Search

Differential precipitation of proteins (DiffPOP) is a simple technique for fractionating complex protein mixtures. Using stepwise addition of acidified methanol, ten distinct subsets of proteins can be selectively precipitated by centrifugation and identified by mass spectrometry-based proteomics. We have previously shown that the ability of a protein to resist precipitation can be altered by drug binding, which enabled us to identify a novel drug-target interaction. Here, we show that the addition of DiffPOP to a standard LC-MS proteomics workflow results in a three-dimensional separation of peptides that increases protein coverage and peptide identifications. Importantly, DiffPOP reveals solubility differences between proteoforms, potentially providing valuable insights that are typically lost in bottom-up proteomics.

Prader-Willi syndrome (PWS) is a developmental disorder caused by loss of maternally imprinted genes on 15q11-q13, including melanoma antigen gene family member L2 (MAGEL2). The clinical phenotypes of PWS suggest impaired hypothalamic neuroendocrine function; however, the exact cellular defects are unknown. Here, we report deficits in secretory granule (SG) abundance and bioactive neuropeptide production upon loss of MAGEL2 in humans and mice. Unbiased proteomic analysis of Magel2pΔ/m+ mice revealed a reduction in components of SG in the hypothalamus that was confirmed in 2 PWS patient-derived neuronal cell models. Mechanistically, we show that proper endosomal trafficking by the MAGEL2-regulated WASH complex is required to prevent aberrant lysosomal degradation of SG proteins and reduction of mature SG abundance. Importantly, loss of MAGEL2 in mice, NGN2-induced neurons, and human patients led to reduced neuropeptide production. Thus, MAGEL2 plays an important role in hypothalamic neuroendocrine function, and cellular defects in this pathway may contribute to PWS disease etiology. Moreover, these findings suggest unanticipated approaches for therapeutic intervention.

Data-independent acquisition (DIA) is a promising technique for the proteomic analysis of complex protein samples. A number of studies have claimed that DIA experiments are more reproducible than data-dependent acquisition (DDA), but these claims are unsubstantiated since different data analysis methods are used in the two methods. Data analysis in most DIA workflows depends on spectral library searches, whereas DDA typically employs sequence database searches. In this study, we examined the reproducibility of the DIA and DDA results using both sequence database and spectral library search. The comparison was first performed using a cell lysate and then extended to an interactome study. Protein overlap among the technical replicates in both DDA and DIA experiments was 30% higher with library-based identifications than with sequence database identifications. The reproducibility of quantification was also improved with library search compared to database search, with the mean of the coefficient of variation decreasing more than 30% and a reduction in the number of missing values of more than 35%. Our results show that regardless of the acquisition method, higher identification and quantification reproducibility is observed when library search was used.

Mass spectrometry-based proteomics is an invaluable tool for addressing important biological questions. Data-dependent acquisition methods effectuate stochastic acquisition of data in complex mixtures, which results in missing identifications across replicates. We developed a search approach that improves the reproducibility of data acquired from any mass spectrometer. In our approach, a spectral library is built from the identification results from a database search, and then, the library is used to research the same data files to obtain the final result. We showed that higher identification and quantification reproducibility is achieved with the dual-search approach than with a typical database search. Four datasets with different complexity were compared: (1) data from a cell lysate study performed in our lab, (2) data from an interactome study performed in our lab, (3) a publicly available extracellular vesicles dataset, and (4) a publicly available phosphoproteomics dataset. Our results show that the dual-search approach can be widely and easily used to improve data quality in proteomics data.

The characterization of complex biological systems based on high-throughput protein quantification through mass spectrometry commonly involves differential expression analysis between replicate samples originating from different experimental conditions. Here we present Proteomics INTegrator (PINT), a new user-friendly Web-based platform-independent system to store, visualize, and query proteomics experiment results. PINT provides an extremely flexible query interface that allows advanced Boolean algebra-based data filtering of many different proteomics features such as confidence values, abundance levels or ratios, data set overlaps, sample characteristics, as well as UniProtKB annotations, which are transparently incorporated into the system. In addition, PINT allows developers to incorporate data visualization and analysis tools, such as PSEA-Quant and Reactome pathway analysis, for data set enrichment analysis. PINT serves as a centralized hub for large-scale proteomics data and as a platform for data analysis, facilitating the interpretation of proteomics results and expediting biologically relevant conclusions.

