The use of proteotypic peptide libraries for protein identification

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

SpliceProt 2.0: A Sequence Repository of Human, Mouse, and Rat Proteoforms

SpliceProt 2.0 is a public proteogenomics database that aims to list the sequence of known proteins and potential new proteoforms in human, mouse, and rat proteomes. This updated repository provides an even broader range of computationally translated proteins and serves, for example, to aid with proteomic validation of splice variants absent from the reference UniProtKB/SwissProt database. We demonstrate the value of SpliceProt 2.0 to predict orthologous proteins between humans and murines based on transcript reconstruction, sequence annotation and detection at the transcriptome and proteome levels. In this release, the annotation data used in the reconstruction of transcripts based on the methodology of ternary matrices were acquired from new databases such as Ensembl, UniProt, and APPRIS. Another innovation implemented in the pipeline is the exclusion of transcripts predicted to be susceptible to degradation through the NMD pathway. Taken together, our repository and its applications represent a valuable resource for the proteogenomics community.

High-Field Asymmetric Waveform Ion Mobility Spectrometry: Practical Alternative for Cardiac Proteome Sample Processing

Heart tissue sample preparation for mass spectrometry (MS) analysis that includes prefractionation reduces the cellular protein dynamic range and increases the relative abundance of nonsarcomeric proteins. We previously described "IN-Sequence" (IN-Seq) where heart tissue lysate is sequentially partitioned into three subcellular fractions to increase the proteome coverage more than a single direct tissue analysis by mass spectrometry. Here, we report an adaptation of the high-field asymmetric ion mobility spectrometry (FAIMS) coupled to mass spectrometry, and the establishment of a simple one step sample preparation coupled with gas-phase fractionation. The FAIMS approach substantially reduces manual sample handling, significantly shortens the MS instrument processing time, and produces unique protein identification and quantification approximating the commonly used IN-Seq method in less time.

AlacatDesigner─Computational Design of Peptide Concatamers for Protein Quantitation

Protein quantitation via mass spectrometry relies on peptide proxies for the parent protein from which abundances are estimated. Owing to the variability in signal from individual peptides, accurate absolute quantitation usually relies on the addition of an external standard. Typically, this involves stable isotope-labeled peptides, delivered singly or as a concatenated recombinant protein. Consequently, the selection of the most appropriate surrogate peptides and the attendant design in recombinant proteins termed QconCATs are challenges for proteome science. QconCATs can now be built in a "a-la-carte" assembly method using synthetic biology: ALACATs. To assist their design, we present "AlacatDesigner", a tool that supports the peptide selection for recombinant protein standards based on the user's target protein. The user-customizable tool considers existing databases, occurrence in the literature, potential post-translational modifications, predicted miscleavage, predicted divergence of the peptide and protein quantifications, and ionization potential within the mass spectrometer. We show that peptide selections are enriched for good proteotypic and quantotypic candidates compared to empirical data. The software is freely available to use either via a web interface AlacatDesigner, downloaded as a Desktop application or imported as a Python package for the command line interface or in scripts.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

Screening a Resource of Recombinant Protein Fragments for Targeted Proteomics

The availability of proteomics resources hosting protein and peptide standards, as well as the data describing their analytical performances, will continue to enhance our current capabilities to develop targeted proteomics methods for quantitative biology. This study describes the analysis of a resource of 26,840 individually purified recombinant protein fragments corresponding to more than 16,000 human protein-coding genes. The resource was screened to identify proteotypic peptides suitable for targeted proteomics efforts, and we report LC-MS/MS assay coordinates for more than 25,000 proteotypic peptides, corresponding to more than 10,000 unique proteins. Additionally, peptide formation and digestion kinetics were, for a subset of the standards, monitored using a time-course protocol involving parallel digestion of isotope-labeled recombinant protein standards and endogenous human plasma proteins. We show that the strategy by adding isotope-labeled recombinant proteins before trypsin digestion enables short digestion protocols (≤60 min) with robust quantitative precision. In a proof-of-concept study, we quantified 23 proteins in human plasma using assay parameters defined in our study and used the standards to describe distinct clusters of individuals linked to different levels of LPA, APOE, SERPINA5, and TFRC. In summary, we describe the use and utility of a resource of recombinant proteins to identify proteotypic peptides useful for targeted proteomics assay development.

