Analysis, statistical validation and dissemination of large-scale proteomics datasets generated by tandem MS

Proteomic analysis of differential expression of lung proteins in response to highly pathogenic avian influenza virus infection in chickens

Elucidation of the molecular pathogenesis underlying virus-host interactions is important for the development of new diagnostic and therapeutic strategies against highly pathogenic avian influenza (HPAI) virus infection in chickens. However, the pathogenesis of HPAI virus in chickens is not completely understood. To identify the intracellular signaling pathways and critical host proteins associated with influenza pathogenesis, we analyzed the lung proteome of a chicken infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala). Mass spectrometry data sets were searched against the chicken UniProt reference database. At the local false discovery rate level of 5%, a total of 3313 proteins with the presence of at least one unique peptide were identified in the chicken lung proteome datasets. Differential expression analysis of these proteins showed that 247 and 1754 proteins were downregulated at 12 h and 48 h postinfection, respectively. We observed expression of proteins of the predominant signaling pathways, including Toll-like receptors (TLRs), retinoic acid-inducible gene I-like receptors (RLRs), NOD-like receptors (NLRs), and JAK-STAT signaling. Activation of these pathways is associated with the cytokine storm effect and thus may be the cause of the severity of HPAI H5N1 infection in chickens. We also observed the expression of myeloid differentiation primary response protein (MyD88), inhibitor of nuclear factor kappa B kinase subunit beta (IKBKB), interleukin 1 receptor associated kinase 4 (IRAK4), RELA proto-oncogene NF-κB subunit (RELA), and mitochondrial antiviral signaling protein (MAVS), which are involved in critical signaling pathways, as well as other, less-commonly identified proteins such as hepatocyte nuclear factor 4 alpha (HNF4A), ELAV-like RNA binding protein 1 (ELAVL1), fibronectin 1 (FN1), COP9 signalosome subunit 5 (COPS5), cullin 1 (CUL1), breast cancer type 1 susceptibility protein (BRCA1), and the FYN proto-oncogene Src family tyrosine kinase (FYN) as main hub proteins that might play important roles in influenza pathogenesis in chickens. In summary, we identified the signaling pathways and the proteomic determinants associated with disease pathogenesis in chickens infected with HPAI H5N1 virus.

Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches

Spectrum annotation is a challenging task due to the presence of unexpected peptide fragmentation ions as well as the inaccuracy of the detectors of the spectrometers. We present a deep convolutional neural network, called Slider, which learns an optimal feature extraction in its kernels for scoring mass spectrometry (MS)/MS spectra to increase the number of spectrum annotations with high confidence. Experimental results using publicly available data sets show that Slider can annotate slightly more spectra than the state-of-the-art methods (BoltzMatch, Res-EV, Prosit), albeit 2-10 times faster. More interestingly, Slider provides only 2-4% fewer spectrum annotations with low-resolution fragmentation information than other methods with high-resolution information. This means that Slider can exploit nearly as much information from the context of low-resolution spectrum peaks as the high-resolution fragmentation information can provide for other scoring methods. Thus, Slider can be an optimal choice for practitioners using old spectrometers with low-resolution detectors.

Annotation of tandem mass spectrometry data using stochastic neural networks in shotgun proteomics

MOTIVATION

The discrimination ability of score functions to separate correct from incorrect peptide-spectrum-matches in database-searching-based spectrum identification is hindered by many superfluous peaks belonging to unexpected fragmentation ions or by the lacking peaks of anticipated fragmentation ions.

RESULTS

Here, we present a new method, called BoltzMatch, to learn score functions using a particular stochastic neural networks, called restricted Boltzmann machines, in order to enhance their discrimination ability. BoltzMatch learns chemically explainable patterns among peak pairs in the spectrum data, and it can augment peaks depending on their semantic context or even reconstruct lacking peaks of expected ions during its internal scoring mechanism. As a result, BoltzMatch achieved 50% and 33% more annotations on high- and low-resolution MS2 data than XCorr at a 0.1% false discovery rate in our benchmark; conversely, XCorr yielded the same number of spectrum annotations as BoltzMatch, albeit with 4-6 times more errors. In addition, BoltzMatch alone does yield 14% more annotations than Prosit (which runs with Percolator), and BoltzMatch with Percolator yields 32% more annotations than Prosit at 0.1% FDR level in our benchmark.

AVAILABILITY AND IMPLEMENTATION

BoltzMatch is freely available at: https://github.com/kfattila/BoltzMatch.

CONTACT

akerteszfarkas@hse.ru.

SUPPORTING INFORMATION

Supplementary data are available at Bioinformatics online.

A Markov Chain Monte Carlo Method for Estimating the Statistical Significance of Proteoform Identifications by Top-Down Mass Spectrometry

Top-down mass spectrometry is capable of identifying whole proteoform sequences with multiple post-translational modifications because it generates tandem mass spectra directly from intact proteoforms. Many software tools, such as ProSightPC, MSPathFinder, and TopMG, have been proposed for identifying proteoforms with modifications. In these tools, various methods are employed to estimate the statistical significance of identifications. However, most existing methods are designed for proteoform identifications without modifications, and the challenge remains for accurately estimating the statistical significance of proteoform identifications with modifications. Here we propose TopMCMC, a method that combines a Markov chain random walk algorithm and a greedy algorithm for assigning statistical significance to matches between spectra and protein sequences with variable modifications. Experimental results showed that TopMCMC achieved high accuracy in estimating E-values and false discovery rates of identifications in top-down mass spectrometry. Coupled with TopMG, TopMCMC identified more spectra than the generating function method from an MCF-7 top-down mass spectrometry data set.