Mammalian cells are organized into different compartments that separate and facilitate physiological processes by providing specialized local environments and allowing different, otherwise incompatible biological processes to be carried out simultaneously. Proteins are targeted to these subcellular locations where they fulfill specialized, compartment-specific functions. Spatial proteomics aims to localize and quantify proteins within subcellular structures., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Protein arginylation-enzymatic addition of the amino acid arginine (Arg) to proteins, mediated by arginyltransferase ATE1, has been discovered in 1963, but is still relatively poorly understood. Studies of arginylation present many technical challenges, which arise from the fact that Arg is a regular amino acid that also incorporates into proteins during translation. Thus, in vitro arginylation needs to be conducted in a strictly ribosome-free system, in highly controlled conditions. Identification of arginylated proteins is currently only possible by high precision mass spectrometry, which relies on very high mass accuracy of the instruments, specific ionization patterns during mass fragmentation, as well as multiple stringent steps of automated and manual validation. Below we describe the methods of in vitro arginylation and mass spectrometry analysis of arginylated proteins, developed by our groups during the last 15 years., (© 2019 Elsevier Inc. All rights reserved.)

Measuring proteome response to perturbations is critical for understanding the underlying mechanisms involved. Traditional quantitative proteomic methods are limited by the large numbers of proteins in the proteome and the mass spectrometer's dynamic range. A previous method uses the biorthogonal reagent azidohomoalanine (AHA), a methionine analog, for labeling, enrichment and detection of newly synthesized proteins (NSPs). Newly synthesized AHA proteins can be coupled to biotin via CuAAC-mediated click chemistry and enriched using avidin-based affinity purification. The combination of AHA-mediated NSP labeling with metabolic stable isotope labeling allows quantitation of low-abundant, newly secreted proteins by mass spectrometry (MS). However, the resulting multiplicity of labeling complicates NSP analysis. We developed a new NSP quantification strategy, called HILAQ (heavy isotope-labeled azidohomoalanine quantification), that uses a heavy isotope-labeled AHA molecule to enable NSP labeling, enrichment, identification and quantification. In addition, the AHA-peptide enrichment used in HILAQ improves both the identification and quantification of NSPs over AHA-protein enrichment. Here, we provide a description of the HILAQ method that includes procedures for (i) pulse-labeling and harvesting NSPs; (ii) addition of biotin by click reaction; (iii) protein precipitation; (iv) protein digestion; (v) enrichment of AHA-biotin peptides by NeutrAvidin beads and four-step elution; (vi) MS analysis; and (vii) data analysis for the identification and quantification of NSPs by ProLuCID and pQuant. We demonstrate our HILAQ approach by identifying NSPs from cell cultures, but we anticipate that it can be adapted for applications in animal models. The whole protocol takes ~6 d to complete.

During an acute infection, antigenic stimulation leads to activation, expansion, and differentiation of naïve CD8 + T cells, first into cytotoxic effector cells and eventually into long-lived memory cells. T cell antigen receptors (TCRs) detect antigens on antigen-presenting cells (APCs) in the form of antigenic peptides bound to major histocompatibility complex I (MHC-I)-encoded molecules and initiate TCR signal transduction network. This process is mediated by phosphorylation of many intracellular signaling proteins. Protein O-GlcNAc modification is another post-translational modification involved in this process, which often has either reciprocal or synergistic roles with phosphorylation. In this study, using a chemoenzymatic glycan labeling technique and proteomics analysis, we compared protein O-GlcNAcylation of murine effector and memory-like CD8 + T cells differentiated in vitro. By quantitative proteomics analysis, we identified 445 proteins that are significantly regulated in either effector- or memory-like T cell subsets. Furthermore, qualitative and quantitative analysis identified highly regulated protein clusters that suggest involvement of this post-translational modification in specific cellular processes. In effector-like T cells, protein O-GlcNAcylation is heavily involved in transcriptional and translational processes that drive fast effector T cells proliferation. During the formation of memory-like T cells, protein O-GlcNAcylation is involved in a more specific, perhaps more targeted regulation of transcription, mRNA processing, and translation. Significantly, O-GlcNAc plays a critical role as part of the "histone code" in both CD8 + T cells subgroups.