Application of immobilized ATP to the study of NLRP inflammasomes

The NLRP proteins are a subfamily of the NOD-like receptor (NLR) innate immune sensors that possess an ATP-binding NACHT domain. As the most well studied member, NLRP3 can initiate the assembly process of a multiprotein complex, termed the inflammasome, upon detection of a wide range of microbial products and endogenous danger signals and results in the activation of pro-caspase-1, a cysteine protease that regulates multiple host defense pathways including cytokine maturation. Dysregulated NLRP3 activation contributes to inflammation and the pathogenesis of several chronic diseases, and the ATP-binding properties of NLRPs are thought to be critical for inflammasome activation. In light of this, we examined the utility of immobilized ATP matrices in the study of NLRP inflammasomes. Using NLRP3 as the prototypical member of the family, P-linked ATP Sepharose was determined to be a highly-effective capture agent. In subsequent examinations, P-linked ATP Sepharose was used as an enrichment tool to enable the effective profiling of NLRP3-biomarker signatures with selected reaction monitoring-mass spectrometry (SRM-MS). Finally, ATP Sepharose was used in combination with a fluorescence-linked enzyme chemoproteomic strategy (FLECS) screen to identify potential competitive inhibitors of NLRP3. The identification of a novel benzo[d]imidazol-2-one inhibitor that specifically targets the ATP-binding and hydrolysis properties of the NLRP3 protein implies that ATP Sepharose and FLECS could be applied other NLRPs as well.

Targeted Proteomics to Study Mitochondrial Biology

Targeted mass spectrometry in the selected or parallel reaction monitoring (SRM or PRM) mode is a widely used methodology to quantify proteins based on so-called signature or proteotypic peptides. SRM has the advantage of being able to quantify a range of proteins in a single analysis, for example, to measure the level of enzymes comprising a biochemical pathway. In this chapter, we will detail how to set up an SRM assay on the example of the mitochondrial protein succinate dehydrogenase [ubiquinone] flavoprotein subunit (mouse UniProt-code Q8K2B3). First, we will outline the in silico assay design including the choice of peptides based on a range of properties. We will further delineate different quantification strategies and introduce the reader to LC-MS assay development including the selection of the optimal peptide charge state and fragment ions as well as a discussion of the dynamic range of detection. The chapter will close with an application from the area of mitochondrial biology related to the quantification of a set of proteins isolated from mouse liver mitochondria in a study on mitochondrial respiratory flux decline in aging mouse muscle.

Robust determination of differential abundance in shotgun proteomics using nonparametric statistics

Label-free shotgun mass spectrometry enables the detection of significant changes in protein abundance between different conditions. Due to often limited cohort sizes or replication, large ratios of potential protein markers to number of samples, as well as multiple null measurements pose important technical challenges to conventional parametric models. From a statistical perspective, a scenario similar to that of unlabeled proteomics is encountered in genomics when looking for differentially expressed genes. Still, the difficulty of detecting a large fraction of the true positives without a high false discovery rate is arguably greater in proteomics due to even smaller sample sizes and peptide-to-peptide variability in detectability. These constraints argue for nonparametric (or distribution-free) tests on normalized peptide values, thus minimizing the number of free parameters, as well as for measuring significance with permutation testing. We propose such a procedure with a class-based statistic, no parametric assumptions, and no parameters to select other than a nominal false discovery rate. Our method was tested on a new dataset which is available via ProteomeXchange with identifier PXD006447. The dataset was prepared using a standard proteolytic digest of a human protein mixture at 1.5-fold to 3-fold protein concentration changes and diluted into a constant background of yeast proteins. We demonstrate its superiority relative to other approaches in terms of the realized sensitivity and realized false discovery rates determined by ground truth, and recommend it for detecting differentially abundant proteins from MS data.

MS Western, a Method of Multiplexed Absolute Protein Quantification is a Practical Alternative to Western Blotting

Absolute quantification of proteins elucidates the molecular composition, regulation and dynamics of multiprotein assemblies and networks. Here we report on a method termed MS Western that accurately determines the molar abundance of dozens of user-selected proteins at the subfemtomole level in whole cell or tissue lysates without metabolic or chemical labeling and without using specific antibodies. MS Western relies on GeLC-MS/MS and quantifies proteins by in-gel codigestion with an isotopically labeled QconCAT protein chimera composed of concatenated proteotypic peptides. It requires no purification of the chimera and relates the molar abundance of all proteotypic peptides to a single reference protein. In comparative experiments, MS Western outperformed immunofluorescence Western blotting by the protein detection specificity, linear dynamic range and sensitivity of protein quantification. To validate MS Western in an in vivo experiment, we quantified the molar content of zebrafish core histones H2A, H2B, H3 and H4 during ten stages of early embryogenesis. Accurate quantification (CV<10%) corroborated the anticipated histones equimolar stoichiometry and revealed an unexpected trend in their total abundance.

Proteomic Data Storage and Sharing

With the advent of high-throughput genomic and proteomic techniques, there is a massive amount of multidimensional data being generated and has increased several orders of magnitude. But the amount of data that is cataloged in the central repositories and shared publicly with the scientific community does not correlate the same rate at which the data is generated. Here, in this chapter, we discuss various proteomics data repositories that are freely accessible to the researchers for further downstream meta-analysis.

Proteotypic Peptides and Their Applications

Recent advances in mass spectrometry based proteomic techniques and publicly available large proteomic repositories are being exploited to characterize the proteome of multiple organisms. While humongous amount of proteomic data is being acquired and analyzed, many biological questions still remain unanswered. Proteotypic peptides which uniquely represent target proteins or a protein isoform are used as an alternative strategy for protein identification in the field of immunological methods and targeted proteomic techniques. Using different computational approaches, resources and techniques used in the identification of proteotypic peptides of target proteins is discussed here.