Mass Spectrometry-Based Biomarkers in Drug Development

Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increases the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.

ASV-ID, a Proteogenomic Workflow To Predict Candidate Protein Isoforms on the Basis of Transcript Evidence

One of the goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to map and characterize the functions of protein isoforms produced by alternative splicing of genes. However, identifying alternative splice variants (ASVs) via mass spectrometry remains a major challenge, because ASVs usually contain highly homologous peptide sequences. A routine protein sequence analysis suggests that more than half of the investigated proteins do not generate two or more uniquely mapping peptides that would enable their isoforms to be distinguished. Here, we develop a new proteogenomics method, named "ASV-ID" (alternative splicing variants identification), which enables identification of ASVs by using a cell type-specific protein sequence database that is supported by RNA-Seq data. Using this workflow, we identify 1935 distinct proteins under highly stringent conditions. In fact, transcript evidence on these 841 proteins helps us distinguish them from other isoforms, despite the fact that these proteins are not predicted to make 2 or more uniquely mapping peptides. We also demonstrate that ASV-ID enables detection of 19 differently expressed isoforms present in several cell lines. Thus, a new workflow using ASV-ID has the potential to map yet-to-be-identified difficult protein isoforms in a simple and robust way.

Analysis of the 3H8 antigen of Candida albicans reveals new aspects of the organization of fungal cell wall proteins

The walls of both, yeast and mycelial cells of Candida albicans possess a species-specific antigen that is recognized by a monoclonal antibody (MAb 3H8). This antigen can be extracted in the form of a very high Mr complex, close or over 106 Da, by treatment, with β-1,3-glucanase, β mercaptoethanol or dithothreitol, or mild alkali, but not by saturated hydrogen fluoride (HF) in pyridine, suggesting that the complex is bound to wall β-1,3 glucans, and to proteins by disulfide bonds, but not to β-1,6 glucans. Through its sensitivity to trypsin and different deglycosylation procedures, it was concluded that the epitope is associated to a glycoprotein containing N-glycosidic, but not O-glycosidic mannan moieties. By means of electrophoresis in polycrylamide gradient gels, followed by mass spectrometric analysis, the epitope was pinpointed to a very high MW complex containing Agglutinin-Like Sequence (ALS) family proteins, and other cytoplasmic, membrane and secreted proteins. The components of this complex are bound by unknown covalent bonds. The material extracted with β mercaptoethanol or dilute alkali appeared under the electron microscope as large aggregates in the form of spheroidal and mostly web-like structures of large sizes. These, and additional data, suggest that this protein complex may constitute an important part of the basic glycoprotein structure of C. albicans. The possibility that similar complexes exist in the wall of other fungi is an attractive, although yet untested possibility.

Deciphering the MHC-associated peptidome: a review of naturally processed ligand data

INTRODUCTION

The availability of big data sets ('OMICS') has greatly impacted fundamental and translational science. High-throughput analysis of HLA class I and II associated peptidomes by mass spectrometry (MS) has generated large datasets, with the last decade witnessing tremendous growth in the breadth and number of studies. Areas covered: For this, we first analyzed naturally processed peptide (NP) data captured within the IEDB to survey and characterize the current state of NP data. We next asked to what extent the NP data overlap with existing T cell epitope and MHC binding data. Expert commentary: The current collection of NP data represents a large and diverse set of class I/II peptides mostly derived from self-antigens. These data overlap only marginally with existing immunogenicity and binding data and it is thus difficult to ascertain the correspondence between the different assay methodologies. This highlights a need for unbiased studies benchmarking in model antigen systems how well MHC binding and NP data predicts immunogenicity. Going forward, efforts at generating an integrated process for capturing all NP, curating associated metadata and accessing NP data from an immunological viewpoint will be important for development of novel methods for identifying optimal target antigens and for class I and II epitope prediction.

A Multivariate Mixture Model to Estimate the Accuracy of Glycosaminoglycan Identifications Made by Tandem Mass Spectrometry (MS/MS) and Database Search

We present a statistical model to estimate the accuracy of derivatized heparin and heparan sulfate (HS) glycosaminoglycan (GAG) assignments to tandem mass (MS/MS) spectra made by the first published database search application, GAG-ID. Employing a multivariate expectation-maximization algorithm, this statistical model distinguishes correct from ambiguous and incorrect database search results when computing the probability that heparin/HS GAG assignments to spectra are correct based upon database search scores. Using GAG-ID search results for spectra generated from a defined mixture of 21 synthesized tetrasaccharide sequences as well as seven spectra of longer defined oligosaccharides, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly, ambiguously, and incorrectly assigned heparin/HS GAGs. This analysis makes it possible to filter large MS/MS database search results with predictable false identification error rates.

