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The Consortium for Top-Down Proteomics (www.topdownproteomics.org) launched the present study to assess the current state of top-down mass spectrometry (TD MS) and middle-down mass spectrometry (MD MS) for characterizing monoclonal antibody (mAb) primary structures, including their modifications. To meet the needs of the rapidly growing therapeutic antibody market, it is important to develop analytical strategies to characterize the heterogeneity of a therapeutic product's primary structure accurately and reproducibly. The major objective of the present study is to determine whether current TD/MD MS technologies and protocols can add value to the more commonly employed bottom-up (BU) approaches with regard to confirming protein integrity, sequencing variable domains, avoiding artifacts, and revealing modifications and their locations. We also aim to gather information on the common TD/MD MS methods and practices in the field. A panel of three mAbs was selected and centrally provided to 20 laboratories worldwide for the analysis: Sigma mAb standard (SiLuLite), NIST mAb standard, and the therapeutic mAb Herceptin (trastuzumab). Various MS instrument platforms and ion dissociation techniques were employed. The present study confirms that TD/MD MS tools are available in laboratories worldwide and provide complementary information to the BU approach that can be crucial for comprehensive mAb characterization. The current limitations, as well as possible solutions to overcome them, are also outlined. A primary limitation revealed by the results of the present study is that the expert knowledge in both experiment and data analysis is indispensable to practice TD/MD MS.

Progress in proteomics research has led to a demand for powerful analytical tools with high separation efficiency and sensitivity for confident identification and quantification of proteins, posttranslational modifications, and protein complexes expressed in cells and tissues. This demand has significantly increased interest in capillary electrophoresis-mass spectrometry (CE-MS) in the past few years. This review provides highlights of recent advances in CE-MS for proteomics research, including a short introduction to top-down mass spectrometry and native mass spectrometry (native MS), as well as a detailed overview of CE methods. Both the potential and limitations of these methods for the analysis of proteins and peptides in synthetic and biological samples and the challenges of CE methods are discussed, along with perspectives about the future direction of CE-MS. @ 2019 Wiley Periodicals, Inc. Mass Spec Rev 00:1-16, 2019., (© 2019 Wiley Periodicals, Inc.)

Mass-spectrometry-based proteomics has evolved into a high-throughput technology in which numerous large-scale data sets are generated from diverse analytical platforms. Furthermore, several scientific journals and funding agencies have emphasized the storage of proteomics data in public repositories to facilitate its evaluation, inspection, and reanalysis. (1) As a consequence, public proteomics data repositories are growing rapidly. However, tools are needed to integrate multiple proteomics data sets to compare different experimental features or to perform quality control analysis. Here, we present a new Java stand-alone tool, Proteomics Assay COMparator (PACOM), that is able to import, combine, and simultaneously compare numerous proteomics experiments to check the integrity of the proteomic data as well as verify data quality. With PACOM, the user can detect source of errors that may have been introduced in any step of a proteomics workflow and that influence the final results. Data sets can be easily compared and integrated, and data quality and reproducibility can be visually assessed through a rich set of graphical representations of proteomics data features as well as a wide variety of data filters. Its flexibility and easy-to-use interface make PACOM a unique tool for daily use in a proteomics laboratory. PACOM is available at https://github.com/smdb21/pacom .

The analysis of protein half-life and degradation dynamics has proven critically important to our understanding of a broad and diverse set of biological conditions ranging from cancer to neurodegeneration. Historically these protein turnover measures have been performed in cells by monitoring protein levels after "pulse" labeling of newly synthesized proteins and subsequent chase periods. Comparing the level of labeled protein remaining as a function of time to the initial level reveals the protein's half-life. In this method we provide a detailed description of the workflow required for the determination of protein turnover rates on a whole proteome scale in vivo. Our approach starts with the metabolic labeling of whole rodents by restricting all the nitrogen in their diet to exclusively nitrogen-15 in the form of spirulina algae. After near complete organismal labeling with nitrogen-15, the rodents are then switched to a normal nitrogen-14 rich diet for time periods of days to years. Tissues are harvested, the extracts are fractionated, and the proteins are digested to peptides. Peptides are separated by multidimensional liquid chromatography and analyzed by high resolution orbitrap mass spectrometry (MS). The nitrogen-15 containing proteins are then identified and measured by the bioinformatic proteome analysis tools Sequest, DTASelect2, and Census. In this way, our metabolic pulse-chase approach reveals in vivo protein decay rates proteome-wide.