Here we describe a new strategy, HILAQ (Heavy Isotope Labeled Azidohomoalanine Quantification), to rapidly quantify the molecular vulnerability profile to oxytosis, which is an oxidative stress-induced programed cell death pathway that has been reported to be involved in aging and neurodegenerative diseases. HILAQ was able to quantify 1962 newly synthesized proteins (NSPs) after 1 h of pulse labeling in HEK293T cell line, while 353 proteins were quantified using the previously published QuaNCAT protocol. HILAQ was successfully applied to the HT22 oxytosis model. 226 proteins were found to have a two-fold change in abundance, and 108 proteins were enriched in the cell death pathway, demonstrating the utility of HT22 cells as a tool to study the molecular details of cell death involved in neurodegenerative diseases. The HILAQ strategy simplifies the analysis of newly synthesized proteomes through the use of isobaric labels and achieves higher sensitivity than previously published methods.

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.

The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.

Mass spectrometric strategies to identify protein subpopulations involved in specific biological functions rely on covalently tagging biotin to proteins using various chemical modification methods. The biotin tag is primarily used for enrichment of the targeted subpopulation for subsequent mass spectrometry (MS) analysis. A limitation of these strategies is that MS analysis does not easily discriminate unlabeled contaminants from the labeled protein subpopulation under study. To solve this problem, we developed a flexible method that only relies on direct MS detection of biotin-tagged proteins called "Direct Detection of Biotin-containing Tags" (DiDBiT). Compared with conventional targeted proteomic strategies, DiDBiT improves direct detection of biotinylated proteins ∼200 fold. We show that DiDBiT is applicable to several protein labeling protocols in cell culture and in vivo using cell permeable NHS-biotin and incorporation of the noncanonical amino acid, azidohomoalanine (AHA), into newly synthesized proteins, followed by click chemistry tagging with biotin. We demonstrate that DiDBiT improves the direct detection of biotin-tagged newly synthesized peptides more than 20-fold compared to conventional methods. With the increased sensitivity afforded by DiDBiT, we demonstrate the MS detection of newly synthesized proteins labeled in vivo in the rodent nervous system with unprecedented temporal resolution as short as 3 h.

Suppressor/Enhancer of Lin-12-like (Sel1L) is an adaptor protein for the E3 ligase hydroxymethylglutaryl reductase degradation protein 1 (Hrd1) involved in endoplasmic reticulum-associated degradation (ERAD). Sel1L's physiological importance in mammalian ERAD, however, remains to be established. Here, using the inducible Sel1L knockout mouse and cell models, we show that Sel1L is indispensable for Hrd1 stability, ER homeostasis, and survival. Acute loss of Sel1L leads to premature death in adult mice within 3 wk with profound pancreatic atrophy. Contrary to current belief, our data show that mammalian Sel1L is required for Hrd1 stability and ERAD function both in vitro and in vivo. Sel1L deficiency disturbs ER homeostasis, activates ER stress, attenuates translation, and promotes cell death. Serendipitously, using a biochemical approach coupled with mass spectrometry, we found that Sel1L deficiency causes the aggregation of both small and large ribosomal subunits. Thus, Sel1L is an indispensable component of the mammalian Hrd1 ERAD complex and ER homeostasis, which is essential for protein translation, pancreatic function, and cellular and organismal survival.

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.)

We describe a solid phase microextraction (SPME), multistep elution, transient isotachophoresis (tITP) capillary electrophoresis-tandem mass spectrometry (CE-MS/MS) procedure which employs a high sensitivity porous electrospray ionization (ESI) sprayer for the proteomic analysis of a moderately complex protein mixture. In order to improve comprehensiveness and sensitivity over a previously reported proteomic application of the ESI sprayer, we evaluated preconcentration with SPME and multistep elution prior to tITP stacking and CE separation. To maximize separation efficiency, we primarily employed electrokinetic methods for elution and separation after loading the sample by application of pressure. Conditions were developed for optimum simultaneous electrokinetic elution and sample stacking using a tryptic digest of 16 proteins to maximize peptide identifications and minimize band broadening. We performed comparative proteomic analysis of a dilution series using CE and nanoflow liquid chromatography (nLC). We found complementary peptide and protein identifications with larger quantities (100 ng) of a Pyrococcus furiosus tryptic digest, but with mass-limited amounts (5 ng) CE was 3 times more effective at identifying proteins. We attribute these gains in sensitivity to lower noise levels with the porous CE sprayer, illustrated by better signal-to-noise ratios of peptide precursor ions and associated higher XCorr values of identified peptides when compared directly to nLC. From comparative analysis of SPME-tITP-CE with direct injection CE, the SPME-tITP process improved comprehensiveness and sensitivity.

