes the historical development of spectral libraries in proteomics, details the computational procedures behind library building and searching, surveys the current applications of spectral libraries, and discusses the outstanding challenges. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:634-648, 2017.

The ability to reliably and reproducibly measure any protein of the human proteome in any tissue or cell type would be transformative for understanding systems-level properties as well as specific pathways in physiology and disease. Here, we describe the generation and verification of a compendium of highly specific assays that enable quantification of 99.7% of the 20,277 annotated human proteins by the widely accessible, sensitive, and robust targeted mass spectrometric method selected reaction monitoring, SRM. This human SRMAtlas provides definitive coordinates that conclusively identify the respective peptide in biological samples. We report data on 166,174 proteotypic peptides providing multiple, independent assays to quantify any human protein and numerous spliced variants, non-synonymous mutations, and post-translational modifications. The data are freely accessible as a resource at http://www.srmatlas.org/, and we demonstrate its utility by examining the network response to inhibition of cholesterol synthesis in liver cells and to docetaxel in prostate cancer lines.

Methods harnessing protein cross-linking and mass spectrometry (XL-MS) offer high-throughput means to identify protein-protein interactions (PPIs) and structural interfaces of protein complexes. Yet, specialized data dependent methods and search algorithms are often required to confidently assign peptide identifications to spectra. To improve the efficiency of matching high confidence spectra we developed a spectral library based approach to search cross-linked peptide data derived from Protein Interaction Reporter (PIR) methods using the spectral library search algorithm, SpectraST. Spectral library matching of cross-linked peptide data from query spectra increased the absolute number of confident peptide relationships matched to spectra, and thereby number of protein-protein interactions identified. By matching library spectra from bona fide, previously established PIR-cross-linked peptide relationships, spectral library searching reduces the need for continued, complex mass spectrometric methods to identify peptide relationships, increases coverage of relationship identifications and improves the accessibility of XL-MS technologies.

Persisters, a small subpopulation of bacterial cells that can survive antibiotic treatment due to transient growth inhibition, pose a serious threat in clinics. Given the nature of persistence as an emergent property of a biological network, proteomics is well-suited to study this phenomenon. Areas covered: In this review, we introduce the phenomenon of bacterial persistence, review previous proteomics studies on persisters, discuss challenges in studying persisters by proteomics, and provide future perspectives in applying proteomics to study persisters. Expert opinion: Despite the potential, there are limited attempts of applying proteomics to study persisters in the literature, partly due to the technical challenges involved. However, with recent advances, such as the discovery of new methods for persister enrichment and isolation, this is the most opportune time to apply proteomics to tackle this high-impact problem of bacterial persistence.

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.

The Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) has now been developing and promoting open community standards and software tools in the field of proteomics for 15 years. Under the guidance of the chair, co-chairs, and other leadership positions, the PSI working groups are tasked with the development and maintenance of community standards via special workshops and ongoing work. Among the existing, ratified standards, the PSI working groups continue to update PSI-MI XML, MITAB, mzML, mzIdentML, mzQuantML, mzTab, and the MIAPE (Minimum Information About a Proteomics Experiment) guidelines with the advance of new technologies and techniques. Further, new standards are currently either in the final stages of completion (proBed and proBAM for proteogenomics results, as well as PEFF) or in early stages of design (a spectral library standard format, a universal spectrum identifier, the qcML quality control format, and the Protein Expression Interface (PROXI) web services Application Programming Interface). In this work we review the current status of all these aspects of the PSI, describe synergies with other efforts such as the ProteomeXchange Consortium, the Human Proteome Project, and the metabolomics community, and provide a look at future directions of the PSI.