A multicentric study to evaluate the use of relative retention times in targeted proteomics

Despite the maturity reached by targeted proteomic strategies, reliable and standardized protocols are urgently needed to enhance reproducibility among different laboratories and analytical platforms, facilitating a more widespread use in biomedical research. To achieve this goal, the use of dimensionless relative retention times (iRT), defined on the basis of peptide standard retention times (RT), has lately emerged as a powerful tool. The robustness, reproducibility and utility of this strategy were examined for the first time in a multicentric setting, involving 28 laboratories that included 24 of the Spanish network of proteomics laboratories (ProteoRed-ISCIII). According to the results obtained in this study, dimensionless retention time values (iRTs) demonstrated to be a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups both intra- and inter-laboratories. iRT values also showed very low variability over long time periods. Furthermore, parallel quantitative analyses showed a high reproducibility despite the variety of experimental strategies used, either MRM (multiple reaction monitoring) or pseudoMRM, and the diversity of analytical platforms employed.

BIOLOGICAL SIGNIFICANCE

From the very beginning of proteomics as an analytical science there has been a growing interest in developing standardized methods and experimental procedures in order to ensure the highest quality and reproducibility of the results. In this regard, the recent (2012) introduction of the dimensionless retention time concept has been a significant advance. In our multicentric (28 laboratories) study we explore the usefulness of this concept in the context of a targeted proteomics experiment, demonstrating that dimensionless retention time values is a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups.

Food allergen detection by mass spectrometry: the role of systems biology

Food allergy prevalence is rising worldwide, motivating the development of assays that can sensitively and reliably detect trace amounts of allergens in manufactured food. Mass spectrometry (MS) is a promising alternative to commonly employed antibody-based assays owing to its ability to quantify multiple proteins in complex matrices with high sensitivity. In this review, we discuss a targeted MS workflow for the quantitation of allergenic protein in food products that employs selected reaction monitoring (SRM). We highlight the aspects of SRM method development unique to allergen quantitation and identify opportunities for simplifying the process. One promising avenue identified through a comprehensive survey of published MS literature is the use of proteotypic peptides, which are peptides whose presence appears robust to variations in food matrix, sample preparation protocol, and MS instrumentation. We conclude that proteotypic peptides exist for a subset of allergenic milk, egg, and peanut proteins. For less studied allergens such as soy, wheat, fish, shellfish, and tree nuts, we offer guidance and tools for peptide selection and specificity verification as part of an interactive web database, the Allergen Peptide Browser (http://www.AllergenPeptideBrowser.org). With ongoing improvements in MS instrumentation, analysis software, and strategies for targeted quantitation, we expect an increasing role of MS as an analytical tool for ensuring regulatory compliance.

A proteomic evaluation of urinary changes associated with cardiopulmonary bypass

Background

The urinary proteome of patients undergoing cardiopulmonary bypass (CPB) may provide important insights into systemic and renal changes associated with the procedure. Such information may ultimately provide a basis to differentiate changes or properties associated with the development of acute kidney injury. While mass spectrometry (MS) analysis offers the potential for in-depth compositional analysis it is often limited in coverage and relative quantitation capacity. The aim of this study was to develop a process flow for the preparation and comparison of the intraoperative urinary proteome.

Methods

Urines were collected from patients at the start of CPB and 1-h into CPB. Pooled samples (n = 5) from each time point were processed using a modified Filter Assisted Sample Preparation protocol. The resulting peptides were analyzed by 2D-LC–MS/MS and by 1D-LC–MS/MS SWATH (Sequential Window acquisition of All Theoretical fragment ion spectra).

Results

The 2D-LC–MS/MS analysis identified 1324 proteins in the two pools, of which 744 were quantifiable. The SWATH approach provided quantitation for 730 proteins, 552 of which overlapped with the common population from the 2D-IDA results. Intensity correlation filtering between the two methods gave 475 proteins for biological interpretation. Proteins displaying greater than threefold changes (>log₂ 1.59) at 1-hour CPB relative to the initiation of CPB (26 down-regulated and 22 up-regulated) were selected for further analysis. Up-regulated proteins were enriched in GO terms related to humoral immune response, predominantly innate immunity (C4b, lactotransferrin, protein S100-A8, cathelicidin, myeloperoxidase) and extracellular matrix reorganization (e.g. MMP-9).

Conclusions

This study describes a scheme for processing urine from patients undergoing CPB for mass spectrometry-based analysis. The introduction of SWATH into the workflow offers a sample and instrument sparing approach to obtaining consistent in-depth sample analysis. The design of the methodology is such that it can be readily applied to large numbers of clinical samples with the potential for automation. The results also suggest that activation of the innate immune responses occur during cardiac bypass surgery.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-016-9118-9) contains supplementary material, which is available to authorized users.