AMPA receptor (AMPA-R) complexes consist of channel-forming subunits, GluA1-4, and auxiliary proteins, including TARPs, CNIHs, synDIG1, and CKAMP44, which can modulate AMPA-R function in specific ways. The combinatorial effects of four GluA subunits binding to various auxiliary subunits amplify the functional diversity of AMPA-Rs. The significance and magnitude of molecular diversity, however, remain elusive. To gain insight into the molecular complexity of AMPA and kainate receptors, we compared the proteins that copurify with each receptor type in the rat brain. This interactome study identified the majority of known interacting proteins and, more importantly, provides candidates for additional studies. We validate the claudin homolog GSG1L as a newly identified binding protein and unique modulator of AMPA-R gating, as determined by detailed molecular, cellular, electrophysiological, and biochemical experiments. GSG1L extends the functional variety of AMPA-R complexes, and further investigation of other candidates may reveal additional complexity of ionotropic glutamate receptor function., (Copyright © 2012 The Authors. Published by Elsevier Inc. All rights reserved.)

Unlabelled: We present an updated version of the TFold software for pinpointing differentially expressed proteins in shotgun proteomics experiments. Given an FDR bound, the updated approach uses a theoretical FDR estimator to maximize the number of identifications that satisfy both a fold-change cutoff that varies with the t-test P-value as a power law and a stringency criterion that aims to detect lowly abundant proteins. The new version has yielded significant improvements in sensitivity over the previous one., Availability: Freely available for academic use at http://pcarvalho.com/patternlab.

Molecular interactions are key processes that drive the functions of molecules. Large data sets of protein interaction data are being assembled that will help analyze structural and functional aspects of interactions. This chapter introduces techniques for analyzing both the structural aspects of molecular interactions and methods to use this information to understand and define biological pathways., (© 2011 by John Wiley & Sons, Inc.)

Summary: We present an approach to statistically pinpoint differentially expressed proteins that have quantitation values near the quantitation threshold and are not identified in all replicates (marginal cases). Our method uses a Bayesian strategy to combine parametric statistics with an empirical distribution built from the reproducibility quality of the technical replicates., Availability: The software is freely available for academic use at http://pcarvalho.com/patternlab.

Knowledge about the protein targets of therapeutic agents is critical for understanding drug mode of action. Described here is a mass spectrometry-based proteomics method for identifying the protein target(s) of drug molecules that is potentially applicable to any drug compound. The method, which involves making thermodynamic measurements of protein-folding reactions in complex biological mixtures to detect protein-drug interactions, is demonstrated in an experiment to identify yeast protein targets of the immunosuppressive drug, cyclosporin A (CsA). Two of the ten protein targets identified in this proof of principle work were cyclophilin A and UDP-glucose-4-epimerase, both of which are known to interact with CsA, the former through a direct binding event (K(d) approximately 70 nM) and the latter through an indirect binding event. These two previously known protein targets validate the methodology and its ability to detect both the on- and off-target effects of protein-drug interactions. The other eight protein targets discovered here, which include several proteins involved in glucose metabolism, create a new framework in which to investigate the molecular basis of CsA side effects in humans.

MicroRNAs (miRNAs) inhibit mRNA expression in general by base pairing to the 3'UTR of target mRNAs and consequently inhibiting translation and/or initiating poly(A) tail deadenylation and mRNA destabilization. Here we examine the mechanism and kinetics of miRNA-mediated deadenylation in mouse Krebs-2 ascites extract. We demonstrate that miRNA-mediated mRNA deadenylation occurs subsequent to initial translational inhibition, indicating a two-step mechanism of miRNA action, which serves to consolidate repression. We show that a let-7 miRNA-loaded RNA-induced silencing complex (miRISC) interacts with the poly(A)-binding protein (PABP) and the CAF1 and CCR4 deadenylases. In addition, we demonstrate that miRNA-mediated deadenylation is dependent upon CAF1 activity and PABP, which serves as a bona fide miRNA coactivator. Importantly, we present evidence that GW182, a core component of the miRISC, directly interacts with PABP via its C-terminal region and that this interaction is required for miRNA-mediated deadenylation.