In the last two decades, computational tools for mass spectrometry-based proteomics data analysis have evolved from a few stand-alone software solutions serving specific goals, such as the identification of amino acid sequences based on mass spectrometry spectra, to large-scale complex pipelines integrating multiple computer programs to solve a collection of problems. This software evolution has been mostly driven by the appearance of novel technologies that allowed the community to tackle complex biological problems, such as the identification of proteins that are differentially expressed in two samples under different conditions. The achievement of such objectives requires a large suite of programs to analyze the intricate mass spectrometry data. Our laboratory addresses complex proteomics questions by producing and using algorithms and software packages. Our current computational pipeline includes, among other things, tools for mass spectrometry raw data processing, peptide and protein identification and quantification, post-translational modification analysis, and protein functional enrichment analysis. In this paper, we describe a suite of software packages we have developed to process mass spectrometry-based proteomics data and we highlight some of the new features of previously published programs as well as tools currently under development. Graphical Abstract ᅟ.

During the early studies of N-terminal arginylation, Edman degradation was widely used to identify N-terminally added Arg on protein substrates. This old method is reliable but highly depends on the purity and abundance of samples and can become misleading unless a highly purified, highly arginylated protein can be obtained. Here we report a mass spectrometry-based method that utilizes Edman degradation chemistry to identify arginylation in more complex and less abundant protein samples. This method can also apply to the analysis of other posttranslational modifications.

Here we describe the method for identification of arginylated proteins by mass spectrometry. This method has been originally applied to the identification of N-terminally added Arg on proteins and peptides, and then expanded to identification of side chain arginylation which has been recently described by our groups. The key steps in this method include the use of the mass spectrometry instruments that can identify peptides with very high pass accuracy (Orbitrap) and apply stringent mass cutoffs during automated data analysis, followed by manual validation of the identified spectra. These methods can be used with both complex and purified protein samples and, to date, constitute the only reliable way to confirm arginylation at a particular site on a protein or peptide.

Mass spectrometry plays a key role in relative quantitative comparisons of proteins in order to understand their functional role in biological systems upon perturbation. In this review, we review studies that examine different aspects of isobaric labeling-based relative quantification for shotgun proteomic analysis. In particular, we focus on different types of isobaric reagents and their reaction chemistry (e.g., amine-, carbonyl-, and sulfhydryl-reactive). Various factors, such as ratio compression, reporter ion dynamic range, and others, cause an underestimation of changes in relative abundance of proteins across samples, undermining the ability of the isobaric labeling approach to be truly quantitative. These factors that affect quantification and the suggested combinations of experimental design and optimal data acquisition methods to increase the precision and accuracy of the measurements will be discussed. Finally, the extended application of isobaric labeling-based approach in hyperplexing strategy, targeted quantification, and phosphopeptide analysis are also examined.

Motivation: We introduce Census 2, an update of a mass spectrometry data analysis tool for peptide/protein quantification. New features for analysis of isobaric labeling, such as Tandem Mass Tag (TMT) or Isobaric Tags for Relative and Absolute Quantification (iTRAQ), have been added in this version, including a reporter ion impurity correction, a reporter ion intensity threshold filter and an option for weighted normalization to correct mixing errors. TMT/iTRAQ analysis can be performed on experiments using HCD (High Energy Collision Dissociation) only, CID (Collision Induced Dissociation)/HCD (High Energy Collision Dissociation) dual scans or HCD triple-stage mass spectrometry data. To improve measurement accuracy, we implemented weighted normalization, multiple tandem spectral approach, impurity correction and dynamic intensity threshold features., Availability and Implementation: Census 2 supports multiple input file formats including MS1/MS2, DTASelect, mzXML and pepXML. It requires JAVA version 6 or later to run. Free download of Census 2 for academic users is available at http://fields.scripps.edu/census/index.php., Contact: jyates@scripps.edu, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Two major challenges in proteomics are the large number of proteins and their broad dynamic range in the cell. We exploited the abundance-dependent Michaelis-Menten kinetics of trypsin digestion to selectively digest and deplete abundant proteins with a method we call DigDeAPr. We validated the depletion mechanism with known yeast protein abundances, and we observed greater than threefold improvement in low-abundance human-protein identification and quantitation metrics. This methodology should be broadly applicable to many organisms, proteases and proteomic pipelines.