Public proteomics data: How the field has evolved from sceptical inquiry to the promise of in silico proteomics

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Proteomics data sharing moved from validation to re-use.

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New tools and services make data very easily accessible.

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Metadata provision can still benefit from improvements.

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Quality control metrics will soon be reported along with submitted data.

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Data re-use will enable the advent of actual in silico proteomics.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis.

Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics

In shotgun proteomics, peptides are typically identified using database searching, which involves scoring acquired tandem mass spectra against peptides derived from standard protein sequence databases such as Uniprot, Refseq, or Ensembl. In this strategy, the sensitivity of peptide identification is known to be affected by the size of the search space. Therefore, creating a targeted sequence database containing only peptides likely to be present in the analyzed sample can be a useful technique for improving the sensitivity of peptide identification. In this study, we describe how targeted peptide databases can be created based on the frequency of identification in the global proteome machine database (GPMDB), the largest publicly available repository of peptide and protein identification data. We demonstrate that targeted peptide databases can be easily integrated into existing proteome analysis workflows and describe a computational strategy for minimizing any loss of peptide identifications arising from potential search space incompleteness in the targeted search spaces. We demonstrate the performance of our workflow using several data sets of varying size and sample complexity.

Interpretation of Tandem Mass Spectrometry (MSMS) Spectra for Peptide Analysis

The aim of this chapter is to give a short introduction to peptide analysis by mass spectrometry (MS) and interpretation of fragment mass spectra. Through examples and guidelines we demonstrate how to understand and validate search results and how to perform de novo sequencing based on the often very complex fragmentation pattern obtained by tandem mass spectrometry (also referred to as MSMS). The focus is on simple rules for interpretation of MSMS spectra of tryptic as well as non-tryptic peptides.

Systematic Analysis of Yeast Proteome Reveals Peptide Detectability Factors for Mass Spectrometry

Here we used a data-independent acquisition (DIA) method, Precursor Acquisition Independent from Ion Count (PAcIFIC), to systematically profile the S. cerevisiae proteome. Direct PAcIFIC analysis of a yeast whole cell lysate (WCL) yielded 90% reproducibility between replicates and detected approximately 2000 proteins. When combined with sub-cellular fractionation, reproducibility was equally high and the number of detected yeast proteins approached 5000. As noted previously, this unbiased DIA approach identified so-called "orphan" peptides that could only be detected by tandem mass spectra because there was no detectable precursor ion. Using this unique dataset we examined features associated with peptide detectability and demonstrated that orphans were more likely to arise from low copy number proteins than proteins with median or high copy number. Finally, an investigation into why some orphans also arose from high copy number proteins found that, aside from protein copy number, there was a bias toward physiochemical factors associated with regions flanking the proteolytic cleavage sites of orphan peptides. This suggested that those orphan peptides originating from high abundance proteins were likely the result of inefficient protease release, which has implications for quantitative bottom-up proteomics.

Whole cell, label free protein quantitation with data independent acquisition: quantitation at the MS2 level

Label free quantitation by measurement of peptide fragment signal intensity (MS2 quantitation) is a technique that has seen limited use due to the stochastic nature of data dependent acquisition (DDA). However, data independent acquisition has the potential to make large scale MS2 quantitation a more viable technique. In this study we used an implementation of data independent acquisition--SWATH--to perform label free protein quantitation in a model bacterium Clostridium stercorarium. Four tryptic digests analyzed by SWATH were probed by an ion library containing information on peptide mass and retention time obtained from DDA experiments. Application of this ion library to SWATH data quantified 1030 proteins with at least two peptides quantified (∼ 40% of predicted proteins in the C. stercorarium genome) in each replicate. Quantitative results obtained were very consistent between biological replicates (R(2) ∼ 0.960). Protein quantitation by summation of peptide fragment signal intensities was also highly consistent between biological replicates (R(2) ∼ 0.930), indicating that this approach may have increased viability compared to recent applications in label free protein quantitation. SWATH based quantitation was able to consistently detect differences in relative protein quantity and it provided coverage for a number of proteins that were missed in some samples by DDA analysis.

Expediting SRM assay development for large-scale targeted proteomics experiments

Because of its high sensitivity and specificity, selected reaction monitoring (SRM)-based targeted proteomics has become increasingly popular for biological and translational applications. Selection of optimal transitions and optimization of collision energy (CE) are important assay development steps for achieving sensitive detection and accurate quantification; however, these steps can be labor-intensive, especially for large-scale applications. Herein, we explored several options for accelerating SRM assay development evaluated in the context of a relatively large set of 215 synthetic peptide targets. We first showed that HCD fragmentation is very similar to that of CID in triple quadrupole (QQQ) instrumentation and that by selection of the top 6 y fragment ions from HCD spectra, >86% of the top transitions optimized from direct infusion with QQQ instrumentation are covered. We also demonstrated that the CE calculated by existing prediction tools was less accurate for 3+ precursors and that a significant increase in intensity for transitions could be obtained using a new CE prediction equation constructed from the present experimental data. Overall, our study illustrated the feasibility of expediting the development of larger numbers of high-sensitivity SRM assays through automation of transition selection and accurate prediction of optimal CE to improve both SRM throughput and measurement quality.

