Endogenous Peptide Discovery of the Rat Circadian Clock: A FOCUSED STUDY OF THE SUPRACHIASMATIC NUCLEUS BY ULTRAHIGH PERFORMANCE TANDEM MASS SPECTROMETRY*

Understanding how a small brain region, the suprachiasmatic nucleus (SCN), can synchronize the body''s circadian rhythms is an ongoing research area. This important time-keeping system requires a complex suite of peptide hormones and transmitters that remain incompletely characterized. Here, capillary liquid chromatography and FTMS have been coupled with tailored software for the analysis of endogenous peptides present in the SCN of the rat brain. After ex vivo processing of brain slices, peptide extraction, identification, and characterization from tandem FTMS data with <5-ppm mass accuracy produced a hyperconfident list of 102 endogenous peptides, including 33 previously unidentified peptides, and 12 peptides that were post-translationally modified with amidation, phosphorylation, pyroglutamylation, or acetylation. This characterization of endogenous peptides from the SCN will aid in understanding the molecular mechanisms that mediate rhythmic behaviors in mammals.Central nervous system neuropeptides function in cell-to-cell signaling and are involved in many physiological processes such as circadian rhythms, pain, hunger, feeding, and body weight regulation (1–4). Neuropeptides are produced from larger protein precursors by the selective action of endopeptidases, which cleave at mono- or dibasic sites and then remove the C-terminal basic residues (1, 2). Some neuropeptides undergo functionally important post-translational modifications (PTMs),1 including amidation, phosphorylation, pyroglutamylation, or acetylation. These aspects of peptide synthesis impact the properties of neuropeptides, further expanding their diverse physiological implications. Therefore, unveiling new peptides and unreported peptide properties is critical to advancing our understanding of nervous system function.Historically, the analysis of neuropeptides was performed by Edman degradation in which the N-terminal amino acid is sequentially removed. However, analysis by this method is slow and does not allow for sequencing of the peptides containing N-terminal PTMs (5). Immunological techniques, such as radioimmunoassay and immunohistochemistry, are used for measuring relative peptide levels and spatial localization, but these methods only detect peptide sequences with known structure (6). More direct, high throughput methods of analyzing brain regions can be used.Mass spectrometry, a rapid and sensitive method that has been used for the analysis of complex biological samples, can detect and identify the precise forms of neuropeptides without prior knowledge of peptide identity, with these approaches making up the field of peptidomics (7–12). The direct tissue and single neuron analysis by MALDI MS has enabled the discovery of hundreds of neuropeptides in the last decade, and the neuronal homogenate analysis by fractionation and subsequent ESI or MALDI MS has yielded an equivalent number of new brain peptides (5). Several recent peptidome studies, including the work by Dowell et al. (10), have used the specificity of FTMS for peptide discovery (10, 13–15). Here, we combine the ability to fragment ions at ultrahigh mass accuracy (16) with a software pipeline designed for neuropeptide discovery. We use nanocapillary reversed-phase LC coupled to 12 Tesla FTMS for the analysis of peptides present in the suprachiasmatic nucleus (SCN) of rat brain.A relatively small, paired brain nucleus located at the base of the hypothalamus directly above the optic chiasm, the SCN contains a biological clock that generates circadian rhythms in behaviors and homeostatic functions (17, 18). The SCN comprises ∼10,000 cellular clocks that are integrated as a tissue level clock which, in turn, orchestrates circadian rhythms throughout the brain and body. It is sensitive to incoming signals from the light-sensing retina and other brain regions, which cause temporal adjustments that align the SCN appropriately with changes in environmental or behavioral state. Previous physiological studies have implicated peptides as critical synchronizers of normal SCN function as well as mediators of SCN inputs, internal signal processing, and outputs; however, only a small number of peptides have been identified and explored in the SCN, leaving unresolved many circadian mechanisms that may involve peptide function.Most peptide expression in the SCN has only been studied through indirect antibody-based techniques (19–29), although we recently used MS approaches to characterize several peptides detected in SCN releasates (30). Previous studies indicate that the SCN expresses a rich diversity of peptides relative to other brain regions studied with the same techniques. Previously used immunohistochemical approaches are not only inadequate for comprehensively evaluating PTMs and alternate isoforms of known peptides but are also incapable of exhaustively examining the full peptide complement of this complex biological network of peptidergic inputs and intrinsic components. A comprehensive study of SCN peptidomics is required that utilizes high resolution strategies for directly analyzing the peptide content of the neuronal networks comprising the SCN.In our study, the SCN was obtained from ex vivo coronal brain slices via tissue punch and subjected to multistage peptide extraction. The SCN tissue extract was analyzed by FTMS/MS, and the high resolution MS and MS/MS data were processed using ProSightPC 2.0 (16), which allows the identification and characterization of peptides or proteins from high mass accuracy MS/MS data. In addition, the Sequence Gazer included in ProSightPC was used for manually determining PTMs (31, 32). As a result, a total of 102 endogenous peptides were identified, including 33 that were previously unidentified, and 12 PTMs (including amidation, phosphorylation, pyroglutamylation, and acetylation) were found. The present study is the first comprehensive peptidomics study for identifying peptides present within the mammalian SCN. In fact, this is one of the first peptidome studies to work with discrete brain nuclei as opposed to larger brain structures and follows up on our recent report using LC-ion trap for analysis of the peptides in the supraoptic nucleus (33); here, the use of FTMS allows a greater range of PTMs to be confirmed and allows higher confidence in the peptide assignments. This information on the peptides in the SCN will serve as a basis to more exhaustively explore the extent that previously unreported SCN neuropeptides may function in SCN regulation of mammalian circadian physiology.     