Detection of differentially abundant proteins in label-free quantitative shotgun liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments requires a series of computational steps that identify and quantify LC-MS features. It also requires statistical analyses that distinguish systematic changes in abundance between conditions from artifacts of biological and technical variation. The 2015 study of the Proteome Informatics Research Group (iPRG) of the Association of Biomolecular Resource Facilities (ABRF) aimed to evaluate the effects of the statistical analysis on the accuracy of the results. The study used LC-tandem mass spectra acquired from a controlled mixture, and made the data available to anonymous volunteer participants. The participants used methods of their choice to detect differentially abundant proteins, estimate the associated fold changes, and characterize the uncertainty of the results. The study found that multiple strategies (including the use of spectral counts versus peak intensities, and various software tools) could lead to accurate results, and that the performance was primarily determined by the analysts' expertise. This manuscript summarizes the outcome of the study, and provides representative examples of good computational and statistical practice. The data set generated as part of this study is publicly available.

Identifying the species on which hematophagous arthropods feed is crucial for studying the factors that affect pathogen distributions and that can aid public health. Here we describe a protocol to identify the species a parasitic arthropod has previously fed upon by identifying the source of the remnants of a previous blood meal via shotgun proteomics and spectral matching. The protocol is a nontargeted approach that uses the entire detected blood proteome for source identification; it does not require a priori knowledge of genome or protein sequences. Instead, reference spectral libraries are compiled from the blood of multiple host species by using SpectraST, which takes ~4 d; the identification of the species from which a previous blood meal of a hematophagous arthropod was taken is achieved with spectral matching against the reference spectral libraries, which takes approximately another 4 d. This method is robust against random degradation of the blood meal and can identify unknown blood remnants months after the feeding event.

The availability of human genome sequence has transformed biomedical research over the past decade. However, an equivalent map for the human proteome with direct measurements of proteins and peptides does not exist yet. Here we present a draft map of the human proteome using high-resolution Fourier-transform mass spectrometry. In-depth proteomic profiling of 30 histologically normal human samples, including 17 adult tissues, 7 fetal tissues and 6 purified primary haematopoietic cells, resulted in identification of proteins encoded by 17,294 genes accounting for approximately 84% of the total annotated protein-coding genes in humans. A unique and comprehensive strategy for proteogenomic analysis enabled us to discover a number of novel protein-coding regions, which includes translated pseudogenes, non-coding RNAs and upstream open reading frames. This large human proteome catalogue (available as an interactive web-based resource at http://www.humanproteomemap.org) will complement available human genome and transcriptome data to accelerate biomedical research in health and disease.

The proteome informatics research group of the Association of Biomolecular Resource Facilities conducted a study to assess the community's ability to detect and characterize peptides bearing a range of biologically occurring post-translational modifications when present in a complex peptide background. A data set derived from a mixture of synthetic peptides with biologically occurring modifications combined with a yeast whole cell lysate as background was distributed to a large group of researchers and their results were collectively analyzed. The results from the twenty-four participants, who represented a broad spectrum of experience levels with this type of data analysis, produced several important observations. First, there is significantly more variability in the ability to assess whether a results is significant than there is to determine the correct answer. Second, labile post-translational modifications, particularly tyrosine sulfation, present a challenge for most researchers. Finally, for modification site localization there are many tools being employed, but researchers are currently unsure of the reliability of the results these programs are producing.

Spectral library searching is a maturing approach for peptide identification from MS/MS, offering an alternative to traditional sequence database searching. Spectral library searching relies on direct spectrum-to-spectrum matching between the query data and the spectral library, which affords better discrimination of true and false matches, leading to improved sensitivity. However, due to the inherent diversity of the peak location and intensity profiles of real spectra, the resulting similarity score distributions often take on unpredictable shapes. This makes it difficult to model the scores of the false matches accurately, necessitating the use of decoy searching to sample the score distribution of the false matches. Here, we refined the similarity scoring in spectral library searching to enable the validation of spectral search results without the use of decoys. We rank-transformed the peak intensities to standardize all spectra, making it possible to fit a parametric distribution to the scores of the nontop-scoring spectral matches. The statistical significance of the top-scoring match can then be estimated in a rigorous manner according to Extreme Value Theory. The overall result is a more robust and interpretable measure of the quality of the spectral match, which can be obtained without decoys. We tested this refined similarity scoring function on real datasets and demonstrated its effectiveness. This approach reduces search time, increases sensitivity, and extends spectral library searching to situations where decoy spectra cannot be readily generated, such as in searching unidentified and nonpeptide spectral libraries.