Expanding the Knowledge on Lignocellulolytic and Redox Enzymes of Worker and Soldier Castes from the Lower Termite Coptotermes gestroi

Termites are considered one of the most efficient decomposers of lignocelluloses on Earth due to their ability to produce, along with its microbial symbionts, a repertoire of carbohydrate-active enzymes (CAZymes). Recently, a set of Pro-oxidant, Antioxidant, and Detoxification enzymes (PAD) were also correlated with the metabolism of carbohydrates and lignin in termites. The lower termite Coptotermes gestroi is considered the main urban pest in Brazil, causing damage to wood constructions. Recently, analysis of the enzymatic repertoire of C. gestroi unveiled the presence of different CAZymes. Because the gene profile of CAZy/PAD enzymes endogenously synthesized by C. gestroi and also by their symbiotic protists remains unclear, the aim of this study was to explore the eukaryotic repertoire of these enzymes in worker and soldier castes of C. gestroi. Our findings showed that worker and soldier castes present similar repertoires of CAZy/PAD enzymes, and also confirmed that endo-glucanases (GH9) and beta-glucosidases (GH1) were the most important glycoside hydrolase families related to lignocellulose degradation in both castes. Classical cellulases such as exo-glucanases (GH7) and endo-glucanases (GH5 and GH45), as well as classical xylanases (GH10 and GH11), were found in both castes only taxonomically related to protists, highlighting the importance of symbiosis in C. gestroi. Moreover, our analysis revealed the presence of Auxiliary Activity enzyme families (AAs), which could be related to lignin modifications in termite digestomes. In conclusion, this report expanded the knowledge on genes and proteins related to CAZy/PAD enzymes from worker and soldier castes of lower termites, revealing new potential enzyme candidates for second-generation biofuel processes.

Invited Review: Toxicoproteomics: Proteomics Applied to Toxicology and Pathology

Global measurement of proteins and their many attributes in tissues and biofluids defines the field of proteomics. Toxicoproteomics, as part of the larger field of toxicogenomics, seeks to identify critical proteins and pathways in biological systems that are affected by and respond to adverse chemical and environmental exposures using global protein expression technologies. Toxicoproteomics integrates 3 disciplinary areas: traditional toxicology and pathology, differential protein and gene expression analysis, and systems biology. Key topics to be reviewed are the evolution of proteomics, proteomic technology platforms and their capabilities with exemplary studies from biology and medicine, a review of over 50 recent studies applying proteomic analysis to toxicological research, and the recent development of databases designed to integrate -Omics technologies with toxicology and pathology. Proteomics is examined for its potential in discovery of new biomarkers and toxicity signatures, in mapping serum, plasma, and other biofluid proteomes, and in parallel proteomic and transcriptomic studies. The new field of toxicoproteomics is uniquely positioned toward an expanded understanding of protein expression during toxicity and environmental disease for the advancement of public health.

Autophosphorylation on S614 inhibits the activity and the transforming potential of BRAF

The BRAF proto-oncogene serine/threonine-protein kinase, known as BRAF, belongs to the RAF kinase family. It regulates the MAPK/ERK signalling pathway affecting several cellular processes such as growth, survival, differentiation, and cellular transformation. BRAF is mutated in ~8% of all human cancers with the V600E mutation constituting ~90% of mutations. Here, we have used quantitative mass spectrometry to map and compare phosphorylation site patterns between BRAF and BRAF V600E. We identified sites that are shared as well as several quantitative differences in phosphorylation abundance. The highest difference is phosphorylation of S614 in the activation loop which is ~5fold enhanced in BRAF V600E. Mutation of S614 increases the kinase activity of both BRAF and BRAF V600E and the transforming ability of BRAF V600E. The phosphorylation of S614 is mitogen inducible and the result of autophosphorylation. These data suggest that phosphorylation at this site is inhibitory, and part of the physiological shut-down mechanism of BRAF signalling.

MZDASoft: a software architecture that enables large-scale comparison of protein expression levels over multiple samples based on liquid chromatography/tandem mass spectrometry

RATIONALE

Without accurate peak linking/alignment, only the expression levels of a small percentage of proteins can be compared across multiple samples in Liquid Chromatography/Mass Spectrometry/Tandem Mass Spectrometry (LC/MS/MS) due to the selective nature of tandem MS peptide identification. This greatly hampers biomedical research that aims at finding biomarkers for disease diagnosis, treatment, and the understanding of disease mechanisms. A recent algorithm, PeakLink, has allowed the accurate linking of LC/MS peaks without tandem MS identifications to their corresponding ones with identifications across multiple samples collected from different instruments, tissues and labs, which greatly enhanced the ability of comparing proteins. However, PeakLink cannot be implemented practically for large numbers of samples based on existing software architectures, because it requires access to peak elution profiles from multiple LC/MS/MS samples simultaneously.

METHODS

We propose a new architecture based on parallel processing, which extracts LC/MS peak features, and saves them in database files to enable the implementation of PeakLink for multiple samples. The software has been deployed in High-Performance Computing (HPC) environments. The core part of the software, MZDASoft Parallel Peak Extractor (PPE), can be downloaded with a user and developer's guide, and it can be run on HPC centers directly. The quantification applications, MZDASoft TandemQuant and MZDASoft PeakLink, are written in Matlab, which are compiled with a Matlab runtime compiler. A sample script that incorporates all necessary processing steps of MZDASoft for LC/MS/MS quantification in a parallel processing environment is available. The project webpage is http://compgenomics.utsa.edu/zgroup/MZDASoft.

RESULTS

The proposed architecture enables the implementation of PeakLink for multiple samples. Significantly more (100%-500%) proteins can be compared over multiple samples with better quantification accuracy in test cases.

CONCLUSION

MZDASoft enables large-scale comparison of protein expression levels over multiple samples with much larger protein comparison coverage and better quantification accuracy. It is an efficient implementation based on parallel processing which can be used to process large amounts of data.