Development of a Novel Cross-linking Strategy for Fast and Accurate Identification of Cross-linked Peptides of Protein Complexes

Knowledge of elaborate structures of protein complexes is fundamental for understanding their functions and regulations. Although cross-linking coupled with mass spectrometry (MS) has been presented as a feasible strategy for structural elucidation of large multisubunit protein complexes, this method has proven challenging because of technical difficulties in unambiguous identification of cross-linked peptides and determination of cross-linked sites by MS analysis. In this work, we developed a novel cross-linking strategy using a newly designed MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). DSSO contains two symmetric collision-induced dissociation (CID)-cleavable sites that allow effective identification of DSSO-cross-linked peptides based on their distinct fragmentation patterns unique to cross-linking types (i.e. interlink, intralink, and dead end). The CID-induced separation of interlinked peptides in MS/MS permits MS³ analysis of single peptide chain fragment ions with defined modifications (due to DSSO remnants) for easy interpretation and unambiguous identification using existing database searching tools. Integration of data analyses from three generated data sets (MS, MS/MS, and MS³) allows high confidence identification of DSSO cross-linked peptides. The efficacy of the newly developed DSSO-based cross-linking strategy was demonstrated using model peptides and proteins. In addition, this method was successfully used for structural characterization of the yeast 20 S proteasome complex. In total, 13 non-redundant interlinked peptides of the 20 S proteasome were identified, representing the first application of an MS-cleavable cross-linker for the characterization of a multisubunit protein complex. Given its effectiveness and simplicity, this cross-linking strategy can find a broad range of applications in elucidating the structural topology of proteins and protein complexes.Proteins form stable and dynamic multisubunit complexes under different physiological conditions to maintain cell viability and normal cell homeostasis. Detailed knowledge of protein interactions and protein complex structures is fundamental to understanding how individual proteins function within a complex and how the complex functions as a whole. However, structural elucidation of large multisubunit protein complexes has been difficult because of a lack of technologies that can effectively handle their dynamic and heterogeneous nature. Traditional methods such as nuclear magnetic resonance (NMR) analysis and x-ray crystallography can yield detailed information on protein structures; however, NMR spectroscopy requires large quantities of pure protein in a specific solvent, whereas x-ray crystallography is often limited by the crystallization process.In recent years, chemical cross-linking coupled with mass spectrometry (MS) has become a powerful method for studying protein interactions (1–3). Chemical cross-linking stabilizes protein interactions through the formation of covalent bonds and allows the detection of stable, weak, and/or transient protein-protein interactions in native cells or tissues (4–9). In addition to capturing protein interacting partners, many studies have shown that chemical cross-linking can yield low resolution structural information about the constraints within a molecule (2, 3, 10) or protein complex (11–13). The application of chemical cross-linking, enzymatic digestion, and subsequent mass spectrometric and computational analyses for the elucidation of three-dimensional protein structures offers distinct advantages over traditional methods because of its speed, sensitivity, and versatility. Identification of cross-linked peptides provides distance constraints that aid in constructing the structural topology of proteins and/or protein complexes. Although this approach has been successful, effective detection and accurate identification of cross-linked peptides as well as unambiguous assignment of cross-linked sites remain extremely challenging due to their low abundance and complicated fragmentation behavior in MS analysis (2, 3, 10, 14). Therefore, new reagents and methods are urgently needed to allow unambiguous identification of cross-linked products and to improve the speed and accuracy of data analysis to facilitate its application in structural elucidation of large protein complexes.A number of approaches have been developed to facilitate MS detection of low abundance cross-linked peptides from complex mixtures. These include selective enrichment using affinity purification with biotinylated cross-linkers (15–17) and click chemistry with alkyne-tagged (18) or azide-tagged (19, 20) cross-linkers. In addition, Staudinger ligation has recently been shown to be effective for selective enrichment of azide-tagged cross-linked peptides (21). Apart from enrichment, detection of cross-linked peptides can be achieved by isotope-labeled (22–24), fluorescently labeled (25), and mass tag-labeled cross-linking reagents (16, 26). These methods can identify cross-linked peptides with MS analysis, but interpretation of the data generated from interlinked peptides (two peptides connected with the cross-link) by automated database searching remains difficult. Several bioinformatics tools have thus been developed to interpret MS/MS data and determine interlinked peptide sequences from complex mixtures (12, 14, 27–32). Although promising, further developments are still needed to make such data analyses as robust and reliable as analyzing MS/MS data of single peptide sequences using existing database searching tools (e.g. Protein Prospector, Mascot, or SEQUEST).Various types of cleavable cross-linkers with distinct chemical properties have been developed to facilitate MS identification and characterization of cross-linked peptides. These include UV photocleavable (33), chemical cleavable (19), isotopically coded cleavable (24), and MS-cleavable reagents (16, 26, 34–38). MS-cleavable cross-linkers have received considerable attention because the resulting cross-linked products can be identified based on their characteristic fragmentation behavior observed during MS analysis. Gas-phase cleavage sites result in the detection of a “reporter” ion (26), single peptide chain fragment ions (35–38), or both reporter and fragment ions (16, 34). In each case, further structural characterization of the peptide product ions generated during the cleavage reaction can be accomplished by subsequent MSⁿ1 analysis. Among these linkers, the “fixed charge” sulfonium ion-containing cross-linker developed by Lu et al. (37) appears to be the most attractive as it allows specific and selective fragmentation of cross-linked peptides regardless of their charge and amino acid composition based on their studies with model peptides.Despite the availability of multiple types of cleavable cross-linkers, most of the applications have been limited to the study of model peptides and single proteins. Additionally, complicated synthesis and fragmentation patterns have impeded most of the known MS-cleavable cross-linkers from wide adaptation by the community. Here we describe the design and characterization of a novel and simple MS-cleavable cross-linker, DSSO, and its application to model peptides and proteins and the yeast 20 S proteasome complex. In combination with new software developed for data integration, we were able to identify DSSO-cross-linked peptides from complex peptide mixtures with speed and accuracy. Given its effectiveness and simplicity, we anticipate a broader application of this MS-cleavable cross-linker in the study of structural topology of other protein complexes using cross-linking and mass spectrometry.     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

Combinatorial Approach for Large-scale Identification of Linked Peptides from Tandem Mass Spectrometry Spectra