With the rapid accumulation of data from shotgun proteomics experiments, it has become feasible to build comprehensive and high-quality spectral libraries of tandem mass spectra of peptides. A spectral library condenses experimental data into a retrievable format and can be used to aid peptide identification by spectral library searching. A key step in spectral library building is spectrum denoising, which is best accomplished by merging multiple replicates of the same peptide ion into a consensus spectrum. However, this approach cannot be applied to "singleton spectra," for which only one observed spectrum is available for the peptide ion. We developed a method, based on a Bayesian classifier, for denoising peptide tandem mass spectra. The classifier accounts for relationships between peaks, and can be trained on the fly from consensus spectra and immediately applied to denoise singleton spectra, without hard-coded knowledge about peptide fragmentation. A linear regression model was also trained to predict the number of useful "signal" peaks in a spectrum, thereby obviating the need for arbitrary thresholds for peak filtering. This Bayesian approach accumulates weak evidence systematically to boost the discrimination power between signal and noise peaks, and produces readily interpretable conditional probabilities that offer valuable insights into peptide fragmentation behaviors. By cross validation, spectra denoised by this method were shown to retain more signal peaks, and have higher spectral similarities to replicates, than those filtered by intensity only.

Intact glycopeptide MS analysis to reveal site-specific protein glycosylation is an important frontier of proteomics. However, computational tools for analyzing MS/MS spectra of intact glycopeptides are still limited and not well-integrated into existing workflows. In this work, a new computational tool which combines the spectral library building/searching tool, SpectraST (Lam et al. Nat. Methods 2008 , 5 , 873–875), and the glycopeptide fragmentation prediction tool, MassAnalyzer (Zhang et al. Anal. Chem. 2010 , 82 , 10194–10202) for intact glycopeptide analysis has been developed. Specifically, this tool enables the determination of the glycan structure directly from low-energy collision-induced dissociation (CID) spectra of intact glycopeptides. Given a list of possible glycopeptide sequences as input, a sample-specific spectral library of MassAnalyzer-predicted spectra is built using SpectraST. Glycan identification from CID spectra is achieved by spectral library searching against this library, in which both m / z and intensity information of the possible fragmentation ions are taken into consideration for improved accuracy. We validated our method using a standard glycoprotein, human transferrin, and evaluated its potential to be used in site-specific glycosylation profiling of glycoprotein datasets from LC-MS/MS. In addition, we further applied our method to reveal, for the first time, the site-specific N -glycosylation profile of recombinant human acetylcholinesterase expressed in HEK293 cells. For maximum usability, SpectraST is developed as part of the Trans-Proteomic Pipeline (TPP), a freely available and open-source software suite for MS data analysis.

Spectral library searching is a promising alternative to sequence database searching in peptide identification from MS/MS spectra. The key advantage of spectral library searching is the utilization of more spectral features to improve score discrimination between good and bad matches, and hence sensitivity. However, the coverage of reference spectral library is limited by current experimental and computational methods. We developed a computational approach to expand the coverage of spectral libraries with semi-empirical spectra predicted from perturbing known spectra of similar sequences, such as those with single amino acid substitutions. We hypothesized that the peptide of similar sequences should produce similar fragmentation patterns, at least in most cases. Our results confirm our hypothesis and specify when this approach can be applied. In actual spectral searching of real data sets, the sensitivity advantage of spectral library searching over sequence database searching can be mostly retained even when all real spectra are replaced by semi-empirical ones. We demonstrated the applicability of this approach by detecting several known non-synonymous single-nucleotide polymorphisms in three large human data sets by spectral searching.