Improving protein identification from tandem mass spectrometry data by one-step methods and integrating data from other platforms

MOTIVATION

Many approaches have been proposed for the protein identification problem based on tandem mass spectrometry (MS/MS) data. In these experiments, proteins are digested into peptides and the resulting peptide mixture is subjected to mass spectrometry. Some interesting putative peptide features (peaks) are selected from the mass spectra. Following that, the precursor ions undergo fragmentation and are analyzed by MS/MS. The process of identification of peptides from the mass spectra and the constituent proteins in the sample is called protein identification from MS/MS data. There are many two-step protein identification procedures, reviewed in the literature, which first attempt to identify the peptides in a separate process and then use these results to infer the proteins. However, in recent years, there have been attempts to provide a one-step solution to protein identification, which simultaneously identifies the proteins and the peptides in the sample.

RESULTS

In this review, we briefly introduce the most popular two-step protein identification procedure, PeptideProphet coupled with ProteinProphet. Following that, we describe the difficulties with two-step procedures and review some recently introduced one-step protein/peptide identification procedures that do not suffer from these issues. The focus of this review is on one-step procedures that are based on statistical likelihood-based models, but some discussion of other one-step procedures is also included. We report comparative performances of one-step and two-step methods, which support the overall superiorities of one-step procedures. We also cover some recent efforts to improve protein identification by incorporating other molecular data along with MS/MS data.

GAG-ID: Heparan Sulfate (HS) and Heparin Glycosaminoglycan High-Throughput Identification Software

Heparin and heparan sulfate are very large linear polysaccharides that undergo a complex variety of modifications and are known to play important roles in human development, cell-cell communication and disease. Sequencing of highly sulfated glycosaminoglycan oligosaccharides like heparin and heparan sulfate by liquid chromatography-tandem mass spectrometry (LC-MS/MS) remains challenging because of the presence of multiple isomeric sequences in a complex mixture of oligosaccharides, the difficulties in separation of these isomers, and the facile loss of sulfates in MS/MS. We have previously introduced a method for structural sequencing of heparin/heparan sulfate oligosaccharides involving chemical derivatizations that replace labile sulfates with stable acetyl groups. This chemical derivatization scheme allows the use of reversed phase LC for high-resolution separation and MS/MS for sequencing of isomeric heparan sulfate oligosaccharides. However, because of the large number of analytes present in complex mixtures of heparin/HS oligosaccharides, the resulting LC-MS/MS data sets are large and cannot be annotated with existing glycomics software because of the specifically designed chemical derivatization strategy. We have developed a tool, called GAG-ID, to automate the interpretation of derivatized heparin/heparan sulfate LC-MS/MS data based on a modified multivariate hypergeometric distribution to weight the annotation of more intense peaks. The software is tested on a LC-MS/MS data set collected from a mixture of 21 synthesized heparan sulfate tetrasaccharides. By testing the discrimination of scoring with this system, we show that stratifying peaks into different intensity classes benefits the discrimination of scoring, and GAG-ID is able to properly assign all 21 synthetic tetrasaccharides in a defined mixture from a single LC-MS/MS run.

Overview of software options for processing, analysis and interpretation of mass spectrometric proteomic data

Recently, the interests in proteomics have been intensively increased, and the proteomic methods have been widely applied to many problems in cell biology. If the age of 1990s is considered to be a decade of genomics, we can claim that the following years of the new century is a decade of proteomics. The rapid evolution of proteomics has continued through these years, with a series of innovations in separation techniques and the core technologies of two-dimensional gel electrophoresis and MS. Both technologies are fueled by automation and high throughput computation for profiling of proteins from biological systems. As Patterson ever mentioned, 'data analysis is the Achilles heel of proteomics and our ability to generate data now outstrips our ability to analyze it'. The development of automatic and high throughput technologies for rapid identification of proteins is essential for large-scale proteome projects and automatic protein identification and characterization is essential for high throughput proteomics. This review provides a snap shot of the tools and applications that are available for mass spectrometric high throughput biocomputation. The review starts with a brief introduction of proteomics and MS. Computational tools that can be employed at various stages of analysis are presented, including that for data processing, identification, quantification, and the understanding of the biological functions of individual proteins and their dynamic interactions. The challenges of computation software development and its future trends in MS-based proteomics have also been speculated.

Mass spectrometry-based biomarkers in drug development

Advances in mass spectrometry, proteomics, protein bioanalytical approaches, and biochemistry have led to a rapid evolution and expansion in the area of mass spectrometry-based biomarker discovery and development. The last decade has also seen significant progress in establishing accepted definitions, guidelines, and criteria for the analytical validation, acceptance, and qualification of biomarkers. These advances have coincided with a decreased return on investment for pharmaceutical research and development and an increasing need for better early decision making tools. Empowering development teams with tools to measure a therapeutic interventions impact on disease state and progression, measure target engagement, and to confirm predicted pharmacodynamic effects is critical to efficient data-driven decision making. Appropriate implementation of a biomarker or a combination of biomarkers can enhance understanding of a drugs mechanism, facilitate effective translation from the preclinical to clinical space, enable early proof of concept and dose selection, and increase the efficiency of drug development. Here we will provide descriptions of the different classes of biomarkers that have utility in the drug development process as well as review specific, protein-centric, mass spectrometry-based approaches for the discovery of biomarkers and development of targeted assays to measure these markers in a selective and analytically precise manner.