The combination of chemical cross-linking and mass spectrometry has recently been shown to constitute a powerful tool for studying protein–protein interactions and elucidating the structure of large protein complexes. However, computational methods for interpreting the complex MS/MS spectra from linked peptides are still in their infancy, making the high-throughput application of this approach largely impractical. Because of the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for the identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to (i) efficiently generate large mass spectral reference data for linked peptides at a low cost and (ii) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we were able to identify thousands of MS/MS spectra from disulfide-bridged peptides through comparison with proteome-scale sequence databases and significantly improve the sensitivity of cross-linked peptide identification. This allowed us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on cross-linking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be valuable resources for the future development of new tools for the identification of linked peptides.The study of protein–protein interactions is crucial to understanding how cellular systems function because proteins act in concert through a highly organized set of interactions. Most cellular processes are carried out by large macromolecular assemblies and regulated through complex cascades of transient protein–protein interactions (1). In the past several years numerous high-throughput studies have pioneered the systematic characterization of protein–protein interactions in model organisms (2–4). Such studies mainly utilize two techniques: the yeast two-hybrid system, which aims at identifying binary interactions (5), and affinity purification combined with tandem mass spectrometry analysis for the identification of multi-protein assemblies (6–8). Together these led to a rapid expansion of known protein–protein interactions in human and other model organisms. Patche and Aloy recently estimated that there are more than one million interactions catalogued to date (9).But despite rapid progress, most current techniques allow one to determine only whether proteins interact, which is only the first step toward understanding how proteins interact. A more complete picture comes from characterizing the three-dimensional structures of protein complexes, which provide mechanistic insights that govern how interactions occur and the high specificity observed inside the cell. Traditionally the gold-standard methods used to solve protein structures are x-ray crystallography and NMR, and there have been several efforts similar to structural genomics (10) aiming to comprehensively solve the structures of protein complexes (11, 12). Although there has been accelerated growth of structures for protein monomers in the Protein Data Bank in recent years (11), the growth of structures for protein complexes has remained relatively small (9). Many factors, including their large size, transient nature, and dynamics of interactions, have prevented many complexes from being solved via traditional approaches in structural biology. Thus, the development of complementary analytical techniques with which to probe the structure of large protein complexes continues to evolve (13–18).Recent developments have advanced the analysis of protein structures and interaction by combining cross-linking and tandem mass spectrometry (17, 19–24). The basic idea behind this technique is to capture and identify pairs of amino acid residues that are spatially close to each other. When these linked pairs of residues are from the same protein (intraprotein cross-links), they provide distance constraints that help one infer the possible conformations of protein structures. Conversely, when pairs of residues come from different proteins (interprotein cross-links), they provide information about how proteins interact with one another. Although cross-linking strategies date back almost a decade (25, 26), difficulty in analyzing the complex MS/MS spectrum generated from linked peptides made this approach challenging, and therefore it was not widely used. With recent advances in mass spectrometry instrumentation, there has been renewed interest in employing this strategy to determine protein structures and identify protein–protein interactions. However, most studies thus far have been focused on purified protein complexes. With today''s mass spectrometers being capable of analyzing tens of thousands of spectra in a single experiment, it is now potentially feasible to extend this approach to the analysis of complex biological samples. Researchers have tried to realize this goal using both experimental and computational approaches. Indeed, a plethora of chemical cross-linking reagents are now available for stabilizing these complexes, and some are designed to allow for easier peptide identification when employed in concert with MS analysis (20, 27, 28). There have also been several recent efforts to develop computational methods for the automatic identification of linked peptides from MS/MS spectra (29–36). However, because of the lack of large annotated training data, most approaches to date either borrow fragmentation models learned from unlinked, linear peptides or learn the fragmentation statistics from training data of limited size (30, 37), which might not generalize well across different samples. In some cases it is possible to generate relatively large training data, but it is often very labor intensive and involves hundreds of separate LC-MS/MS runs (36). Here, employing disulfide-bridged peptides as an example, we propose a novel method that uses a combinatorial peptide library to (a) efficiently generate a large mass spectral reference dataset for linked peptides and (b) use these data to automatically train our new algorithm, MXDB, which can efficiently and accurately identify linked peptides from MS/MS spectra.     

JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from “mixture spectra” to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.Peptide identification by tandem mass spectra is a critical step in mass spectrometry (MS)-based¹ proteomics (1). Numerous computational algorithms and software tools have been developed for this purpose (2–6). These algorithms can be classified into three categories: (i) pattern-based database search, (ii) de novo sequencing, and (iii) hybrid search that combines database search and de novo sequencing. With the continuous development of high-performance liquid chromatography and high-resolution mass spectrometers, it is now possible to analyze almost all protein components in mammalian cells (7). In contrast to rapid data collection, it remains a challenge to extract accurate information from the raw data to identify peptides with low false positive rates (specificity) and minimal false negatives (sensitivity) (8).Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS² (19), and Morpheus (20). Some other programs, such as SpectraST (21) and Pepitome (22), utilize a spectral library composed of experimentally identified and validated MS/MS spectra. These methods use a variety of scoring algorithms to rank potential peptide spectrum matches (PSMs) and select the top hit as a putative PSM. However, not all PSMs are correctly assigned. For example, false peptides may be assigned to MS/MS spectra with numerous noisy peaks and poor fragmentation patterns. If the samples contain unknown protein modifications, mutations, and contaminants, the related MS/MS spectra also result in false positives, as their corresponding peptides are not in the database. Other false positives may be generated simply by random matches. Therefore, it is of importance to remove these false PSMs to improve dataset quality. One common approach is to filter putative PSMs to achieve a final list with a predefined false discovery rate (FDR) via a target-decoy strategy, in which decoy proteins are merged with target proteins in the same database for estimating false PSMs (23–26). However, the true and false PSMs are not always distinguishable based on matching scores. It is a problem to set up an appropriate score threshold to achieve maximal sensitivity and high specificity (13, 27, 28).De novo methods, including Lutefisk (29), PEAKS (30), NovoHMM (31), PepNovo (32), pNovo (33), Vonovo (34), and UniNovo (35), identify peptide sequences directly from MS/MS spectra. These methods can be used to derive novel peptides and post-translational modifications without a database, which is useful, especially when the related genome is not sequenced. High-resolution MS/MS spectra greatly facilitate the generation of peptide sequences in these de novo methods. However, because MS/MS fragmentation cannot always produce all predicted product ions, only a portion of collected MS/MS spectra have sufficient quality to extract partial or full peptide sequences, leading to lower sensitivity than achieved with the database search methods.To improve the sensitivity of the de novo methods, a hybrid approach has been proposed to integrate peptide sequence tags into PSM scoring during database searches (36). Numerous software packages have been developed, such as GutenTag (37), InsPecT (38), Byonic (39), DirecTag (40), and PEAKS DB (41). These methods use peptide tag sequences to filter a protein database, followed by error-tolerant database searching. One restriction in most of these algorithms is the requirement of a minimum tag length of three amino acids for matching protein sequences in the database. This restriction reduces the sensitivity of the database search, because it filters out some high-quality spectra in which consecutive tags cannot be generated.In this paper, we describe JUMP, a novel tag-based hybrid algorithm for peptide identification. The program is optimized to balance sensitivity and specificity during tag derivation and MS/MS pattern matching. JUMP can use all potential sequence tags, including tags consisting of only one amino acid. When we compared its performance to that of two widely used search algorithms, SEQUEST and Mascot, JUMP identified ∼30% more PSMs at the same FDR threshold. In addition, the program provides two additional features: (i) using tag sequences to improve modification site assignment, and (ii) analyzing co-fragmented peptides from mixture MS/MS spectra.     