The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of tandem mass spectrometry datasets. The tools encompass most of the steps in a proteomic data analysis workflow in a single, integrated software system. Specifically, the TPP supports all steps from spectrometer output file conversion to protein-level statistical validation, including quantification by stable isotope ratios. We describe here the full workflow of the TPP and the tools therein, along with an example on a sample dataset, demonstrating that the set up and use of the tools is straightforward and well supported and does not require specialized informatics resources or knowledge.

Electron transfer dissociation (ETD) is an alternative fragmentation technique to collision induced dissociation (CID) that has recently become commercially available. ETD has several advantages over CID. It is less prone to fragmenting amino acid side chains, especially those that are modified, thus yielding fragment ion spectra with more uniform peak intensities. Further, precursor ions of longer peptides and higher charge states can be fragmented and identified. However, analysis of ETD spectra has a few important differences that require the optimization of the software packages used for the analysis of CID data, or the development of specialized tools. We have adapted the Trans-Proteomic Pipeline (TPP) to process ETD data. Specifically, we have added support for fragment ion spectra from high charge precursors, compatibility with charge-state estimation algorithms, provisions for the use of the Lys-C protease, capabilities for ETD spectrum library building, and updates to the data formats to differentiate CID and ETD spectra. We show the results of processing datasets from several different types of ETD instruments and demonstrate that application of the ETD-enhanced TPP can increase the number of spectrum identifications at a fixed false discovery rate by as much as 100% over native output from a single sequence search engine.

We have developed and validated a ubiquitin (Ub) and ubiquitin-like protein (Ubl) spectral library, consisting of 467 consensus spectra (320 unique peptides derived from autophagy-related protein 8, F-adjacent transcript 10, interferon-stimulated gene 15 kDa protein, neural precursor cell expressed developmentally down-regulated protein 8, small ubiquitin-related modifiers 1-3 and Ub, and nine of the most commonly observed Ub/Ub1 chain linkages). The use of the Ub/Ub1 library with a spectral matching tool (SpectraST) yields improved performance over database search engines, and can successfully identify many types of Ub/ Ub1 chain-derived peptides that cannot be identified by standard database search algorithms.

Multiple reaction monitoring mass spectrometry (MRM-MS) is a targeted analysis method that has been increasingly viewed as an avenue to explore proteomes with unprecedented sensitivity and throughput. We have developed a software tool, called MaRiMba, to automate the creation of explicitly defined MRM transition lists required to program triple quadrupole mass spectrometers in such analyses. MaRiMba creates MRM transition lists from downloaded or custom-built spectral libraries, restricts output to specified proteins or peptides, and filters based on precursor peptide and product ion properties. MaRiMba can also create MRM lists containing corresponding transitions for isotopically heavy peptides, for which the precursor and product ions are adjusted according to user specifications. This open-source application is operated through a graphical user interface incorporated into the Trans-Proteomic Pipeline, and it outputs the final MRM list to a text file for upload to MS instruments. To illustrate the use of MaRiMba, we used the tool to design and execute an MRM-MS experiment in which we targeted the proteins of a well-defined and previously published standard mixture.

A crucial part of a successful systems biology experiment is an assay that provides reliable, quantitative measurements for each of the components in the system being studied. For proteomics to be a key part of such studies, it must deliver accurate quantification of all the components in the system for each tested perturbation without any gaps in the data. This will require a new approach to proteomics that is based on emerging targeted quantitative mass spectrometry techniques. The PeptideAtlas Project comprises a growing, publicly accessible database of peptides identified in many tandem mass spectrometry proteomics studies and software tools that allow the building of PeptideAtlas, as well as its use by the research community. Here, we describe the PeptideAtlas Project, its contents and components, and show how together they provide a unique platform to select and validate mass spectrometry targets, thereby allowing the next revolution in proteomics.