Open source libraries and frameworks for mass spectrometry based proteomics: a developer's perspective

Data processing, management and visualization are central and critical components of a state of the art high-throughput mass spectrometry (MS)-based proteomics experiment, and are often some of the most time-consuming steps, especially for labs without much bioinformatics support. The growing interest in the field of proteomics has triggered an increase in the development of new software libraries, including freely available and open-source software. From database search analysis to post-processing of the identification results, even though the objectives of these libraries and packages can vary significantly, they usually share a number of features. Common use cases include the handling of protein and peptide sequences, the parsing of results from various proteomics search engines output files, and the visualization of MS-related information (including mass spectra and chromatograms). In this review, we provide an overview of the existing software libraries, open-source frameworks and also, we give information on some of the freely available applications which make use of them. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

PTMTreeSearch: a novel two-stage tree-search algorithm with pruning rules for the identification of post-translational modification of proteins in MS/MS spectra

MOTIVATION

Tandem mass spectrometry has become a standard tool for identifying post-translational modifications (PTMs) of proteins. Algorithmic searches for PTMs from tandem mass spectrum data (MS/MS) tend to be hampered by noisy data as well as by a combinatorial explosion of search space. This leads to high uncertainty and long search-execution times.

RESULTS

To address this issue, we present PTMTreeSearch, a new algorithm that uses a large database of known PTMs to identify PTMs from MS/MS data. For a given peptide sequence, PTMTreeSearch builds a computational tree wherein each path from the root to the leaves is labeled with the amino acids of a peptide sequence. Branches then represent PTMs. Various empirical tree pruning rules have been designed to decrease the search-execution time by eliminating biologically unlikely solutions. PTMTreeSearch first identifies a relatively small set of high confidence PTM types, and in a second stage, performs a more exhaustive search on this restricted set using relaxed search parameter settings. An analysis of experimental data shows that using the same criteria for false discovery, PTMTreeSearch annotates more peptides than the current state-of-the-art methods and PTM identification algorithms, and achieves this at roughly the same execution time. PTMTreeSearch is implemented as a plugable scoring function in the X!Tandem search engine.

AVAILABILITY

The source code of PTMTreeSearch and a demo server application can be found at http://net.icgeb.org/ptmtreesearch

Pinpointing differentially expressed domains in complex protein mixtures with the cloud service of PatternLab for Proteomics

Mass-spectrometry-based shotgun proteomics has become a widespread technology for analyzing complex protein mixtures. Here we describe a new module integrated into PatternLab for Proteomics that allows the pinpointing of differentially expressed domains. This is accomplished by inferring functional domains through our cloud service, using HMMER3 and Pfam remotely, and then mapping the quantitation values into domains for downstream analysis. In all, spotting which functional domains are changing when comparing biological states serves as a complementary approach to facilitate the understanding of a system's biology. We exemplify the new module's use by reanalyzing a previously published MudPIT dataset of Cryptococcus gattii cultivated under iron-depleted and replete conditions. We show how the differential analysis of functional domains can facilitate the interpretation of proteomic data by providing further valuable insight.

Evaluation and optimization of mass spectrometric settings during data-dependent acquisition mode: focus on LTQ-Orbitrap mass analyzers

Mass-spectrometry-based proteomics has evolved as the preferred method for the analysis of complex proteomes. Undoubtedly, recent advances in mass spectrometry instrumentation have greatly enhanced proteomic analysis. A popular instrument platform in proteomics research is the LTQ-Orbitrap mass analyzer. In this tutorial, we discuss the significance of evaluating and optimizing mass spectrometric settings on the LTQ-Orbitrap during CID data-dependent acquisition (DDA) mode to improve protein and peptide identification rates. We focus on those MS and MS/MS parameters that have been systematically examined and evaluated by several researchers and are commonly used during DDA. More specifically, we discuss the effect of mass resolving power, preview mode for FTMS scan, monoisotopic precursor selection, signal threshold for triggering MS/MS events, number of microscans per MS/MS scan, number of MS/MS events, automatic gain control target value (ion population) for MS and MS/MS, maximum ion injection time for MS/MS, rapid and normal scan rate, and prediction of ion injection time. We furthermore present data from the latest generation LTQ-Orbitrap system, the Orbitrap Elite, along with recommended MS and MS/MS parameters. The Orbitrap Elite outperforms the Orbitrap Classic in terms of scan speed, sensitivity, dynamic range, and resolving power and results in higher identification rates. Several of the optimized MS parameters determined on the LTQ-Orbitrap Classic and XL were easily transferable to the Orbitrap Elite, whereas others needed to be reevaluated. Finally, the Q Exactive and HCD are briefly discussed, as well as sample preparation, LC-optimization, and bioinformatics analysis. We hope this tutorial will serve as guidance for researchers new to the field of proteomics and assist in achieving optimal results.

A new approach to evaluating statistical significance of spectral identifications

While nonlinear peptide natural products such as Vancomycin and Daptomycin are among the most effective antibiotics, the computational techniques for sequencing such peptides are still in their infancy. Previous methods for sequencing peptide natural products are based on Nuclear Magnetic Resonance spectroscopy and require large amounts (milligrams) of purified materials. Recently, development of mass spectrometry-based methods has enabled accurate sequencing of nonlinear peptide natural products using picograms of material, but the question of evaluating statistical significance of Peptide Spectrum Matches (PSM) for these peptides remains open. Moreover, it is unclear how to decide whether a given spectrum is produced by a linear, cyclic, or branch-cyclic peptide. Surprisingly, all previous mass spectrometry studies overlooked the fact that a very similar problem has been successfully addressed in particle physics in 1951. In this work, we develop a method for estimating statistical significance of PSMs defined by any peptide (including linear and nonlinear). This method enables us to identify whether a peptide is linear, cyclic, or branch-cyclic, an important step toward identification of peptide natural products.