DeMix Workflow for Efficient Identification of Cofragmented Peptides in High Resolution Data-dependent Tandem Mass Spectrometry

Based on conventional data-dependent acquisition strategy of shotgun proteomics, we present a new workflow DeMix, which significantly increases the efficiency of peptide identification for in-depth shotgun analysis of complex proteomes. Capitalizing on the high resolution and mass accuracy of Orbitrap-based tandem mass spectrometry, we developed a simple deconvolution method of “cloning” chimeric tandem spectra for cofragmented peptides. Additional to a database search, a simple rescoring scheme utilizes mass accuracy and converts the unwanted cofragmenting events into a surprising advantage of multiplexing. With the combination of cloning and rescoring, we obtained on average nine peptide-spectrum matches per second on a Q-Exactive workbench, whereas the actual MS/MS acquisition rate was close to seven spectra per second. This efficiency boost to 1.24 identified peptides per MS/MS spectrum enabled analysis of over 5000 human proteins in single-dimensional LC-MS/MS shotgun experiments with an only two-hour gradient. These findings suggest a change in the dominant “one MS/MS spectrum - one peptide” paradigm for data acquisition and analysis in shotgun data-dependent proteomics. DeMix also demonstrated higher robustness than conventional approaches in terms of lower variation among the results of consecutive LC-MS/MS runs.Shotgun proteomics analysis based on a combination of high performance liquid chromatography and tandem mass spectrometry (MS/MS) (1) has achieved remarkable speed and efficiency (2–7). In a single four-hour long high performance liquid chromatography-MS/MS run, over 40,000 peptides and 5000 proteins can be identified using a high-resolution Orbitrap mass spectrometer with data-dependent acquisition (DDA)¹ (2, 3). However, in a typical LC-MS analysis of unfractionated human cell lysate, over 100,000 individual peptide isotopic patterns can be detected (4), which corresponds to simultaneous elution of hundreds of peptides. With this complexity, a mass spectrometer needs to achieve ≥25 Hz MS/MS acquisition rate to fully sample all the detectable peptides, and ≥17 Hz to cover reasonably abundant ones (4). Although this acquisition rate is reachable by modern time-of-flight (TOF) instruments, the reported DDA identification results do not encompass all expected peptides. Recently, the next-generation Orbitrap instrument, working at 20 Hz MS/MS acquisition rate, demonstrated nearly full profiling of yeast proteome using an 80 min gradient, which opened the way for comprehensive analysis of human proteome in a time efficient manner (5).During the high performance liquid chromatography-MS/MS DDA analysis of complex samples, high density of co-eluting peptides results in a high probability for two or more peptides to overlap within an MS/MS isolation window. With the commonly used ±1.0–2.0 Th isolation windows, most MS/MS spectra are chimeric (4, 8–10), with cofragmenting precursors being naturally multiplexed. However, as has been discussed previously (9, 10), the cofragmentation events are currently ignored in most of the conventional analysis workflows. According to the prevailing assumption of “one MS/MS spectrum–one peptide,” chimeric MS/MS spectra are generally unwelcome in DDA, because the product ions from different precursors may interfere with the assignment of MS/MS fragment identities, increasing the rate of false discoveries in database search (8, 9). In some studies, the precursor isolation width was set as narrow as ±0.35 Th to prevent unwanted ions from being coselected, fragmented or detected (4, 5).On the contrary, multiplexing by cofragmentation is considered to be one of the solid advantages in data-independent acquisition (DIA) (10–13). In several commonly used DIA methods, the precursor ion selection windows are set much wider than in DDA: from 25 Th as in SWATH (12), to extremely broad range as in AIF (13). In order to use the benefit of MS/MS multiplexing in DDA, several approaches have been proposed to deconvolute chimeric MS/MS spectra. In “alternative peptide identification” method implemented in Percolator (14), a machine learning algorithm reranks and rescores peptide-spectrum matches (PSMs) obtained from one or more MS/MS search engines. But the deconvolution in Percolator is limited to cofragmented peptides with masses differing from the target peptide by the tolerance of the database search, which can be as narrow as a few ppm. The “active demultiplexing” method proposed by Ledvina et al. (15) actively separates MS/MS data from several precursors using masses of complementary fragments. However, higher-energy collisional dissociation often produces MS/MS spectra with too few complementary pairs for reliable peptide identification. The “MixDB” method introduces a sophisticated new search engine, also with a machine learning algorithm (9). And the “second peptide identification” method implemented in Andromeda/MaxQuant workflow (16) submits the same dataset to the search engine several times based on the list of chromatographic peptide features, subtracting assigned MS/MS peaks after each identification round. This approach is similar to the ProbIDTree search engine that also performed iterative identification while removing assigned peaks after each round of identification (17).One important factor for spectral deconvolution that has not been fully utilized in most conventional workflows is the excellent mass accuracy achievable with modern high-resolution mass spectrometry (18). An Orbitrap Fourier-transform mass spectrometer can provide mass accuracy in the range of hundreds of ppb (parts per billion) for mass peaks with high signal-to-noise (S/N) ratio (19). However, the mass error of peaks with lower S/N ratios can be significantly higher and exceed 1 ppm. Despite this dependence of the mass accuracy from the S/N level, most MS and MS/MS search engines only allow users to set hard cut-off values for the mass error tolerances. Moreover, some search engines do not provide the option of choosing a relative error tolerance for MS/MS fragments. Such negligent treatment of mass accuracy reduces the analytical power of high accuracy experiments (18).Identification results coming from different MS/MS search engines are sometimes not consistent because of different statistical assumptions used in scoring PSMs. Introduction of tools integrating the results of different search engines (14, 20, 21) makes the data interpretation even more complex and opaque for the user. The opposite trend—simplification of MS/MS data interpretation—is therefore a welcome development. For example, an extremely straightforward algorithm recently proposed by Wenger et al. (22) demonstrated a surprisingly high performance in peptide identification, even though it is only marginally more complex than simply counting the number of matches of theoretical fragment peaks in high resolution MS/MS, without any a priori statistical assumption.In order to take advantage of natural multiplexing of MS/MS spectra in DDA, as well as properly utilize high accuracy of Orbitrap-based mass spectrometry, we developed a simple and robust data analysis workflow DeMix. It is presented in Fig. 1 as an expansion of the conventional workflow. Principles of some of the processes used by the workflow are borrowed from other approaches, including the custom-made mass peak centroiding (20), chromatographic feature detection (19, 20), and two-pass database search with the first limited pass to provide a “software lock mass” for mass scale recalibration (23).Open in a separate windowFig. 1.An overview of the DeMix workflow that expands the conventional workflow, shown by the dashed line. Processes are colored in purple for TOPP, red for search engine (Morpheus/Mascot/MS-GF+), and blue for in-house programs.In DeMix workflow, the deconvolution of chimeric MS/MS spectra consists of simply “cloning” an MS/MS spectrum if a potential cofragmented peptide is detected. The list of candidate peptide precursors is generated from chromatographic feature detection, as in the MaxQuant/Andromeda workflow (16, 19), but using The OpenMS Proteomics Pipeline (TOPP) (20, 24). During the cloning, the precursor is replaced by the new candidate, but no changes in the MS/MS fragment list are made, and therefore the cloned MS/MS spectra remain chimeric. Processing such spectra requires a search engine tolerant to the presence of unassigned peaks, as such peaks are always expected when multiple precursors cofragment. Thus, we chose Morpheus (22) as a search engine. Based on the original search algorithm, we implement a reformed scoring scheme: Morpheus-AS (advanced scoring). It inherits all the basic principles from Morpheus but deeper utilizes the high mass accuracy of the data. This kind of database search removes the necessity of spectral processing for physical separation of MS/MS data into multiple subspectra (15), or consecutive subtraction of peaks (16, 17).Despite the fact that DeMix workflow is largely a combination of known approaches, it provides remarkable improvement compared with the state-of-the-art. On our Orbitrap Q-Exactive workbench, testing on a benchmark dataset of two-hour single-dimension LC-MS/MS experiments from HeLa cell lysate, we identified on average 1.24 peptide per MS/MS spectrum, breaking the “one MS/MS spectrum–one peptide” paradigm on the level of whole data set. At 1% false discovery rate (FDR), we obtained on average nine PSMs per second (at the actual acquisition rate of ca. seven MS/MS spectra per second), and detected 40 human proteins per minute.     