Data processing is a central and critical component of a successful proteomics experiment, and is often the most time-consuming step. There have been considerable advances in the field of proteomics informatics in the past 5 years, spurred mainly by free and open-source software tools. Along with the gains afforded by new software, the benefits of making raw data and processed results freely available to the community in data repositories are finally in evidence. In this review, we provide an overview of the general analysis approaches, software tools, and repositories that are enabling successful proteomics research via tandem mass spectrometry.

The use of antibacterial drug combinations with synergistic effects is increasingly seen as a critical strategy to combat multi-drug resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). In this work, the proteome responses in MRSA under the stress of a sub-inhibitory dose of a synergistic drug combination of a novel erythromycin derivative, SIPI-8294 and oxacillin, were studied by label-free quantitative proteomics. Several control treatment groups were designed to isolate proteome responses potentially related to the synergy: (1) the non-synergistic drug combination of erythromycin and oxacillin, (2) SIPI-8294 only, (3) oxacillin only and (4) erythromycin only. Results showed that 200 proteins were differentially expressed in SIPI-8294/oxacillin-treated cells. Among these proteins, the level of penicillin binding protein 2a, the protein mainly responsible for oxacillin resistance in MRSA, was four times lower in the SIPI-8294/oxacillin group than in the erythromycin/oxacillin group, suggesting that SIPI-8294 may interfere with this known oxacillin resistance mechanism. Moreover, hierarchical clustering analysis of differentially expressed proteins under different treatments revealed that SIPI-8294/oxacillin elicits very different responses than the individual drugs or the non-synergistic erythromycin/oxacillin combination. Bioinformatic analysis indicated that the synergistic effect can be further traced to a disruption in oxidation-reduction homeostasis and cell wall biosynthesis.

A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same identifiable peptides by sequence database searching methods, which often are time-consuming and error-prone. A more precise and efficient method, in which previously observed and identified peptide MS/MS spectra are catalogued and condensed into searchable spectral libraries to allow new identifications by spectral matching, is seen as a promising alternative. To that end, an open-source, functionally complete, high-throughput and readily extensible MS/MS spectral searching tool, SpectraST, was developed. A high-quality spectral library was constructed by combining the high-confidence identifications of millions of spectra taken from various data repositories and searched using four sequence search engines. The resulting library consists of over 30,000 spectra for Saccharomyces cerevisiae. Using this library, SpectraST vastly outperforms the sequence search engine SEQUEST in terms of speed and the ability to discriminate good and bad hits. A unique advantage of SpectraST is its full integration into the popular Trans Proteomic Pipeline suite of software, which facilitates user adoption and provides important functionalities such as peptide and protein probability assignment, quantification, and data visualization. This method of spectral library searching is especially suited for targeted proteomics applications, offering superior performance to traditional sequence searching.

Shotgun proteomics is an emerging tool for bacterial identification and differentiation. However, the identification of the mass spectra of peptides to genome-derived peptide sequences remains a key issue that limits the use of shotgun proteomics to bacteria with genome sequences available. In this proof-of-concept study, we report a novel bacterial fingerprinting method that enjoys the resolving power and accuracy of mass spectrometry without the burden of peptide identification (i.e. genome sequence-independent). This method uses a similarity-clustering algorithm to search for mass spectra that are derived from the same peptide and merge them into a unique consensus spectrum as the basis to generate proteomic fingerprints of bacterial isolates. In comparison to a traditional peptide identification-based shotgun proteomics workflow and a PCR-based DNA fingerprinting method targeting the repetitive extragenic palindromes elements in bacterial genomes, the novel method generated fingerprints that were richer in information and more discriminative in differentiating E. coli isolates by their animal sources. The novel method is readily deployable to any cultivable bacteria and may be used for several fields of study such as environmental microbiology, applied microbiology and clinical microbiology.