PHF6 interacts with the nucleosome remodeling and deacetylation (NuRD) complex

Mutations in PHF6 are the cause of Börjeson-Forssman-Lehman syndrome (BFLS), an X-linked intellectual disability (XLID) disorder, and both T-cell acute lymphoblastic leukemia (T-ALL) and acute myeloid leukemia (AML). The PHF6 gene encodes a protein with two plant homeodomain (PHD)-like zinc finger domains. As many PHD-like domains function to target chromatin remodelers to post-translationally modified histones, this suggests a role for PHF6 in chromatin regulation. However, PHD domains are usually found in association with a catalytic domain, a feature that is lacking in PHF6. This distinct domain structure and the minimal information on its cellular function prompted us to perform a proteomic screen to identify PHF6 binding partners. We expressed recombinant Flag-tagged PHF6 in HEK 293T cells for coimmunoprecipitation, and analyzed the purified products by mass spectrometry. We identified proteins involved in ribosome biogenesis, RNA splicing, and chromatin regulation, consistent with PHF6 localization to both the nucleoplasm and nucleolus. Notably, PHF6 copurified with multiple constituents of the nucleosome remodeling and deacetylation (NuRD) complex, including CHD4, HDAC1, and RBBP4. We demonstrate that this PHF6-NuRD complex is not present in the nucleolus but is restricted to the nucleoplasm. The association with NuRD represents the first known interaction for PHF6 and implicates it in chromatin regulation.

The protein expression landscape of the Arabidopsis root

Because proteins are the major functional components of cells, knowledge of their cellular localization is crucial to gaining an understanding of the biology of multicellular organisms. We have generated a protein expression map of the Arabidopsis root providing the identity and cell type-specific localization of nearly 2,000 proteins. Grouping proteins into functional categories revealed unique cellular functions and identified cell type-specific biomarkers. Cellular colocalization provided support for numerous protein-protein interactions. With a binary comparison, we found that RNA and protein expression profiles are weakly correlated. We then performed peak integration at cell type-specific resolution and found an improved correlation with transcriptome data using continuous values. We performed GeLC-MS/MS (in-gel tryptic digestion followed by liquid chromatography-tandem mass spectrometry) proteomic experiments on mutants with ectopic and no root hairs, providing complementary proteomic data. Finally, among our root hair-specific proteins we identified two unique regulators of root hair development.

Proteomic identification of immunoproteasome accumulation in formalin-fixed rodent spinal cords with experimental autoimmune encephalomyelitis

Clinically relevant formalin-fixed and paraffin-embedded (FFPE) tissues have not been widely used in neuroproteomic studies because many proteins are presumed to be degraded during tissue preservation. Recent improvements in proteomics technologies, from the 2D gel analysis of intact proteins to the "shotgun" quantification of peptides and the use of isobaric tags for absolute and relative quantification (iTRAQ) method, have made the analysis of FFPE tissues possible. In recent years, iTRAQ has been one of the main methods of choice for high throughput quantitative proteomics analysis, which enables simultaneous comparison of up to eight samples in one experiment. Our objective was to assess the relative merits of iTRAQ analysis of fresh frozen versus FFPE nervous tissues by comparing experimental autoimmune encephalomyelitis (EAE)-induced proteomic changes in FFPE rat spinal cords and frozen tissues. EAE-induced proteomic changes in FFPE tissues were positively correlated with those found in the frozen tissues, albeit with ∼50% less proteome coverage. Subsequent validation of the enrichment of immunoproteasome (IP) activator 1 in EAE spinal cords led us to evaluate other proteasome and IP-specific proteins. We discovered that many IP-specific (as opposed to constitutive) proteasomal proteins were enriched in EAE rat spinal cords, and EAE-induced IP accumulation also occurred in the spinal cords of an independent mouse EAE model in a disability score-dependent manner. Therefore, we conclude that it is feasible to generate useful information from iTRAQ-based neuroproteomics analysis of archived FFPE tissues for studying neurological disease tissues.

Staged-probability strategy of processing shotgun proteomic data to discover more functionally important proteins

Biologically important proteins related to membrane receptors, signal transduction, regulation, transcription, and translation are usually low in abundance and identified with low probability in mass spectroscopy (MS)-based analyses. Most valuable proteomics information on them were hitherto discarded due to the application of excessively strict data filtering for accurate identification. In this study, we present a staged-probability strategy for assessing proteomic data for potential functionally important protein clues. MS-based protein identifications from the second (L2) and third (L3) layers of the cascade affinity fractionation using the Trans-Proteomic Pipeline software were classified into three probability stages as 1.00-0.95, 0.95-0.50, and 0.50-0.20 according to their distinctive identification correctness rates (i.e. 100%-95%, 95%-50%, and 50%-20%, respectively). We found large data volumes and more functionally important proteins located at the previously unacceptable lower probability stages of 0.95-0.50 and 0.50-0.20 with acceptable correctness rate. More importantly, low probability proteins in L2 were verified to exist in L3. Together with some MS spectrogram examples, comparisons of protein identifications of L2 and L3 demonstrated that the staged-probability strategy could more adequately present both quantity and quality of proteomic information, especially for researches involving biomarker discovery and novel therapeutic target screening.