PatternLab for proteomics is a self-contained computational environment for analyzing shotgun proteomic data. Recent improvements incorporate modules to facilitate the computational analysis, such as FastaDBXtractor for sequence database preparation and ProLuCID runner for simplifying and managing the protein identification search engine; modules for pushing the limits on proteomics standards, such as SEPro, which relies on a semi-labeled decoy approach for increasing confidence in filtering and organizing peptide spectrum matches; and modules with novel features, such as SEProQ for enabling label-free quantitation by extracted ion chromatograms according to a distributed normalized ion abundance factor approach (dNIAF). Existing modules were also improved, such as the TFold module for pinpointing differentially expressed proteins. These new modules are integrated into the previously described arsenal of tools for further data analysis. Here we provide detailed instructions for operating and understanding them., (© 2012 by John Wiley & Sons, Inc.)

Cellular membranes are composed of proteins and glyco- and phospholipids and play an indispensible role in maintaining cellular integrity and homeostasis, by physically restricting biochemical processes within cells and providing protection. Membrane proteins perform many essential functions, which include operating as transporters, adhesion-anchors, receptors, and enzymes. Recent advancements in proteomic mass spectrometry have resulted in substantial progress towards the determination of the plasma membrane (PM) proteome, resolution of membrane protein topology, establishment of numerous receptor protein complexes, identification of ligand-receptor pairs, and the elucidation of signaling networks originating at the PM. Here, we discuss the recent accelerated success of discovery-based proteomic pipelines for the establishment of a complete membrane proteome., (2011 Elsevier Ltd. All rights reserved.)

Mass spectrometry-based proteomics is rapidly becoming an essential tool for biologists. One of the most common applications is identifying the components of protein complexes isolated by co-immunoprecipitation. In this review, we discuss the co-immunoprecipitation, mass spectrometry and data analysis techniques that have been used successfully to define protein complexes in C. elegans research. In this discussion, two strategies emerged. One approach is to use stringent biochemical purification methods and attempt to identify a small number of complex components with a high degree of certainty based on MS data. A second approach is to use less stringent purification and identification parameters, and ultimately test a longer list of potential binding partners in biological validation assays. This should provide a useful guide for biologists planning proteomic experiments., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Mass spectrometry has evolved and matured to a level where it is able to assess the complexity of the human proteome. We discuss some of the expected challenges ahead and promising strategies for success.

The activator of Hsp90 ATPase 1, Aha1, has been shown to participate in the Hsp90 chaperone cycle by stimulating the low intrinsic ATPase activity of Hsp90. To elucidate the structural basis for ATPase stimulation of human Hsp90 by human Aha1, we have developed novel mass spectrometry approaches that demonstrate that the N- and C-terminal domains of Aha1 cooperatively bind across the dimer interface of Hsp90 to modulate the ATP hydrolysis cycle and client activity in vivo. Mutations in both the N- and C-terminal domains of Aha1 impair its ability to bind Hsp90 and stimulate its ATPase activity in vitro and impair in vivo the ability of the Hsp90 system to modulate the folding and trafficking of wild-type and variant (DeltaF508) cystic fibrosis transmembrane conductance regulator (CFTR) responsible for the inherited disease cystic fibrosis (CF). We now propose a general model for the role of Aha1 in the Hsp90 ATPase cycle in proteostasis whereby Aha1 regulates the dwell time of Hsp90 with client. We suggest that Aha1 activity integrates chaperone function with client folding energetics by modulating ATPase sensitive N-terminal dimer structural transitions, thereby protecting transient folding intermediates in vivo that could contribute to protein misfolding systems disorders such as CF when destabilized.