Green fluorescent protein (GFP) has been proposed as an ideal choice for a protein-based biological indicator for use in the validation of decontamination or disinfection treatments. In this article, we present a potentially scalable and cost-effective way to purify recombinant GFP, produced by fermentation in Escherichia coli, by affinity-enhanced extraction in a two-phase aqueous micellar system. Affinity-enhanced partitioning, which improves the specificity and yield of the target protein by specific bioaffinity interactions, has been demonstrated. A novel affinity tag, family 9 carbohydrate-binding module (CBM9) is fused to GFP, and the resulting fusion protein is affinity-extracted in a decyl beta-D-glucopyranoside (C10G1) two-phase aqueous micellar system. In this system, C10G1 acts as phase forming and as affinity surfactant. We will further demonstrate the implementation of this concept to attain partial recovery of affinity-tagged GFP from a clarified E. coli cell lysate, including the simultaneous removal of other contaminating proteins. The cell lysate was partitioned at three levels of dilution (5x, 10x, and 40x). Irrespective of the dilution level, CBM9-GFP was found to partition preferentially to the micelle-rich phase, with the same partition coefficient value as that found in the absence of the cell lysate. The host cell proteins from the cell lysate were found to partition preferentially to the micelle-poor phase, where they experience less excluded-volume interactions. The demonstration of proof-of-principle of the direct affinity-enhanced extraction of CBM9-GFP from the cell lysate represents an important first step towards developing a cost-effective separation method for GFP, and more generally, for other proteins of interest.

The enzyme glucose-6-phosphate dehydrogenase (G6PD) plays an important role in maintaining the level of NADPH and in producing pentose phosphates for nucleotide biosynthesis. It is also of great value as an analytical reagent, being used in various quantitative assays. In searching for new strategies to purify this enzyme, the partitioning of G6PD in two-phase aqueous mixed (nonionic/cationic) micellar systems was investigated both experimentally and theoretically. Our results indicate that the use of a two-phase aqueous mixed micellar system composed of the nonionic surfactant C10E4 (n-decyl tetra(ethylene oxide)) and the cationic surfactant CnTAB (alkyltrimethylammonium bromide, n = 8, 10, or 12) can improve significantly the partitioning behavior of G6PD relative to that obtained in the two-phase aqueous C10E4 micellar system. This improvement can be attributed to electrostatic attractions between the positively charged mixed (nonionic/cationic) micelles and the net negatively charged enzyme G6PD, resulting in the preferential partitioning of G6PD to the top, mixed micelle-rich phase of the two-phase aqueous mixed micellar systems. The effect of varying the cationic surfactant tail length (n = 8, 10, and 12) on the denaturation and partitioning behavior of G6PD in the C10E4 /CnTAB/buffer system was investigated. It was found that C8TAB is the least denaturing to G6PD, followed by C10TAB and C12TAB. However, the C10E4/C12TAB/buffer system generated stronger electrostatic attractions with the net negatively charged enzyme G6PD than the C10E4/C10TAB/buffer and the C10E4/C8TAB/buffer systems, when using the same amount of cationic surfactant. Overall, the two-phase aqueous mixed (C10E4/C10TAB) micellar system yielded the highest G6PD partition coefficient of 7.7, with a G6PD yield in the top phase of 71%, providing the optimal balance between the denaturing effect and the electrostatic attractions for the three cationic surfactants examined. A recently developed theoretical framework to predict protein partition coefficients in two-phase aqueous mixed (nonionic/ionic) micellar systems was implemented, and the theoretically predicted G6PD partition coefficients were found to be in reasonable quantitative agreement with the experimentally measured ones. © 2003 Wiley Periodicals, Inc. Biotechnol Bioeng 82: 445–456, 2003.