We evaluate the effect of ion-abundance threshold settings for data-dependent acquisition on a hybrid LTQ-Orbitrap mass spectrometer, analyzing features such as the total number of spectra collected, the signal to noise ratio of the full MS scans, the spectral quality of the tandem mass spectra acquired, and the number of peptides and proteins identified from a complex mixture. We find that increasing the threshold for data-dependent acquisition generally decreases the quantity but increases the quality of the spectra acquired. This is especially true when the threshold setting is set above the noise level of the full MS scan. We compare two distinct experimental configurations: one where full MS scans are acquired in the Orbitrap analyzer while tandem MS scans are acquired in the LTQ analyzer, and one where both full MS and tandem MS scans are acquired in the LTQ analyzer. We examine the number of spectra, peptides, and proteins identified under various threshold conditions, and we find that the optimal threshold setting is at or below the respective noise level of the instrument regardless of whether the full MS scan is performed in the Orbitrap or in the LTQ analyzer. When comparing the high-throughput identification performance of the two analyzers, we conclude that, used at optimal threshold levels, the LTQ and the Orbitrap identify similar numbers of peptides and proteins. The higher scan speed of the LTQ, which results in more spectra being collected, is roughly compensated by the higher mass accuracy of the Orbitrap, which results in improved database searching and peptide validation software performance.

Shotgun proteomics is based on identification and quantification of peptides from digested proteins using tandem mass spectrometry. In this chapter, we discuss computational methods to analyze tandem mass spectra of peptides, including database searching, de novo peptide sequencing, hybrid approaches, library searching, and unrestricted modification search. A special focus is given to database searching programs since they are most widely used. The process of inferring proteins from identified peptides is then discussed. We also provide description of key steps in the quantitative analysis of mass spectrometry proteomics data.

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.

Background: DNA-bound transcription factors recruit an array of coregulatory proteins that influence gene expression. We previously demonstrated that DNA functions as an allosteric modulator of estrogen receptor alpha (ERalpha) conformation, alters the recruitment of regulatory proteins, and influences estrogen-responsive gene expression and reasoned that it would be useful to develop a method of isolating proteins associated with the DNA-bound ERalpha using full-length receptor and endogenously-expressed nuclear proteins., Results: We have developed a novel approach to isolate large complexes of proteins associated with the DNA-bound ERalpha. Purified ERalpha and HeLa nuclear extracts were combined with oligos containing ERalpha binding sites and fractionated on agarose gels. The protein-DNA complexes were isolated and mass spectrometry analysis was used to identify proteins associated with the DNA-bound receptor. Rather than simply identifying individual proteins that interact with ERalpha, we identified interconnected networks of proteins with a variety of enzymatic and catalytic activities that interact not only with ERalpha, but also with each other. Characterization of a number of these proteins has demonstrated that, in addition to their previously identified functions, they also influence ERalpha activity and expression of estrogen-responsive genes., Conclusion: The agarose gel fractionation method we have developed would be useful in identifying proteins that interact with DNA-bound transcription factors and should be easily adapted for use with a variety of cultured cell lines, DNA sequences, and transcription factors.

Two modes of separation coupled with MS enable researchers to study complicated biological structures.

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.

Loss-of-function diseases are often caused by a mutation in a protein traversing the secretory pathway that compromises the normal balance between protein folding, trafficking, and degradation. We demonstrate that the innate cellular protein homeostasis, or proteostasis, capacity can be enhanced to fold mutated enzymes that would otherwise misfold and be degraded, using small molecule proteostasis regulators. Two proteostasis regulators are reported that alter the composition of the proteostasis network in the endoplasmic reticulum through the unfolded protein response, increasing the mutant folded protein concentration that can engage the trafficking machinery, restoring function to two nonhomologous mutant enzymes associated with distinct lysosomal storage diseases. Coapplication of a pharmacologic chaperone and a proteostasis regulator exhibits synergy because of the former's ability to further increase the concentration of trafficking-competent mutant folded enzymes. It may be possible to ameliorate loss-of-function diseases by using proteostasis regulators alone or in combination with a pharmacologic chaperone.