Unlabelled: YADA can deisotope and decharge high-resolution mass spectra from large peptide molecules, link the precursor monoisotopic peak information to the corresponding tandem mass spectrum, and account for different co-fragmenting ion species (multiplexed spectra). We describe how YADA enables a pipeline consisting of ProLuCID and DTASelect for analyzing large-scale middle-down proteomics data., Availability: http://fields.scripps.edu/yada

Electron transfer dissociation (ETD) can dissociate highly charged ions. Efficient analysis of ions dissociated with ETD requires accurate determination of charge states for calculation of molecular weight. We created an algorithm to assign the charge state of ions often used for ETD. The program, Charge Prediction Machine (CPM), uses Bayesian decision theory to account for different charge reduction processes encountered in ETD and can also handle multiplex spectra. CPM correctly assigned charge states to 98% of the 13,097 MS2 spectra from a combined data set of four experiments. In a comparison between CPM and a competing program, Charger (ThermoFisher), CPM produced half the mistakes.

This protocol outlines the essential steps of mass spectrometry-based analysis of protein samples that can be used to identify post-translational arginylation. We describe special considerations for sample preparation and digestion, mass spectrometry analysis using high-precision instruments, database searching for the addition of N-terminal arginine, and multiple steps of automated and manual data validation. Data validation is especially important and involves automated data filtering, manual elimination of mass ambiguities, isotopic peak checking and analysis of the ion fragmentation patterns of the putative arginylated peptides. This protocol is an unambiguous method for the identification of post-translationally arginylated proteins, and it can be used to identify new arginylated proteins. This method provides a definitive answer on the identity of arginylated proteins and peptides in simple and complex protein preparations. It is recommended for studies of arginylation and can be applied to other poorly understood post-translational modifications.

Mass spectrometry (MS) is the most comprehensive and versatile tool in large-scale proteomics. In this review, we dissect the overall framework of the MS experiment into its key components. We discuss the fundamentals of proteomic analyses as well as recent developments in the areas of separation methods, instrumentation, and overall experimental design. We highlight both the inherent strengths and limitations of protein MS and offer a rough guide for selecting an experimental design based on the goals of the analysis. We emphasize the versatility of the Orbitrap, a novel mass analyzer that features high resolution (up to 150,000), high mass accuracy (2-5 ppm), a mass-to-charge range of 6000, and a dynamic range greater than 10(3). High mass accuracy of the Orbitrap expands the arsenal of the data acquisition and analysis approaches compared with a low-resolution instrument. We discuss various chromatographic techniques, including multidimensional separation and ultra-performance liquid chromatography. Multidimensional protein identification technology (MudPIT) involves a continuum sample preparation, orthogonal separations, and MS and software solutions. We discuss several aspects of MudPIT applications to quantitative phosphoproteomics. MudPIT application to large-scale analysis of phosphoproteins includes (a) a fractionation procedure for motif-specific enrichment of phosphopeptides, (b) development of informatics tools for interrogation and validation of shotgun phosphopeptide data, and (c) in-depth data analysis for simultaneous determination of protein expression and phosphorylation levels, analog to western blot measurements. We illustrate MudPIT application to quantitative phosphoproteomics of the beta adrenergic pathway. We discuss several biological discoveries made via mass spectrometry pipelines with a focus on cell signaling proteomics.

Methods for the global analysis of protein expression offer an approach to study the molecular basis of disease. Studies of protein expression in tissue, such as brain, are complicated by the need for efficient and unbiased digestion of proteins that permit identification of peptides by shotgun proteomic methods. In particular, identification and characterization of less abundant membrane proteins has been of great interest for studies of brain physiology, but often proteins of interest are of low abundance or exist in multiple isoforms. Parsing protein isoforms as a function of disease will be essential. In this study, we develop a digestion scheme using detergents compatible with mass spectrometry that improves membrane protein identification from brain tissue. We show the modified procedure yields close to 5,000 protein identifications from 1.8 mg of rat brain homogenate with an average of 25% protein sequence coverage. This procedure achieves a remarkable reduction in the amount of starting material required to observe a broad spectrum of membrane proteins. Among the proteins identified from a mammalian brain homogenate, 1897 (35%) proteins are annotated by Gene Ontology as membrane proteins, and 1225 (22.6%) proteins are predicted to contain at least one transmembrane domain. Membrane proteins identified included neurotransmitter receptors and ion channels implicated in important physiological functions and disease.