A Double-Barrel Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) System to Quantify 96 Interactomes per Day

The field of proteomics has evolved hand-in-hand with technological advances in LC-MS/MS systems, now enabling the analysis of very deep proteomes in a reasonable time. However, most applications do not deal with full cell or tissue proteomes but rather with restricted subproteomes relevant for the research context at hand or resulting from extensive fractionation. At the same time, investigation of many conditions or perturbations puts a strain on measurement capacity. Here, we develop a high-throughput workflow capable of dealing with large numbers of low or medium complexity samples and specifically aim at the analysis of 96-well plates in a single day (15 min per sample). We combine parallel sample processing with a modified liquid chromatography platform driving two analytical columns in tandem, which are coupled to a quadrupole Orbitrap mass spectrometer (Q Exactive HF). The modified LC platform eliminates idle time between measurements, and the high sequencing speed of the Q Exactive HF reduces required measurement time. We apply the pipeline to the yeast chromatin remodeling landscape and demonstrate quantification of 96 pull-downs of chromatin complexes in about 1 day. This is achieved with only 500 μg input material, enabling yeast cultivation in a 96-well format. Our system retrieved known complex-members and the high throughput allowed probing with many bait proteins. Even alternative complex compositions were detectable in these very short gradients. Thus, sample throughput, sensitivity and LC/MS-MS duty cycle are improved severalfold compared with established workflows. The pipeline can be extended to different types of interaction studies and to other medium complexity proteomes.Shotgun proteomics is concerned with the identification and quantification of proteins (1–3). Prior to analysis, the proteins are digested into peptides, resulting in highly complex mixtures. To deal with this complexity, the peptides are separated by liquid chromatography followed by online analysis with mass spectrometry (MS), today facilitating the characterization of almost complete cell line proteomes in a short time (3–5). In addition to the characterization of entire proteomes, there is also a great demand for analyzing low or medium complexity samples. Given the trend toward a systems biology view, relatively larges sets of samples often have to be measured. One such category of lower complexity protein mixtures occurs in the determination of physical interaction partners of a protein of interest, which requires the identification and quantification of the proteins “pulled-down” or immunoprecipitated via a bait protein. Protein interactions are essential for almost all biological processes and orchestrate a cell''s behavior by regulating enzymes, forming macromolecular assemblies and functionalizing multiprotein complexes that are capable of more complex behavior than the sum of their parts. The human genome has almost 20,000 protein encoding genes, and it has been estimated that 80% of the proteins engage in complex interactions and that 130,000 to 650,000 protein interactions can take place in a human cell (6, 7). These numbers demonstrate a clear need for systematic and high-throughput mapping of protein–protein interactions (PPIs) to understand these complexes.The introduction of generic methods to detect PPIs, such as the yeast two-hybrid screen (Y2H) (8) or affinity purification combined with mass spectrometry (AP-MS)¹ (9), have revolutionized the protein interactomics field. AP-MS in particular has emerged as an important tool to catalogue interactions with the aim of better understanding basic biochemical mechanisms in many different organisms (10–17). It can be performed under near-physiological conditions and is capable of identifying functional protein complexes (18). In addition, the combination of affinity purification with quantitative mass spectrometry has greatly improved the discrimination of true interactors from unspecific background binders, a long-standing challenge in the AP-MS field (19–21). Nowadays, quantitative AP-MS is employed to address many different biological questions, such as detection of dynamic changes in PPIs upon perturbation (22–25) or the impact of posttranslational signaling on PPIs (26, 27). Recent developments even make it possible to provide abundances and stoichiometry information of the bait and prey proteins under study, combined with quantitative data from very deep cellular proteomes. Furthermore, sample preparation in AP-MS can now be performed in high-throughput formats capable of producing hundreds of samples per day. With such throughput in sample generation, the LC-MS/MS part of the AP-MS pipeline has become a major bottleneck for large studies, limiting throughput to a small fraction of the available samples. In principle, this limitation could be circumvented by multiplexing analysis via isotope-labeling strategies (28, 29) or by drastically reducing the measurement time per sample (30–32). The former strategy requires exquisite control of the processing steps and has not been widely implemented yet. The latter strategy depends on mass spectrometers with sufficiently high sequencing speed to deal with the pull-down in a very short time. Since its introduction about 10 years ago (33), the Orbitrap mass spectrometer has featured ever-faster sequencing capabilities, with the Q Exactive HF now reaching a peptide sequencing speed of up to 17 Hz (34). This should now make it feasible to substantially lower the amount of time spent per measurement.Although very short LC-MS/MS runs can in principle be used for high-throughput analyses, they usually lead to a drop in LC-MS duty cycle. This is because each sample needs initial washing, loading, and equilibration steps, independent of gradient time, which takes a substantial percentage for most LC setups - typically at least 15–20 min. To achieve a more efficient LC-MS duty cycle, while maintaining high sensitivity, a second analytical column can be introduced. This enables the parallelization of several steps related to sample loading and to the LC operating steps, including valve switching. Such dual analytical column or “double-barrel: setups have been described for various applications and platforms (30, 35–39).Starting from the reported performance and throughput of workflows that are standard today (16, 21, 40–42), we asked if it would be possible to obtain a severalfold increase in both sample throughput and sensitivity, as well as a considerable reduction in overall wet lab costs and working time. Specifically, our goal was to quantify 96 medium complexity samples in a single day. Such a number of samples can be processed with a 96-well plate, which currently is the format of choice for highly parallelized sample preparation workflows, often with a high degree of automation. We investigated which advances were needed in sample preparation, liquid chromatography, and mass spectrometry. Based on our findings, we developed a parallelized platform for high-throughput sample preparation and LC-MS/MS analysis, which we applied to pull-down samples from the yeast chromatin remodeling landscape. The extent of retrieval of known complex members served as a quality control of the developed pipeline.     