Large-scale biology emerged out of the efforts to sequence genomes of important organisms. Based on resources created by whole genome sequencing, large-scale analyses of messenger RNA (mRNA) and protein expression are now possible. With the availability of large amounts of genomic sequence information, a convenient method for the identification and analysis of proteins based on proteolytic digestion into peptides emerged. Processes to fragment peptides using collision-activated dissociation (CAD) in tandem mass spectrometers and computer algorithms to match the tandem mass spectra of peptides to sequences in databases enable rapid identification of amino acid sequences, and hence proteins, present in mixtures. The inherent complexity of the peptide mixtures has necessitated improvements in methodology for mass spectrometry (MS) analysis of peptides.

Posttranslational arginylation is critical for embryogenesis, cardiovascular development, and angiogenesis, but its molecular effects and the identity of proteins arginylated in vivo are largely unknown. Here we report a global analysis of this modification on the protein level and identification of 43 proteins arginylated in vivo on highly specific sites. Our data demonstrate that unlike previously believed, arginylation can occur on any N-terminally exposed residue likely defined by a structural recognition motif on the protein surface, and that it preferentially affects a number of physiological systems, including cytoskeleton and primary metabolic pathways. The results of our study suggest that protein arginylation is a general mechanism for regulation of protein structure and function and outline the potential role of protein arginylation in cell metabolism and embryonic development.

Multidimensional liquid chromatography in combination with tandem mass spectrometry has been used to analyze a variety of biological structures including protein complexes. Incorporating this approach with autosampling devices presents a number of problems including decreased sensitivity due to exposure to extra surfaces, carryover from run to run, and increased dead volume. We developed a device, termed Radial Column Array for Distribution and Automation (RCADiA), to automate multiple MuDPIT experiments while eliminating many of these problems and maintaining a high resolution and sensitive analysis. The design, which places each sample downstream of any common fluid path, presents a low risk of carryover between successive analyses. Beyond the convenience of automation, the RCADiA platform also produces data of similar quality to the standard method of performing individual MuDPIT experiments. We demonstrate this device by performing a comparative analysis of mitochondria enriched from rat liver and spinal cord.

We previously reported the metabolic 15N labeling of a rat where enrichment ranged from 94% to 74%. We report here an improved labeling strategy which generates 94% 15N enrichment throughout all tissues of the rat. A high 15N enrichment of the internal standard is necessary for accurate quantitation, and thus, this approach will allow quantitative mass spectrometry analysis of animal models of disease targeting any tissue.

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.

A proteomic method that purifies and identifies palmitoylated proteins from complex protein extracts is described. Using the fatty acid exchange labeling chemistry (described in the preceding report), palmitoyl modifications are exchanged for biotinylated compounds, allowing the subset of palmitoyl-proteins to be affinity-purified and then identified by mass spectroscopic protein identification technologies. The advantages and pitfalls of this new technology are discussed within the context of the recent application of this method in the yeast Saccharomyces cerevisiae.

Multidimensional separation is one of the most successful approaches for proteomics studies that deal with complex samples. We have developed an automated ultra-high-pressure multidimensional liquid chromatography system that operates up to approximately 20 kpsi to improve separations and increase protein coverage from limited amount of samples. The reversed-phase gradient is operated in the constant-flow mode opposed to the constant-pressure mode, which is typical of previous ultra-high-pressure systems. In contrast to constant-pressure systems, the gradient shape is fully controllable and can be optimized for the type of samples to be run. The system also features fast sample loading/desalting using a vented column approach to improve sample throughput. This approach was validated on a soluble fraction from yeast lysate where we achieved approximately 30% more protein identifications using a 60-cm-long triphasic capillary column than with our traditional approach. Advantages of the use of a relatively long reversed-phase column (approximately 50 cm) for MudPIT-type experiments are also discussed.

In this paper, we present an intensity-based probability function to identify peptides from tandem mass spectra and amino acid sequence databases. The function is an approximation to the central limiting theorem, and it explicitly depends on the cumulative product ion intensities, number of product ions of a peptide, and expectation value of the cumulative intensity. We compare the results of database searches using the new scoring function and scoring functions from earlier algorithms, which implement hypergeometric probability, Poisson's model, and cross-correlation scores. For a standard protein mixture (tandem mass spectra generated from the mixture of five known proteins), we generate receiver operating curves with all scoring schemes. The receiver operating curves show that the shared peaks count-based probability methods (like Poisson and hypergeometric models) are the most specific for matching high-quality tandem mass spectra. The intensity-based (central limit model) and intensity-modeled (cross-correlation) methods are more sensitive when matching low-quality tandem mass spectra, where the number of shared peaks is insufficient to correctly identify a peptide. Cross-correlation methods show a small advantage over the intensity-based probability method.