Mass spectrometry has been widely used to analyze biological samples and has evolved into an indispensable tool for proteomics research. Our desire to understand the proteome has led to new technologies that push the boundary of mass spectrometry capabilities, which in return has allowed mass spectrometry to address an ever-increasing array of biological questions. The recent development of a novel mass spectrometer (Orbitrap) and new dissociation methods such as electron-transfer dissociation has made possible the exciting new areas of proteomic application. Although bottom-up proteomics (analysis of proteolytic peptide mixtures) remains the workhorse for proteomic analysis, middle-down and top-down strategies (analysis of longer peptides and intact proteins, respectively) should allow more complete characterization of protein isoforms and post-translational modifications. Finally, stable isotope labeling strategies have transformed mass spectrometry from merely descriptive to a tool for measuring dynamic changes in protein expression, interaction, and modification.

The goal of nutritional science is to determine the effect of dietary components. Proteomics has emerged as a method to study proteins on a large scale. The proteomic information gathered from a sample, whether a biological fluid, cell, or tissue, includes not only the identities of proteins present but also their quantities and posttranslational modifications. Recent advances in mass spectrometry make it possible to accurately measure concentrations of thousands of proteins derived from such complex biological samples. Quantitative data will provide increased understanding of the dynamic changes in the proteome induced by dietary components.

We describe Census, a quantitative software tool compatible with many labeling strategies as well as with label-free analyses, single-stage mass spectrometry (MS1) and tandem mass spectrometry (MS/MS) scans, and high- and low-resolution mass spectrometry data. Census uses robust algorithms to address poor-quality measurements and improve quantitative efficiency, and it can support several input file formats. We tested Census with stable-isotope labeling analyses as well as label-free analyses.

In the past decade, there have been remarkable advances in proteomic technologies. Mass spectrometry has emerged as the preferred method for in-depth characterization of the protein components of biological systems. Using mass spectrometry, key insights into the composition, regulation and function of molecular complexes and pathways have been gained. From these studies, it is clear that mass-spectrometry-based proteomics is now a powerful 'hypothesis-generating engine' that, when combined with complementary molecular, cellular and pharmacological techniques, provides a framework for translating large data sets into an understanding of complex biological processes.

An optimization and comparison of trypsin digestion strategies for peptide/protein identifications by microLC-MS/MS with or without MS compatible detergents in mixed organic-aqueous and aqueous systems was carried out in this study. We determine that adding MS-compatible detergents to proteolytic digestion protocols dramatically increases peptide and protein identifications in complex protein mixtures by shotgun proteomics. Protein solubilization and proteolytic efficiency are increased by including MS-compatible detergents in trypsin digestion buffers. A modified trypsin digestion protocol incorporating the MS compatible detergents consistently identifies over 300 proteins from 5 microg of pancreatic cell lysates and generates a greater number of peptide identifications than trypsin digestion with urea when using LC-MS/MS. Furthermore, over 700 proteins were identified by merging protein identifications from trypsin digestion with three different MS-compatible detergents. We also observe that the use of mixed aqueous and organic solvent systems can influence protein identifications in combinations with different MS-compatible detergents. Peptide mixtures generated from different MS-compatible detergents and buffer combinations show a significant difference in hydrophobicity. Our results show that protein digestion schemes incorporating MS-compatible detergents generate quantitative as well as qualitative changes in observed peptide identifications, which lead to increased protein identifications overall and potentially increased identification of low-abundance proteins.

The quantitative analysis of complex biological samples has emerged as a key research area in the field of proteomics. Although quantitative proteomic experiments remain challenging, these strategies have been greatly facilitated by the development of newer high-performance mass spectrometers. In this work, we have evaluated the use of the LTQ-Orbitrap, a hybrid mass spectrometer in which a linear ion trap is coupled to an Orbitrap mass analyzer, for quantitative analyses. By analyzing a range of yeast protein standards, we found that the high mass accuracy, high resolution, large ion capacity, and large dynamic range of the LTQ-Orbitrap led to as much as a 4-5-fold improvement in the number and quality of the peptide ratio measurements compared to similar analyses done on the LTQ. We also successfully quantified protein expression differences that occur in metabolically labeled rat synapses during brain development to further demonstrate the suitability of the LTQ-Orbitrap for the comparative analysis of complex tissue samples.