In-depth Qualitative and Quantitative Profiling of Tyrosine Phosphorylation Using a Combination of Phosphopeptide Immunoaffinity Purification and Stable Isotope Dimethyl Labeling

Several mass spectrometry-based assays have emerged for the quantitative profiling of cellular tyrosine phosphorylation. Ideally, these methods should reveal the exact sites of tyrosine phosphorylation, be quantitative, and not be cost-prohibitive. The latter is often an issue as typically several milligrams of (stable isotope-labeled) starting protein material are required to enable the detection of low abundance phosphotyrosine peptides. Here, we adopted and refined a peptidecentric immunoaffinity purification approach for the quantitative analysis of tyrosine phosphorylation by combining it with a cost-effective stable isotope dimethyl labeling method. We were able to identify by mass spectrometry, using just two LC-MS/MS runs, more than 1100 unique non-redundant phosphopeptides in HeLa cells from about 4 mg of starting material without requiring any further affinity enrichment as close to 80% of the identified peptides were tyrosine phosphorylated peptides. Stable isotope dimethyl labeling could be incorporated prior to the immunoaffinity purification, even for the large quantities (mg) of peptide material used, enabling the quantification of differences in tyrosine phosphorylation upon pervanadate treatment or epidermal growth factor stimulation. Analysis of the epidermal growth factor-stimulated HeLa cells, a frequently used model system for tyrosine phosphorylation, resulted in the quantification of 73 regulated unique phosphotyrosine peptides. The quantitative data were found to be exceptionally consistent with the literature, evidencing that such a targeted quantitative phosphoproteomics approach can provide reproducible results. In general, the combination of immunoaffinity purification of tyrosine phosphorylated peptides with large scale stable isotope dimethyl labeling provides a cost-effective approach that can alleviate variation in sample preparation and analysis as samples can be combined early on. Using this approach, a rather complete qualitative and quantitative picture of tyrosine phosphorylation signaling events can be generated.Reversible tyrosine phosphorylation plays an important role in numerous cellular processes like growth, differentiation, and migration. Phosphotyrosine signaling is tightly controlled by the balanced action of protein-tyrosine kinases and protein-tyrosine phosphatases. Aberrant tyrosine phosphorylation has been suggested to be an underlying cause in multiple cancers (1). Therefore, the identification of tyrosine phosphorylated proteins and the investigation into their involvement in signaling pathways are important. Several groups have attempted to comprehensively study tyrosine phosphorylation by proteomics means (2–5). However, large scale identification of tyrosine phosphorylation sites by MS can be hindered by the low abundance of tyrosine phosphorylated proteins. Especially, signaling intermediates are usually low abundance proteins that show substoichiometric phosphorylation levels. In addition, the identification by mass spectrometry of phosphopeptides from a complex cellular lysate digest is often complicated by ion suppression effects due to a high background of non-phosphorylated peptides. Enrichment of tyrosine phosphorylated proteins or peptides prior to mass spectrometric detection is therefore essential. Traditionally, antibodies against phosphorylated tyrosine have been used to immunoprecipitate tyrosine phosphorylated proteins from cultured cells (2–4, 6–8). This phosphoprotein immunoaffinity purification method has for example been used to study the global dynamics of phosphotyrosine signaling events after EGF¹ stimulation using stable isotope labeling by amino acids in cell culture (SILAC) (2). This approach led to the identification of known and previously unidentified signaling proteins in the EGF receptor (EGFR) pathway, including their temporal activation profile after stimulation of the EGFR, providing crucial information for modeling signaling events in the cell. However, as the identification and quantification of these phosphorylated proteins in these studies were not necessarily based on tyrosine phosphorylated peptides but largely on non-phosphorylated peptides, little information is derived on the exact site(s) of tyrosine phosphorylation. Also, binding partners of tyrosine phosphorylated proteins, which themselves are not tyrosine phosphorylated, might be co-precipitated and impair the tyrosine phosphorylation quantification. Immunoaffinity purification of phosphotyrosine peptides, rather than proteins, using anti-phosphotyrosine antibodies (5, 9–16) significantly facilitates the identification of the site(s) of phosphorylation as it greatly alleviates most of the above mentioned problems because the tyrosine phosphorylated site can be directly identified and quantified.Accurate MS-based quantification is typically performed by stable isotope labeling. The isotopes can be incorporated metabolically during cell culture as in SILAC (17) or chemically as in an isobaric tag for relative and absolute quantitation (iTRAQ) (18) or stable isotope dimethyl labeling (19–21). Typically, the most precise quantification can be obtained by metabolic labeling as the different samples can be combined at the level of intact cells (22). However, metabolic labeling is somewhat limited to biological systems that can be grown in culture, and the medium may have an effect on the growth and development of the cells. iTRAQ has been used in conjunction with phosphotyrosine peptide immunoprecipitation (5). As the chemical labeling is performed before immunoprecipitation, the differentially labeled samples can be precipitated together, thereby neutralizing the potentially largest source of variation. However, as this phosphotyrosine peptide immunoprecipitation is typically performed on several hundreds of micrograms to milligrams of protein sample, iTRAQ provides in these cases a rather cost-prohibitive means.Here, we present an optimized immunoaffinity purification approach for the analysis of tyrosine phosphorylation combined with stable isotope dimethyl labeling (19–21, 23). We efficiently enriched and identified by MS 1112 unique phosphopeptides derived from 4 mg of starting protein material without any further affinity chromatographic enrichment whereby up to 80% of the peptides analyzed in the final LC run were phosphotyrosine peptides. We further advanced the method by introducing triplex stable isotope dimethyl labeling prior to immunoprecipitation. We quantified differences in tyrosine phosphorylation upon pervanadate treatment or EGF stimulation to detect site-specific changes in tyrosine phosphorylation. 128 unique phosphotyrosine peptides were identified and quantified upon pervanadate treatment. By using an internal standard comprising both mock and pervanadate-treated samples, we could more confidently identify and quantify phosphorylation sites that are strongly regulated and on-off situations. Analysis of EGF-stimulated HeLa cells resulted in the quantification of 73 unique phosphotyrosine peptides. Most of the up-regulated phosphotyrosine peptides that were identified have been reported previously to be involved in the EGFR signaling pathway, validating our approach. However, for the first time, we found TFG to also become highly tyrosine phosphorylated upon EGF stimulation together with some tyrosine phosphorylation sites on for example IRS2, SgK269, and DLG3 that have not been firmly established earlier to be involved in EGFR signaling.In general, we show that the combination of immunoaffinity purification of tyrosine phosphorylated peptides with large scale chemical stable isotope dimethyl labeling provides a cost-effective approach that can alleviate variation in immunoprecipitation and LC-MS as samples can be combined before immunoprecipitation and the necessity of performing additional enrichment is removed by an optimization of the protocol. With only a single LC-MS run, already a rather complete qualitative and quantitative picture of a signaling event can be generated.     