PeptideManager: a peptide selection tool for targeted proteomic studies involving mixed samples from different species

The search for clinically useful protein biomarkers using advanced mass spectrometry approaches represents a major focus in cancer research. However, the direct analysis of human samples may be challenging due to limited availability, the absence of appropriate control samples, or the large background variability observed in patient material. As an alternative approach, human tumors orthotopically implanted into a different species (xenografts) are clinically relevant models that have proven their utility in pre-clinical research. Patient derived xenografts for glioblastoma have been extensively characterized in our laboratory and have been shown to retain the characteristics of the parental tumor at the phenotypic and genetic level. Such models were also found to adequately mimic the behavior and treatment response of human tumors. The reproducibility of such xenograft models, the possibility to identify their host background and perform tumor-host interaction studies, are major advantages over the direct analysis of human samples. At the proteome level, the analysis of xenograft samples is challenged by the presence of proteins from two different species which, depending on tumor size, type or location, often appear at variable ratios. Any proteomics approach aimed at quantifying proteins within such samples must consider the identification of species specific peptides in order to avoid biases introduced by the host proteome. Here, we present an in-house methodology and tool developed to select peptides used as surrogates for protein candidates from a defined proteome (e.g., human) in a host proteome background (e.g., mouse, rat) suited for a mass spectrometry analysis. The tools presented here are applicable to any species specific proteome, provided a protein database is available. By linking the information from both proteomes, PeptideManager significantly facilitates and expedites the selection of peptides used as surrogates to analyze proteins of interest.

Conserved peptide fragmentation as a benchmarking tool for mass spectrometers and a discriminating feature for targeted proteomics

Quantifying the similarity of spectra is an important task in various areas of spectroscopy, for example, to identify a compound by comparing sample spectra to those of reference standards. In mass spectrometry based discovery proteomics, spectral comparisons are used to infer the amino acid sequence of peptides. In targeted proteomics by selected reaction monitoring (SRM) or SWATH MS, predetermined sets of fragment ion signals integrated over chromatographic time are used to identify target peptides in complex samples. In both cases, confidence in peptide identification is directly related to the quality of spectral matches. In this study, we used sets of simulated spectra of well-controlled dissimilarity to benchmark different spectral comparison measures and to develop a robust scoring scheme that quantifies the similarity of fragment ion spectra. We applied the normalized spectral contrast angle score to quantify the similarity of spectra to objectively assess fragment ion variability of tandem mass spectrometric datasets, to evaluate portability of peptide fragment ion spectra for targeted mass spectrometry across different types of mass spectrometers and to discriminate target assays from decoys in targeted proteomics. Altogether, this study validates the use of the normalized spectral contrast angle as a sensitive spectral similarity measure for targeted proteomics, and more generally provides a methodology to assess the performance of spectral comparisons and to support the rational selection of the most appropriate similarity measure. The algorithms used in this study are made publicly available as an open source toolset with a graphical user interface.

Bioinformatics for proteomics: opportunities at the interface between the scientists, their experiments, and the community

Within the last decade, bioinformatics has moved from command line scripts dedicated to single experiments towards production grade software integrated in experimental workflows providing a rich environment for biological investigation. Located at the interface between the scientists, their experiments, and the community, bioinformatics acts as a gateway to a wide source of information. This chapter does not list tools and methods, but rather hints at how bioinformatics can help in improving biological projects, all the way from their initial design to the dissemination of the results.

Redox regulation of methionine aminopeptidase 2 activity

Protein translation is initiated with methionine in eukaryotes, and the majority of proteins have their N-terminal methionine removed by methionine aminopeptidases (MetAP1 and MetAP2) prior to action. Methionine removal can be important for protein function, localization, or stability. No mechanism of regulation of MetAP activity has been identified. MetAP2, but not MetAP1, contains a single Cys(228)-Cys(448) disulfide bond that has an -RHStaple configuration and links two β-loop structures, which are hallmarks of allosteric disulfide bonds. From analysis of crystal structures and using mass spectrometry and activity assays, we found that the disulfide bond exists in oxidized and reduced states in the recombinant enzyme. The disulfide has a standard redox potential of -261 mV and is efficiently reduced by the protein reductant, thioredoxin, with a rate constant of 16,180 m(-1) s(-1). The MetAP2 disulfide bond also exists in oxidized and reduced states in glioblastoma tumor cells, and stressing the cells by oxygen or glucose deprivation results in more oxidized enzyme. The Cys(228)-Cys(448) disulfide is at the rim of the active site and is only three residues distant from the catalytic His(231), which suggested that cleavage of the bond would influence substrate hydrolysis. Indeed, oxidized and reduced isoforms have different catalytic efficiencies for hydrolysis of MetAP2 peptide substrates. These findings indicate that MetAP2 is post-translationally regulated by an allosteric disulfide bond, which controls substrate specificity and catalytic efficiency.