DTASelect provides a means by which complex SEQUEST results can be filtered, organized, and viewed. A single sample may produce tens of thousands of tandem mass spectra. Manually perusing and selecting SEQUEST matches among such a mass of data carries a risk of inconsistency. DTASelect allows the user to set complex criteria for acceptance or rejection of individual spectrum results. It also features rules for dealing with multiple, identical peptide matches and for removing proteins that are insufficiently evidenced. It provides its sorted and filtered summary as HTML and text documents for easy review and also offers several auxiliary reports. DTASelect is a powerful tool for automatic analysis of complex mixture tandem mass spectrometry.

Multidimensional liquid chromatography techniques have been coupled to tandem mass spectrometry to provide a robust method to identify proteins in complex mixtures. Data acquisition is interfaced directly with search algorithms for identification through cross-correlation with databases. This review describes the most recent advances in methodologies for protein identification by mass spectrometry and describes the limitations of the application of the technologies.

Database searching is an essential element of large-scale proteomics. Because these methods are widely used, it is important to understand the rationale of the algorithms. Most algorithms are based on concepts first developed in SEQUEST and PeptideSearch. Four basic approaches are used to determine a match between a spectrum and sequence: descriptive, interpretative, stochastic and probability-based matching. We review the basic concepts used by most search algorithms, the computational modeling of peptide identification and current challenges and limitations of this approach for protein identification.

The global identification of post-translationally modified proteins is a difficult challenge that is currently being addressed by many researchers in the field of mass spectrometry (MS)-based proteomics. The ability to identify thousands of proteins by shotgun-based strategies has made the mere idea of a global analysis of a particular protein modification seem reasonable. There has been much progress in the development of methods that make use of shotgun-based protein identification in the analysis of a wide variety of protein modifications, some of which will be discussed here.

Motivation: A powerful proteomics methodology couples high-performance liquid chromatography (HPLC) with tandem mass spectrometry and database-search software, such as SEQUEST. Such a set-up, however, produces a large number of spectra, many of which are of too poor quality to be useful. Hence a filter that eliminates poor spectra before the database search can significantly improve throughput and robustness. Moreover, spectra judged to be of high quality, but that cannot be identified by database search, are prime candidates for still more computationally intensive methods, such as de novo sequencing or wider database searches including post-translational modifications., Results: We report on two different approaches to assessing spectral quality prior to identification: binary classification, which predicts whether or not SEQUEST will be able to make an identification, and statistical regression, which predicts a more universal quality metric involving the number of b- and y-ion peaks. The best of our binary classifiers can eliminate over 75% of the unidentifiable spectra while losing only 10% of the identifiable spectra. Statistical regression can pick out spectra of modified peptides that can be identified by a de novo program but not by SEQUEST. In a section of independent interest, we discuss intensity normalization of mass spectra.

Endothelial cells can function differently in vitro and in vivo; however, the degree of microenvironmental modulation in vivo remains unknown at the molecular level largely because of analytical limitations. We use multidimensional protein identification technology (MudPIT) to identify 450 proteins (with three or more spectra) in luminal endothelial cell plasma membranes isolated from rat lungs and from cultured rat lung microvascular endothelial cells. Forty-one percent of proteins expressed in vivo are not detected in vitro. Statistical analysis measuring reproducibility reveals that seven to ten MudPIT measurements are necessary to achieve > or =95% confidence of analytical completeness with current ion trap equipment. Large-scale mapping of the proteome of vascular endothelial cell surface in vivo, as demonstrated here, is advisable because distinct protein expression is apparently regulated by the tissue microenvironment that cannot yet be duplicated in standard cell culture.

Peptide identification based on tandem mass spectrometry and database searching algorithms has become one of the central technologies in proteomics. At the heart of this technology is the ability to reproducibly acquire high-quality tandem mass spectra for database interrogation. The variability in tandem mass spectra generation is often assumed to be minimal, and peptide identifications are typically based on a single tandem mass spectrum. In this paper, we characterize the variance of scores derived from replicate tandem mass spectra using several database search algorithms and demonstrate the effects of spectral variability on the correct identification of peptides. We show that the variance associated with the collection of tandem mass spectra can be substantial leading to sizable errors in search algorithm scores ( approximately 5-25% RSD) and ultimately incorrect assignments. Processing strategies are discussed to minimize the impact of tandem mass spectra variability on peptide identification.