Printor, a Novel TorsinA-interacting Protein Implicated in Dystonia Pathogenesis

Early onset generalized dystonia (DYT1) is an autosomal dominant neurological disorder caused by deletion of a single glutamate residue (torsinA ΔE) in the C-terminal region of the AAA⁺ (ATPases associated with a variety of cellular activities) protein torsinA. The pathogenic mechanism by which torsinA ΔE mutation leads to dystonia remains unknown. Here we report the identification and characterization of a 628-amino acid novel protein, printor, that interacts with torsinA. Printor co-distributes with torsinA in multiple brain regions and co-localizes with torsinA in the endoplasmic reticulum. Interestingly, printor selectively binds to the ATP-free form but not to the ATP-bound form of torsinA, supporting a role for printor as a cofactor rather than a substrate of torsinA. The interaction of printor with torsinA is completely abolished by the dystonia-associated torsinA ΔE mutation. Our findings suggest that printor is a new component of the DYT1 pathogenic pathway and provide a potential molecular target for therapeutic intervention in dystonia.Early onset generalized torsion dystonia (DYT1) is the most common and severe form of hereditary dystonia, a movement disorder characterized by involuntary movements and sustained muscle spasms (1). This autosomal dominant disease has childhood onset and its dystonic symptoms are thought to result from neuronal dysfunction rather than neurodegeneration (2, 3). Most DYT1 cases are caused by deletion of a single glutamate residue at positions 302 or 303 (torsinA ΔE) of the 332-amino acid protein torsinA (4). In addition, a different torsinA mutation that deletes amino acids Phe³²³–Tyr³²⁸ (torsinA Δ323–328) was identified in a single family with dystonia (5), although the pathogenic significance of this torsinA mutation is unclear because these patients contain a concomitant mutation in another dystonia-related protein, ϵ-sarcoglycan (6). Recently, genetic association studies have implicated polymorphisms in the torsinA gene as a genetic risk factor in the development of adult-onset idiopathic dystonia (7, 8).TorsinA contains an N-terminal endoplasmic reticulum (ER)³ signal sequence and a 20-amino acid hydrophobic region followed by a conserved AAA⁺ (ATPases associated with a variety of cellular activities) domain (9, 10). Because members of the AAA⁺ family are known to facilitate conformational changes in target proteins (11, 12), it has been proposed that torsinA may function as a molecular chaperone (13, 14). TorsinA is widely expressed in brain and multiple other tissues (15) and is primarily associated with the ER and nuclear envelope (NE) compartments in cells (16–20). TorsinA is believed to mainly reside in the lumen of the ER and NE (17–19) and has been shown to bind lamina-associated polypeptide 1 (LAP1) (21), lumenal domain-like LAP1 (LULL1) (21), and nesprins (22). In addition, recent evidence indicates that a significant pool of torsinA exhibits a topology in which the AAA⁺ domain faces the cytoplasm (20). In support of this topology, torsinA is found in the cytoplasm, neuronal processes, and synaptic terminals (2, 3, 15, 23–26) and has been shown to bind cytosolic proteins snapin (27) and kinesin light chain 1 (20). TorsinA has been proposed to play a role in several cellular processes, including dopaminergic neurotransmission (28–31), NE organization and dynamics (17, 22, 32), and protein trafficking (27, 33). However, the precise biological function of torsinA and its regulation remain unknown.To gain insights into torsinA function, we performed yeast two-hybrid screens to search for torsinA-interacting proteins in the brain. We report here the isolation and characterization of a novel protein named printor (protein interactor of torsinA) that interacts selectively with wild-type (WT) torsinA but not the dystonia-associated torsinA ΔE mutant. Our data suggest that printor may serve as a cofactor of torsinA and provide a new molecular target for understanding and treating dystonia.     

Peptide Identification from Mixture Tandem Mass Spectra

The success of high-throughput proteomics hinges on the ability of computational methods to identify peptides from tandem mass spectra (MS/MS). However, a common limitation of most peptide identification approaches is the nearly ubiquitous assumption that each MS/MS spectrum is generated from a single peptide. We propose a new computational approach for the identification of mixture spectra generated from more than one peptide. Capitalizing on the growing availability of large libraries of single-peptide spectra (spectral libraries), our quantitative approach is able to identify up to 98% of all mixture spectra from equally abundant peptides and automatically adjust to varying abundance ratios of up to 10:1. Furthermore, we show how theoretical bounds on spectral similarity avoid the need to compare each experimental spectrum against all possible combinations of candidate peptides (achieving speedups of over five orders of magnitude) and demonstrate that mixture-spectra can be identified in a matter of seconds against proteome-scale spectral libraries. Although our approach was developed for and is demonstrated on peptide spectra, we argue that the generality of the methods allows for their direct application to other types of spectral libraries and mixture spectra.The success of tandem MS (MS/MS¹) approaches to peptide identification is partly due to advances in computational techniques allowing for the reliable interpretation of MS/MS spectra. Mainstream computational techniques mainly fall into two categories: database search approaches that score each spectrum against peptides in a sequence database (1–4) or de novo techniques that directly reconstruct the peptide sequence from each spectrum (5–8). The combination of these methods with advances in high-throughput MS/MS have promoted the accelerated growth of spectral libraries, collections of peptide MS/MS spectra the identification of which were validated by accepted statistical methods (9, 10) and often also manually confirmed by mass spectrometry experts. The similar concept of spectral archives was also recently proposed to denote spectral libraries including “interesting” nonidentified spectra (11) (i.e. recurring spectra with good de novo reconstructions but no database match). The growing availability of these large collections of MS/MS spectra has reignited the development of alternative peptide identification approaches based on spectral matching (12–14) and alignment (15–17) algorithms.However, mainstream approaches were developed under the (often unstated) assumption that each MS/MS spectrum is generated from a single peptide. Although chromatographic procedures greatly contribute to making this a reasonable assumption, there are several situations where it is difficult or even impossible to separate pairs of peptides. Examples include certain permutations of the peptide sequence or post-translational modifications (see (18) for examples of co-eluting histone modification variants). In addition, innovative experimental setups have demonstrated the potential for increased throughput in peptide identification using mixture spectra; examples include data-independent acquisition (19) ion-mobility MS (20), and MS^E strategies (21).To alleviate the algorithmic bottleneck in such scenarios, we describe a computational approach, M-SPLIT (mixture-spectrum partitioning using library of identified tandem mass spectra), that is able to reliably and efficiently identify peptides from mixture spectra, which are generated from a pair of peptides. In brief, a mixture spectrum is modeled as linear combination of two single-peptide spectra, and peptide identification is done by searching against a spectral library. We show that efficient filtration and accurate branch-and-bound strategies can be used to avoid the huge computational cost of searching all possible pairs. Thus equipped, our approach is able to identify the correct matches by considering only a minuscule fraction of all possible matches. Beyond potentially enhancing the identification capabilities of current MS/MS acquisition setups, we argue that the availability of methods to reliably identify MS/MS spectra from mixtures of peptides could enable the collection of MS/MS data using accelerated chromatography setups to obtain the same or better peptide identification results in a fraction of the experimental time currently required for exhaustive peptide separation.     