Proteomic and systems biology analysis of the monocyte response to Coxiella burnetii infection

Coxiella burnetii is an obligate intracellular bacterial pathogen and the causative agent of Q fever. Chronic Q fever can produce debilitating fatigue and C. burnetii is considered a significant bioterror threat. C. burnetii occupies the monocyte phagolysosome and although prior work has explained features of the host-pathogen interaction, many aspects are still poorly understood. We have conducted a proteomic investigation of human Monomac I cells infected with the Nine Mile Phase II strain of C. burnetii and used the results as a framework for a systems biology model of the host response. Our principal methodology was multiplex differential 2D gel electrophoresis using ZDyes, a new generation of covalently linked fluorescent protein detection dyes under development at Montana State University. The 2D gel analysis facilitated the detection of changes in posttranslational modifications on intact proteins in response to infection. The systems model created from our data a framework for the design of experiments to seek a deeper understanding of the host-pathogen interactions.

Automated assignments of N- and O-site specific glycosylation with extensive glycan heterogeneity of glycoprotein mixtures

Site-specific glycosylation (SSG) of glycoproteins remains a considerable challenge and limits further progress in the areas of proteomics and glycomics. Effective methods require new approaches in sample preparation, detection, and data analysis. While the field has advanced in sample preparation and detection, automated data analysis remains an important goal. A new bioinformatics approach implemented in software called GP Finder automatically distinguishes correct assignments from random matches and complements experimental techniques that are optimal for glycopeptides, including nonspecific proteolysis and high mass resolution liquid chromatography/tandem mass spectrometry (LC/MS/MS). SSG for multiple N- and O-glycosylation sites, including extensive glycan heterogeneity, was annotated for single proteins and protein mixtures with a 5% false-discovery rate, generating hundreds of nonrandom glycopeptide matches and demonstrating the proof-of-concept for a self-consistency scoring algorithm shown to be compliant with the target-decoy approach (TDA). The approach was further applied to a mixture of N-glycoproteins from unprocessed human milk and O-glycoproteins from very-low-density-lipoprotein (vLDL) particles.

Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra

Searching spectral libraries in MS/MS is an important new approach to improving the quality of peptide and protein identification. The idea relies on the observation that ion intensities in an MS/MS spectrum of a given peptide are generally reproducible across experiments, and thus, matching between spectra from an experiment and the spectra of previously identified peptides stored in a spectral library can lead to better peptide identification compared to the traditional database search. However, the use of libraries is greatly limited by their coverage of peptide sequences: even for well-studied organisms a large fraction of peptides have not been previously identified. To address this issue, we propose to expand spectral libraries by predicting the MS/MS spectra of peptides based on the spectra of peptides with similar sequences. We first demonstrate that the intensity patterns of dominant fragment ions between similar peptides tend to be similar. In accordance with this observation, we develop a neighbor-based approach that first selects peptides that are likely to have spectra similar to the target peptide and then combines their spectra using a weighted K-nearest neighbor method to accurately predict fragment ion intensities corresponding to the target peptide. This approach has the potential to predict spectra for every peptide in the proteome. When rigorous quality criteria are applied, we estimate that the method increases the coverage of spectral libraries available from the National Institute of Standards and Technology by 20-60%, although the values vary with peptide length and charge state. We find that the overall best search performance is achieved when spectral libraries are supplemented by the high quality predicted spectra.

Crowdsourcing in proteomics: public resources lead to better experiments

With the growing interest in the field of proteomics, the amount of publicly available proteome resources has also increased dramatically. This means that there are many useful resources available for almost all aspects of a proteomics experiment. However, it remains vital to use the right resource, for the right purpose, at the right time. This review is therefore meant to aid the reader in obtaining an overview of the available resources and their application, thus providing the necessary background to choose the appropriate resources for the experiment at hand. Many of the resources are also taking advantage of so-called crowdsourcing to maximize the potential of the resource. What this means and how this can improve future experiments will also be discussed. The text roughly follows the steps involved in a proteomics experiment, starting with the planning of the experiment, via the processing of the data and the analysis of the results, to the community-wide sharing of the produced data.