New inflammation-related biomarkers during malaria infection

Malaria is one of the most prevalent infectious diseases worldwide with more than 250 million cases and one million deaths each year. One of the well-characterized malarial-related molecules is hemozoin (HZ), which is a dark-brown crystal formed by the parasite and released into the host during the burst of infected red blood cells. HZ has a stimulatory effect on the host immune system such as its ability to induce pro-inflammatory mediators responsible for some of the malaria related clinical symptoms such as fever. However, the host serum proteins interacting with malarial HZ as well as how this interaction modifies its recognition by phagocytes remained elusive. In the actual study, using proteomic liquid chromatographic mass spectrometry (LC-MS/MS) and immunochemical approaches, we compared the serum protein profiles of malaria patients and healthy individuals. Particularly, we utilized the malarial HZ itself to capture serum proteins capable to bind to HZ, enabling us to identify several proteins such as apolipoprotein E (ApoE), serum amyloid A (SAA), gelsolin, complement factor H and fibrinogen that were found to differ among healthy and malaria individual. Of particular interest is LPS binding protein (LBP), which is reported herein for the first time in the context of malaria. LBP is usually produced during innate inflammatory response to gram-negative bacterial infections. The exact role of these biomarkers and acute phase responses in malaria in general and HZ in particular remains to be investigated. The identification of these inflammation-related biomarkers in malaria paves the way to potentially utilize them as diagnostic and therapeutic targets.

Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

Recent advances in computational analysis of mass spectrometry for proteomic profiling

The proteome, defined as an organism's proteins and their actions, is a highly complex end-effector of molecular and cellular events. Differing amounts of proteins in a sample can be indicators of an individual's health status; thus, it is valuable to identify key proteins that serve as 'biomarkers' for diseases. Since the proteome cannot be simply inferred from the genome due to pre- and posttranslational modifications, a direct approach toward mapping the proteome must be taken. The difficulty in evaluating a large number of individual proteins has been eased with the development of high-throughput methods based on mass spectrometry (MS) of peptide or protein mixtures, bypassing the time-consuming, laborious process of protein purification. However, proteomic profiling by MS requires extensive computational analysis. This article describes key issues and recent advances in computational analysis of mass spectra for biomarker identification.

Quantitative proteomic analysis of dystrophic dog muscle

Duchenne muscular dystrophy (DMD) is caused by null mutations in the dystrophin gene, leading to progressive and unrelenting muscle loss. Although the genetic basis of DMD is well resolved, the cellular mechanisms associated with the physiopathology remain largely unknown. Increasing evidence suggests that secondary mechanisms, as the alteration of key signaling pathways, may play an important role. In order to identify reliable biomarkers and potential therapeutic targets, and taking advantage of the clinically relevant Golden Retriever Muscular Dystrophy (GRMD) dog model, a proteomic study was performed. Isotope-coded affinity tag (ICAT) profiling was used to compile quantitative changes in protein expression profiles of the vastus lateralis muscles of 4-month old GRMD vs healthy dogs. Interestingly, the set of under-expressed proteins detected appeared primarily composed of metabolic proteins, many of which have been shown to be regulated by the transcriptional peroxisome proliferator-activated receptor-gamma co-activator 1 alpha (PGC-1α). Subsequently, we were able to showed that PGC1-α expression is dramatically reduced in GRMD compared to healthy muscle. Collectively, these results provide novel insights into the molecular pathology of the clinically relevant animal model of DMD, and indicate that defective energy metabolism is a central hallmark of the disease in the canine model.

LRRK2 controls synaptic vesicle storage and mobilization within the recycling pool

Mutations in leucine-rich repeat kinase 2 (LRRK2) are the single most common cause of inherited Parkinson's disease. Little is known about its involvement in the pathogenesis of Parkinson's disease mainly because of the lack of knowledge about the physiological role of LRRK2. To determine the function of LRRK2, we studied the impact of short hairpin RNA-mediated silencing of LRRK2 expression in cortical neurons. Paired recording indicated that LRRK2 silencing affects evoked postsynaptic currents. Furthermore, LRRK2 silencing induces at the presynaptic site a redistribution of vesicles within the bouton, altered recycling dynamics, and increased vesicle kinetics. Accordingly, LRRK2 protein is present in the synaptosomal compartment of cortical neurons in which it interacts with several proteins involved in vesicular recycling. Our results suggest that LRRK2 modulates synaptic vesicle trafficking and distribution in neurons and in consequence participates in regulating the dynamics between vesicle pools inside the presynaptic bouton.

Ethanol-induced changes in the expression of proteins related to neurotransmission and metabolism in different regions of the rat brain

Despite extensive description of the damaging effects of chronic alcohol exposure on brain structure, mechanistic explanations for the observed changes are just emerging. To investigate regional brain changes in protein expression levels following chronic ethanol treatment, one rat per sibling pair of male Wistar rats was exposed to intermittent (14 h/day) vaporized ethanol, the other to air for 26 weeks. At the end of 24 weeks of vapor exposure, the ethanol group had blood ethanol levels averaging 450 mg%, had not experienced a protracted (> 16 h) withdrawal from ethanol, and revealed only mild evidence of hepatic steatosis. Extracted brains were micro-dissected to isolate the prefrontal cortex (PFC), dorsal striatum (STR), corpus callosum genu (CCg), CC body (CCb), anterior vermis (AV), and anterior dorsal lateral cerebellum (ADLC) for protein analysis with two-dimensional gel electrophoresis. Expression levels for 54 protein spots were significantly different between the ethanol- and air-treated groups. Of these 54 proteins, tandem mass spectroscopy successfully identified 39 unique proteins, the levels of which were modified by ethanol treatment: 13 in the PFC, 7 in the STR, 2 in the CCg, 7 in the CCb, 7 in the AV, and 5 in the ADLC. The functions of the proteins altered by chronic ethanol exposure were predominantly associated with neurotransmitter systems in the PFC and cell metabolism in the STR. Stress response proteins were elevated only in the PFC, AV, and ADLC perhaps supporting a role for frontocerebellar circuitry disruption in alcoholism. Of the remaining proteins, some had functions associated with cytoskeletal physiology (e.g., in the CCb) and others with transcription/translation (e.g., in the ADLC). Considered collectively, all but 4 of the 39 proteins identified in the present study have been previously identified in ethanol gene- and/or protein-expression studies lending support for their role in ethanol-related brain alterations.