The purpose of this work is to develop and verify statistical models for protein identification using peptide identifications derived from the results of tandem mass spectral database searches. Recently we have presented a probabilistic model for peptide identification that uses hypergeometric distribution to approximate fragment ion matches of database peptide sequences to experimental tandem mass spectra. Here we apply statistical models to the database search results to validate protein identifications. For this we formulate the protein identification problem in terms of two independent models, two-hypothesis binomial and multinomial models, which use the hypergeometric probabilities and cross-correlation scores, respectively. Each database search result is assumed to be a probabilistic event. The Bernoulli event has two outcomes: a protein is either identified or not. The probability of identifying a protein at each Bernoulli event is determined from relative length of the protein in the database (the null hypothesis) or the hypergeometric probability scores of the protein's peptides (the alternative hypothesis). We then calculate the binomial probability that the protein will be observed a certain number of times (number of database matches to its peptides) given the size of the data set (number of spectra) and the probability of protein identification at each Bernoulli event. The ratio of the probabilities from these two hypotheses (maximum likelihood ratio) is used as a test statistic to discriminate between true and false identifications. The significance and confidence levels of protein identifications are calculated from the model distributions. The multinomial model combines the database search results and generates an observed frequency distribution of cross-correlation scores (grouped into bins) between experimental spectra and identified amino acid sequences. The frequency distribution is used to generate p-value probabilities of each score bin. The probabilities are then normalized with respect to score bins to generate normalized probabilities of all score bins. A protein identification probability is the multinomial probability of observing the given set of peptide scores. To reduce the effect of random matches, we employ a marginalized multinomial model for small values of cross-correlation scores. We demonstrate that the combination of the two independent methods provides a useful tool for protein identification from results of database search using tandem mass spectra. A receiver operating characteristic curve demonstrates the sensitivity and accuracy level of the approach. The shortcomings of the models are related to the cases when protein assignment is based on unusual peptide fragmentation patterns that dominate over the model encoded in the peptide identification process. We have implemented the approach in a program called PROT_PROBE.

The recent completion of human, Anopheles gambiae, and Plasmodium falciparum genomes relevant to the study of human malaria allows the application of modern proteomic technologies to complement previously implemented conventional approaches. Proteomic analysis has been employed to elucidate global protein expression profiles, subcellular localization of gene products, and host-pathogen interactions that are central to disease pathogenesis and treatment. The high-throughput nature of these techniques is in accord with the pace of drug and vaccine development that have the potential to directly reduce the morbidity and mortality of disease.

As the speed with which proteomic labs generate data increases along with the scale of projects they are undertaking, the resulting data storage and data processing problems will continue to challenge computational resources. This is especially true for shotgun proteomic techniques that can generate tens of thousands of spectra per instrument each day. One design factor leading to many of these problems is caused by storing spectra and the database identifications for a given spectrum as individual files. While these problems can be addressed by storing all of the spectra and search results in large relational databases, the infrastructure to implement such a strategy can be beyond the means of academic labs. We report here a series of unified text file formats for storing spectral data (MS1 and MS2) and search results (SQT) that are compact, easily parsed by both machine and humans, and yet flexible enough to be coupled with new algorithms and data-mining strategies., (Copyright 2004 John Wiley & Sons, Ltd.)

Mass spectrometry provides key tools for the analysis of proteins. New types of mass spectrometers that provide enhanced capability to discover protein identities and perform improved proteomic experiments are discussed. Handling the complex mixtures of peptides and proteins generated from protein complexes and whole cells requires multidimensional separations; several forms of separation are discussed. Applications of mass spectrometry-based approaches for contemporary proteomic analyses are described.