Acclimation to different depths by the marine angiosperm Posidonia oceanica: transcriptomic and proteomic profiles

For seagrasses, seasonal and daily variations in light and temperature represent the mains factors driving their distribution along the bathymetric cline. Changes in these environmental factors, due to climatic and anthropogenic effects, can compromise their survival. In a framework of conservation and restoration, it becomes crucial to improve our knowledge about the physiological plasticity of seagrass species along environmental gradients. Here, we aimed to identify differences in transcriptomic and proteomic profiles, involved in the acclimation along the depth gradient in the seagrass Posidonia oceanica, and to improve the available molecular resources in this species, which is an important requisite for the application of eco-genomic approaches. To do that, from plant growing in shallow (-5 m) and deep (-25 m) portions of a single meadow, (i) we generated two reciprocal Expressed Sequences Tags (EST) libraries using a Suppressive Subtractive Hybridization (SSH) approach, to obtain depth/specific transcriptional profiles, and (ii) we identified proteins differentially expressed, using the highly innovative USIS mass spectrometry methodology, coupled with 1D-SDS electrophoresis and labeling free approach. Mass spectra were searched in the open source Global Proteome Machine (GPM) engine against plant databases and with the X!Tandem algorithm against a local database. Transcriptional analysis showed both quantitative and qualitative differences between depths. EST libraries had only the 3% of transcripts in common. A total of 315 peptides belonging to 64 proteins were identified by mass spectrometry. ATP synthase subunits were among the most abundant proteins in both conditions. Both approaches identified genes and proteins in pathways related to energy metabolism, transport and genetic information processing, that appear to be the most involved in depth acclimation in P. oceanica. Their putative rules in acclimation to depth were discussed.

Processing of Mass Spectrometry Data in Clinical Applications

Mass spectrometry-based proteomics has become the leading approach for analyzing complex biological samples at a large-scale level. Its importance for clinical applications is more and more increasing, thanks to the development of high-performing instruments which allow the discovery of disease-specific biomarkers and an automated and rapid protein profiling of the analyzed samples. In this scenario, the large-scale production of proteomic data has driven the development of specific bioinformatic tools to assist researchers during the discovery processes. Here, we discuss the main methods, algorithms, and procedures to identify and use biomarkers for clinical and research purposes. In particular, we have been focused on quantitative approaches, the identification of proteotypic peptides, and the classification of samples, using proteomic data. Finally, this chapter is concluded by reporting the integration of experimental data with network datasets, as valuable instrument for identifying alterations that underline the emergence of specific phenotypes. Based on our experience, we show some examples taking into consideration experimental data obtained by multidimensional protein identification technology (MudPIT) approach.

Design and application of a data-independent precursor and product ion repository

The functional design and application of a data-independent LC-MS precursor and product ion repository for protein identification, quantification, and validation is conceptually described. The ion repository was constructed from the sequence search results of a broad range of discovery experiments investigating various tissue types of two closely related mammalian species. The relative high degree of similarity in protein complement, ion detection, and peptide and protein identification allows for the analysis of normalized precursor and product ion intensity values, as well as standardized retention times, creating a multidimensional/orthogonal queryable, qualitative, and quantitative space. Peptide ion map selection for identification and quantification is primarily based on replication and limited variation. The information is stored in a relational database and is used to create peptide- and protein-specific fragment ion maps that can be queried in a targeted fashion against the raw or time aligned ion detections. These queries can be conducted either individually or as groups, where the latter affords pathway and molecular machinery analysis of the protein complement. The presented results also suggest that peptide ionization and fragmentation efficiencies are highly conserved between experiments and practically independent of the analyzed biological sample when using similar instrumentation. Moreover, the data illustrate only minor variation in ionization efficiency with amino acid sequence substitutions occurring between species. Finally, the data and the presented results illustrate how LC-MS performance metrics can be extracted and utilized to ensure optimal performance of the employed analytical workflows.

Implicit biology in peptide spectral libraries

Mass spectral libraries are collections of mass spectra curated specifically to facilitate the identification of small molecules, metabolites, and short peptides. One of the most comprehensive peptide spectral libraries is curated by NIST and contains upward of half a million annotated spectra dominated by human and model organisms including budding yeast and mouse. While motivated primarily by the technological goal of increasing sensitivity and specificity in spectral identification, we have found that the NIST spectral library constitutes a surprisingly rich source of biological knowledge. In this Article, we show that data-mining of these published libraries while applying strict empirical thresholds yields many characteristics of protein biology. In particular, we demonstrate that the size and increasingly comprehensive nature of these libraries, generated from whole-proteome digests, enables inference from the presence but crucially also from the absence of spectra for individual peptides. We illustrate implicit biological trends that lead to significant absence of spectra accounted for by complex post-translational modifications and overlooked proteolytic sites. We conclude that many subtle biological signatures such as genetic variants, regulated proteolysis, and post-translational modifications are exposed through the systematic mining of spectral collections originally compiled as general-purpose, technology-oriented resources.