Stable isotope-enriched molecules are used as internal standards and as tracers of in vivo substrate metabolism. The accurate conversion of measured ratios in the mass spectrometer to mole ratios is complicated because a polyatomic molecule containing enriched atoms will result in a combinatorial distribution of isotopomers depending on the enrichment and number of "labeled" atoms. This effect could potentially cause a large error in the mole ratio measurement depending on which isotope peak or peaks were used to determine the ratio. We report a computational method that predicts isotope distributions over a range of enrichments and compares the predicted distributions to experimental peptide isotope distributions obtained by Fourier transform ion cyclotron resonance mass spectrometry. Our approach is accurate with measured enrichments within 1.5% of expected isotope distributions. The method is also precise with 4.9, 2.0, and 0.8% relative standard deviations for peptides containing 59, 79, and 99 atom % excess (15)N, respectively. The approach is automated making isotope enrichment calculations possible for thousands of peptides in a single muLC-FTICR-MS experiment.

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.

Proteomic analysis of complex protein mixtures using proteolytic digestion and liquid chromatography in combination with tandem mass spectrometry is a standard approach in biological studies. Data-dependent acquisition is used to automatically acquire tandem mass spectra of peptides eluting into the mass spectrometer. In more complicated mixtures, for example, whole cell lysates, data-dependent acquisition incompletely samples among the peptide ions present rather than acquiring tandem mass spectra for all ions available. We analyzed the sampling process and developed a statistical model to accurately predict the level of sampling expected for mixtures of a specific complexity. The model also predicts how many analyses are required for saturated sampling of a complex protein mixture. For a yeast-soluble cell lysate 10 analyses are required to reach a 95% saturation level on protein identifications based on our model. The statistical model also suggests a relationship between the level of sampling observed for a protein and the relative abundance of the protein in the mixture. We demonstrate a linear dynamic range over 2 orders of magnitude by using the number of spectra (spectral sampling) acquired for each protein.

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.

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.

A comparative analysis of protein identification for a total of 162 protein spots separated by two-dimensional gel electrophoresis from two fully sequenced archaea, Methanococcus jannaschii and Pyrococcus furiosus, using MALDI-TOF peptide mass mapping (PMM) and mu LC-MS/MS is presented. 100% of the gel spots analyzed were successfully matched to the predicted proteins in the two corresponding open reading frame databases by mu LC-MS/MS while 97% of them were identified by MALDI-TOF PMM. The high success rate from the PMM resulted from sample desalting/concentrating with ZipTip(C18) and optimization of several PMM search parameters including a 25 ppm average mass tolerance and the application of two different protein molecular weight search windows. By using this strategy, low-molecular weight (<23 kDa) proteins could be identified unambiguously with less than 5 peptide matches. Nine percent of spots were identified as containing multiple proteins. By using mu LC-MS/MS, 50% of the spots analyzed were identified as containing multiple proteins. mu LC-MS/MS demonstrated better protein sequence coverage than MALDI-TOF PMM over the entire mass range of proteins identified. MALDI-TOF and PMM produced unique peptide molecular weight matches that were not identified by mu LC-MS/MS. By incorporating amino acid sequence modifications into database searches, combined sequence coverage obtained from these two complimentary ionization methods exceeded 50% for approximately 70% of the 162 spots analyzed. This improved sequence coverage in combination with enzymatic digestions of different specificity is proposed as a method for analysis of post-translational modification from 2D-gel separated proteins.

We describe a simple, rapid method for protein complex purification in planta. Using a biotin peptide as an affinity tag with TATA-box binding protein (TBP), 86 unique proteins present in the purified complex were identified by tandem mass spectrometry. We identified proteins known to be associated with TBP, and many other proteins involved in pre-mRNA processing and chromatin remodeling. The identification of these novel protein-protein associations will upon further investigations provide new insights into the mechanisms of mRNA transcription and pre-mRNA processing.