We conducted a meta-analysis to evaluate the efficacy of adding any antiangiogenic therapy (AT) to the standard of care in advanced non-small-cell lung cancer (NSCLC). The electronic databases Ovid PubMed, Cochrane Central Register of Controlled Trials, and Embase were searched to identify eligible trials. We included all phase III randomized trials with any line and type of treatment, histology. and AT dose. Pooled hazard ratios (HRs) for overall survival (OS) and progression-free survival (PFS), and pooled odds ratio (OR) for overall response rates (RR) were calculated. We divided the population into 2 subgroups based on the bevacizumab dose. Data of 19,098 patients from 25 phase III trials were analyzed. Compared with the standard of care, the addition of AT did not prolong OS (HR 0.98; 95% confidence interval [CI], 0.96-1.00; P = .1 and HR 0.97; 95% CI, 0.94-1.00; P = .06 for groups 1 and 2, respectively). However, there was a significant improvement in PFS with the addition of AT (HR 0.85; 95% CI, 0.79-0.91; P .00001 and HR 0.81; 95% CI, 0.75-0.88; P .00001 for groups 1 and 2, respectively) and overall RR (OR 1.61; 95% CI, 1.30-2.01; P .0001 and OR 1.72; 95% CI, 1.39-2.14; P .00001 for groups 1 and 2, respectively). This is the first meta-analysis including only all phase III trials with AT in NSCLC showing no significant effect on OS and an improvement in PFS and RR only. The role of AT in advanced NSCLC is still questionable; strong validated biomarkers are eagerly needed to predict which subgroup might benefit the most from such therapy.

A method for clustering billions of unidentified tandem mass spectra from shotgun proteomics experiments offers new ways of storing, organizing and analyzing proteomics data, with potential benefits to the entire proteomics community.

The fruit of Ziziphus jujuba, named as jujube or Chinese date, is used as a health supplement worldwide. Two kinds of jujubes are commonly found in the market: immature jujubes eaten as fruits, and mature jujubes employed as medicinal herbs. To study the variation of jujubes at two developmental stages, we investigated their chemical and biological properties by metabolic profiling and cellular assays. In NMR profiling, the levels of 11 metabolites were measured. Statistically differences in the levels of threonine, alanine, acetate, creatine, glucose, sucrose, and formate were found between mature and immature jujubes. In parallel, their neuro-protecting and erythropoietic activities were compared. The water extract of mature jujube possessed better effect in inducing neurofilament expression than that of the immature one, while immature jujube extract performed better in activating HRE-mediated transcriptional activity. These findings suggest the maturity of jujube has to be considered when it is being used for health food products.

Marine bacteria are the most widely distributed organisms in the ocean environment and produce a wide variety of secondary metabolites. However, traditional screening for bioactive natural compounds is greatly hindered by the lack of a systematic way of cataloguing the chemical profiles of bacterial strains found in nature. Here we present a chemical fingerprint database of marine bacteria based on their secondary metabolite profiles, acquired by high-resolution LC-MS. Till now, 1,430 bacterial strains spanning 168 known species collected from different marine environments were cultured and profiled. Using this database, we demonstrated that secondary metabolite profile similarity is approximately, but not always, correlated with taxonomical similarity. We also validated the ability of this database to find species-specific metabolites, as well as to discover known bioactive compounds from previously unknown sources. An online interface to this database, as well as the accompanying software, is provided freely for the community to use.

Mass spectrometry is the method of choice for deep and reliable exploration of the (human) proteome. Targeted mass spectrometry reliably detects and quantifies pre-determined sets of proteins in a complex biological matrix and is used in studies that rely on the quantitatively accurate and reproducible measurement of proteins across multiple samples. It requires the one-time, a priori generation of a specific measurement assay for each targeted protein. SWATH-MS is a mass spectrometric method that combines data-independent acquisition (DIA) and targeted data analysis and vastly extends the throughput of proteins that can be targeted in a sample compared to selected reaction monitoring (SRM). Here we present a compendium of highly specific assays covering more than 10,000 human proteins and enabling their targeted analysis in SWATH-MS datasets acquired from research or clinical specimens. This resource supports the confident detection and quantification of 50.9% of all human proteins annotated by UniProtKB/Swiss-Prot and is therefore expected to find wide application in basic and clinical research. Data are available via ProteomeXchange (PXD000953-954) and SWATHAtlas (SAL00016-35).