An improved machine learning protocol for the identification of correct Sequest search results

Background

Mass spectrometry has become a standard method by which the proteomic profile of cell or tissue samples is characterized. To fully take advantage of tandem mass spectrometry (MS/MS) techniques in large scale protein characterization studies robust and consistent data analysis procedures are crucial. In this work we present a machine learning based protocol for the identification of correct peptide-spectrum matches from Sequest database search results, improving on previously published protocols.

Results

The developed model improves on published machine learning classification procedures by 6% as measured by the area under the ROC curve. Further, we show how the developed model can be presented as an interpretable tree of additive rules, thereby effectively removing the 'black-box' notion often associated with machine learning classifiers, allowing for comparison with expert rule-of-thumb. Finally, a method for extending the developed peptide identification protocol to give probabilistic estimates of the presence of a given protein is proposed and tested.

Conclusions

We demonstrate the construction of a high accuracy classification model for Sequest search results from MS/MS spectra obtained by using the MALDI ionization. The developed model performs well in identifying correct peptide-spectrum matches and is easily extendable to the protein identification problem. The relative ease with which additional experimental parameters can be incorporated into the classification framework, to give additional discriminatory power, allows for future tailoring of the model to take advantage of information from specific instrument set-ups.

Liquid Chromatography Mass Spectrometry-Based Proteomics: Biological and Technological Aspects

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.

Interaction proteomics: characterization of protein complexes using tandem affinity purification-mass spectrometry

Most cellular processes are carried out by a multitude of proteins that assemble into multimeric complexes. Thus a precise understanding of the biological pathways that control cellular events relies on the identification and on the biochemical characterization of the proteins involved in such multimeric assemblies. Advances in MS have made possible the identification of multisubunit protein complexes isolated from cell lysates with high sensitivity and accuracy, whereas the TAP (tandem affinity purification) methodology efficiently isolates native protein complexes from cells for proteomics analysis. TAP is a generic method based on the sequential utilization of two affinity tags to purify protein assemblies. During the first purification step, the Protein A moiety of the TAP tag is bound to IgG beads, and protein components associated with the TAP-tagged protein are retrieved by TEV (tobacco etch virus) protease cleavage. This enzyme is a sequence-specific protease cleaving a seven-amino-acid recognition site located between the first and second tags. In the second affinity step, the protein complex is immobilized to calmodulin-coated beads via the CBP (calmodulin-binding peptide) of the TAP tag. The CBP-calmodulin interaction is calcium-dependent and calcium-chelating agents are used in the second elution step to release the final protein complex preparation used for protein identification by MS. The TAP-MS approach has proven to efficiently permit the characterization of protein complexes from bacteria, yeast and mammalian cells, as well as from multicellular organisms such as Caenorhabditis elegans, Drosophila and mice.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

Mass spectrometry based glycoproteomics--from a proteomics perspective

Glycosylation is one of the most important and common forms of protein post-translational modification that is involved in many physiological functions and biological pathways. Altered glycosylation has been associated with a variety of diseases, including cancer, inflammatory and degenerative diseases. Glycoproteins are becoming important targets for the development of biomarkers for disease diagnosis, prognosis, and therapeutic response to drugs. The emerging technology of glycoproteomics, which focuses on glycoproteome analysis, is increasingly becoming an important tool for biomarker discovery. An in-depth, comprehensive identification of aberrant glycoproteins, and further, quantitative detection of specific glycosylation abnormalities in a complex environment require a concerted approach drawing from a variety of techniques. This report provides an overview of the recent advances in mass spectrometry based glycoproteomic methods and technology, in the context of biomarker discovery and clinical application.

Protein and gene model inference based on statistical modeling in k-partite graphs

One of the major goals of proteomics is the comprehensive and accurate description of a proteome. Shotgun proteomics, the method of choice for the analysis of complex protein mixtures, requires that experimentally observed peptides are mapped back to the proteins they were derived from. This process is also known as protein inference. We present Markovian Inference of Proteins and Gene Models (MIPGEM), a statistical model based on clearly stated assumptions to address the problem of protein and gene model inference for shotgun proteomics data. In particular, we are dealing with dependencies among peptides and proteins using a Markovian assumption on k-partite graphs. We are also addressing the problems of shared peptides and ambiguous proteins by scoring the encoding gene models. Empirical results on two control datasets with synthetic mixtures of proteins and on complex protein samples of Saccharomyces cerevisiae, Drosophila melanogaster, and Arabidopsis thaliana suggest that the results with MIPGEM are competitive with existing tools for protein inference.

A guided tour of the Trans-Proteomic Pipeline

The Trans-Proteomic Pipeline (TPP) is a suite of software tools for the analysis of MS/MS data sets. 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 data set, demonstrating that the setup and use of the tools are straightforward and well supported and do not require specialized informatic resources or knowledge.