Proteomic and systems biology analysis of monocytes exposed to securinine, a GABA(A) receptor antagonist and immune adjuvant

Securinine, a GABA(A) receptor antagonist, has been reported to enhance monocyte cell killing of Coxiella burnetii without obvious adverse effects in vivo. We employed multiplex 2D gel electrophoresis using Zdyes, a new generation of covalently linked fluorescent differential protein detection dyes to analyze changes in the monocyte proteome in response to Securinine. Securinine antagonism of GABA(A) receptors triggers the activation of p38. We used the differential protein expression results to guide a search of the literature and network analysis software to construct a systems biology model of the effect of Securinine on monocytes. The model suggests that various metabolic modulators (fatty acid binding protein 5, inosine 5'-monophosphate dehydrogenase, and thioredoxin) are at least partially reshaping the metabolic landscape within the monocytes. The actin bundling protein L-plastin, and the Ca(2+) binding protein S100A4 also appear to have important roles in the immune response stimulated by Securinine. Fatty acid binding protein 5 (FABP5) may be involved in effecting lipid raft composition, inflammation, and hormonal regulation of monocytes, and the model suggests that FABP5 may be a central regulator of metabolism in activated monocytes. The model also suggests that the heat shock proteins have a significant impact on the monocyte immune response. The model provides a framework to guide future investigations into the mechanisms of Securinine action and with elaboration may help guide development of new types of immune adjuvants.

Bioinformatic challenges in targeted proteomics

Selected reaction monitoring mass spectrometry is an emerging targeted proteomics technology that allows for the investigation of complex protein samples with high sensitivity and efficiency. It requires extensive knowledge about the sample for the many parameters needed to carry out the experiment to be set appropriately. Most studies today rely on parameter estimation from prior studies, public databases, or from measuring synthetic peptides. This is efficient and sound, but in absence of prior data, de novo parameter estimation is necessary. Computational methods can be used to create an automated framework to address this problem. However, the number of available applications is still small. This review aims at giving an orientation on the various bioinformatical challenges. To this end, we state the problems in classical machine learning and data mining terms, give examples of implemented solutions and provide some room for alternatives. This will hopefully lead to an increased momentum for the development of algorithms and serve the needs of the community for computational methods. We note that the combination of such methods in an assisted workflow will ease both the usage of targeted proteomics in experimental studies as well as the further development of computational approaches.

Intact proteome fractionation strategies compatible with mass spectrometry

Proteome fractionation refers to separation at the level of intact proteins. Proteome fractionation may precede sample digestion and subsequent peptide-level separation and detection (i.e., bottom-up mass spectrometry [MS]). For top-down MS, proteome fractionation acts as a stand-alone separation platform, since intact proteins are directly analyzed by the mass spectrometer. Regardless of the MS identification strategy, separation of intact proteins has clear benefits as a result of decreasing sample complexity. However, this stage of the workflow also creates considerable challenges, which are generally absent from the counterpart peptide separation experiment. For example, maintaining protein solubility is a key concern before, during and after separation. To this end, surfactants such as sodium dodecyl sulfate may be employed during fractionation, so long as they are eliminated prior to MS. In this article, current strategies for proteome fractionation in a MS-compatible format are reviewed, illustrating the challenges and outlooks on this important aspect of proteomics.

Uromodulin and α(1)-antitrypsin urinary peptide analysis to differentiate glomerular kidney diseases

BACKGROUND/AIMS

Glomerular kidney disease (GKD) is suspected in patients based on proteinuria, but its diagnosis relies primarily on renal biopsy. We used urine peptide profiling as a noninvasive means to link GKD-associated changes to each glomerular entity.

METHODS

Urinary peptide profiles of 60 biopsy-proven glomerular patients and 14 controls were analyzed by combining magnetic bead peptide enrichment, MALDI-TOF MS analysis, and ClinProTools v2.0 to select differential peptides. Tentative identification of the differential peptides was carried out by HPLC-MS/MS.

RESULTS

The HPLC-MS/MS results suggest that uromodulin (UMOD; m/z: 1682, 1898 and 1913) and α(1)-antitrypsin (A1AT; m/z: 1945, 2392 and 2505) are differentially expressed urinary peptides that distinguish between GKD patients and healthy subjects. Low UMOD and high A1AT peptide abundance was observed in 80-92% of patients with GKD. Proliferative forms of GKD were distinguished from nonproliferative forms, based on a combination of UMOD and A1AT peptides. Nonproliferative forms correlated with higher A1AT peptide levels - focal segmental glomerulosclerosis was linked more closely to high levels of the m/z 1945 peptide than minimal change disease.

CONCLUSION

We describe a workflow - urinary peptide profiling coupled with histological findings - that can be used to distinguish GKD accurately and noninvasively, particularly its nonproliferative forms.

Label-free protein quantitation using weighted spectral counting

Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of MS/MS spectra attributable to each protein, provided that one corrects for differential MS-detectability of the contributing peptides. We describe the use of a method, APEX, which calculates Absolute Protein EXpression levels based on learned correction factors, MS/MS spectral counts, and each protein's probability of correct identification.The APEX-based calculations consist of three parts: (1) Using training data, peptide sequences and their sequence properties, a model is built that can be used to estimate MS-detectability (O (i)) for any given protein. (2) Absolute abundances of proteins measured in an MS/MS experiment are calculated with information from spectral counts, identification probabilities and the learned O (i)-values. (3) Simple statistics allow for significance analysis of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span more than four orders of magnitude and are applicable to mixtures of hundreds to thousands of proteins from any type of organism.