We present a new probability-based method for protein identification using tandem mass spectra and protein databases. The method employs a hypergeometric distribution to model frequencies of matches between fragment ions predicted for peptide sequences with a specific (M + H)+ value (at some mass tolerance) in a protein sequence database and an experimental tandem mass spectrum. The hypergeometric distribution constitutes null hypothesis-all peptide matches to a tandem mass spectrum are random. It is used to generate a score characterizing the randomness of a database sequence match to an experimental tandem mass spectrum and to determine the level of significance of the null hypothesis. For each tandem mass spectrum and database search, a peptide is identified that has the least probability of being a random match to the spectrum and the corresponding level of significance of the null hypothesis is determined. To check the validity of the hypergeometric model in describing fragment ion matches, we used chi2 test. The distribution of frequencies and corresponding hypergeometric probabilities are generated for each tandem mass spectrum. No proteolytic cleavage specificity is used to create the peptide sequences from the database. We do not use any empirical probabilities in this method. The scores generated by the hypergeometric model do not have a significant molecular weight bias and are reasonably independent of database size. The approach has been implemented in a database search algorithm, PEP_PROBE. By using a large set of tandem mass spectra derived from a set of peptides created by digestion of a collection of known proteins using four different proteases, a false positive rate of 5% is demonstrated.

Liquid chromatography paired with tandem mass spectrometry is a standard technique for identifying peptides from complex protein mixtures. Most fragment ion spectra acquired by this technique are unique, but some are repeated. Similarities among the spectra from 1D and 2D liquid chromatography experiments were calculated by the dot product algorithm. Similar spectra were grouped, and the degree of duplication was calculated for each sample. In 1D liquid chromatography data from 1D gel bands, 18% of the fragment ion spectra were duplicates. A six-cycle 2D liquid chromatographic separation of more than 200 proteins produced 28% duplicate spectra. A rat hippocampal homogenate analyzed by a 12-cycle 2D liquid chromatographic separation contained 25% duplicate spectra. Removal of these duplicate spectra, however, resulted in fewer peptides being successfully identified by SEQUEST. We propose a modification for peptide identification algorithms that would improve their performance and accuracy by explicitly recognizing and making use of spectral similarity.

We describe a method that allows for the concurrent proteomic analysis of both membrane and soluble proteins from complex membrane-containing samples. When coupled with multidimensional protein identification technology (MudPIT), this method results in (i) the identification of soluble and membrane proteins, (ii) the identification of post-translational modification sites on soluble and membrane proteins, and (iii) the characterization of membrane protein topology and relative localization of soluble proteins. Overlapping peptides produced from digestion with the robust nonspecific protease proteinase K facilitates the identification of covalent modifications (phosphorylation and methylation). High-pH treatment disrupts sealed membrane compartments without solubilizing or denaturing the lipid bilayer to allow mapping of the soluble domains of integral membrane proteins. Furthermore, coupling protease protection strategies to this method permits characterization of the relative sidedness of the hydrophilic domains of membrane proteins.

Membrane proteins perform some of the most important functions in the cell, including the regulation of cell signaling through surface receptors, cell-cell interactions, and the intracellular compartmentalization of organelles. Recent developments in proteomic strategies have focused on the inclusion of membrane proteins in high-throughput analyses. While slow and steady progress continues to be made in gel-based technologies, significant advances have been reported in non-gel shotgun methods using liquid chromatography coupled to mass spectrometry (LC/MS). These latter strategies facilitate the identification of large numbers of membrane proteins and modifications, and have the potential to provide insights into protein topology and orientation in membranes.

Liquid chromatography techniques have been successfully coupled with mass spectrometers to provide a robust method for the identification of proteins in mixtures. Chromatography can be performed in-line with the mass spectrometer and data acquisition can be directly interfaced with search algorithms for identification by correlation with databases.

We describe the initial characterization of the wheat amyloplast proteome, consisting of the identification and classification of 171 proteins. Whole amyloplasts and purified amyloplast membranes were prepared from wheat (Triticum aestivum). Protein extracts were examined by one-dimensional and two-dimensional electrophoresis, followed by high performance liquid chromatography-tandem mass spectrometry of separated proteins. Tandem mass spectrometry data of individual peptides was then searched by SEQUEST, using a database containing known protein sequences from both wheat and other homologous cereal crops. Using this approach we identified 108 proteins from whole amyloplasts and 63 proteins from purified amyloplast membranes. The majority of protein identifications were derived from protein sequences from cereal crops other than wheat, for which relatively little gene sequence data is available. The highest percentage of protein identifications obtained from any individual species was 46% of the total number of proteins identified, using sequence data found in our proprietary rice (Oryza sativa) genome database.