A New in Vivo Cross-linking Mass Spectrometry Platform to Define Protein–Protein Interactions in Living Cells

Protein–protein interactions (PPIs) are fundamental to the structure and function of protein complexes. Resolving the physical contacts between proteins as they occur in cells is critical to uncovering the molecular details underlying various cellular activities. To advance the study of PPIs in living cells, we have developed a new in vivo cross-linking mass spectrometry platform that couples a novel membrane-permeable, enrichable, and MS-cleavable cross-linker with multistage tandem mass spectrometry. This strategy permits the effective capture, enrichment, and identification of in vivo cross-linked products from mammalian cells and thus enables the determination of protein interaction interfaces. The utility of the developed method has been demonstrated by profiling PPIs in mammalian cells at the proteome scale and the targeted protein complex level. Our work represents a general approach for studying in vivo PPIs and provides a solid foundation for future studies toward the complete mapping of PPI networks in living systems.Protein–protein interactions (PPIs)¹ play a key role in defining protein functions in biological systems. Aberrant PPIs can have drastic effects on biochemical activities essential to cell homeostasis, growth, and proliferation, and thereby lead to various human diseases (1). Consequently, PPI interfaces have been recognized as a new paradigm for drug development. Therefore, mapping PPIs and their interaction interfaces in living cells is critical not only for a comprehensive understanding of protein function and regulation, but also for describing the molecular mechanisms underlying human pathologies and identifying potential targets for better therapeutics.Several strategies exist for identifying and mapping PPIs, including yeast two-hybrid, protein microarray, and affinity purification mass spectrometry (AP-MS) (2–5). Thanks to new developments in sample preparation strategies, mass spectrometry technologies, and bioinformatics tools, AP-MS has become a powerful and preferred method for studying PPIs at the systems level (6–9). Unlike other approaches, AP-MS experiments allow the capture of protein interactions directly from their natural cellular environment, thus better retaining native protein structures and biologically relevant interactions. In addition, a broader scope of PPI networks can be obtained with greater sensitivity, accuracy, versatility, and speed. Despite the success of this very promising technique, AP-MS experiments can lead to the loss of weak/transient interactions and/or the reorganization of protein interactions during biochemical manipulation under native purification conditions. To circumvent these problems, in vivo chemical cross-linking has been successfully employed to stabilize protein interactions in native cells or tissues prior to cell lysis (10–16). The resulting covalent bonds formed between interacting partners allow affinity purification under stringent and fully denaturing conditions, consequently reducing nonspecific background while preserving stable and weak/transient interactions (12–16). Subsequent mass spectrometric analysis can reveal not only the identities of interacting proteins, but also cross-linked amino acid residues. The latter provides direct molecular evidence describing the physical contacts between and within proteins (17). This information can be used for computational modeling to establish structural topologies of proteins and protein complexes (17–22), as well as for generating experimentally derived protein interaction network topology maps (23, 24). Thus, cross-linking mass spectrometry (XL-MS) strategies represent a powerful and emergent technology that possesses unparalleled capabilities for studying PPIs.Despite their great potential, current XL-MS studies that have aimed to identify cross-linked peptides have been mostly limited to in vitro cross-linking experiments, with few successfully identifying protein interaction interfaces in living cells (24, 25). This is largely because XL-MS studies remain challenging due to the inherent difficulty in the effective MS detection and accurate identification of cross-linked peptides, as well as in unambiguous assignment of cross-linked residues. In general, cross-linked products are heterogeneous and low in abundance relative to non-cross-linked products. In addition, their MS fragmentation is too complex to be interpreted using conventional database searching tools (17, 26). It is noted that almost all of the current in vivo PPI studies utilize formaldehyde cross-linking because of its membrane permeability and fast kinetics (10–16). However, in comparison to the most commonly used amine reactive NHS ester cross-linkers, identification of formaldehyde cross-linked peptides is even more challenging because of its promiscuous nonspecific reactivity and extremely short spacer length (27). Therefore, further developments in reagents and methods are urgently needed to enable simple MS detection and effective identification of in vivo cross-linked products, and thus allow the mapping of authentic protein contact sites as established in cells, especially for protein complexes.Various efforts have been made to address the limitations of XL-MS studies, resulting in new developments in bioinformatics tools for improved data interpretation (28–32) and new designs of cross-linking reagents for enhanced MS analysis of cross-linked peptides (24, 33–39). Among these approaches, the development of new cross-linking reagents holds great promise for mapping PPIs on the systems level. One class of cross-linking reagents containing an enrichment handle have been shown to allow selective isolation of cross-linked products from complex mixtures, boosting their detectability by MS (33–35, 40–42). A second class of cross-linkers containing MS-cleavable bonds have proven to be effective in facilitating the unambiguous identification of cross-linked peptides (36–39, 43, 44), as the resulting cross-linked products can be identified based on their characteristic and simplified fragmentation behavior during MS analysis. Therefore, an ideal cross-linking reagent would possess the combined features of both classes of cross-linkers. To advance the study of in vivo PPIs, we have developed a new XL-MS platform based on a novel membrane-permeable, enrichable, and MS-cleavable cross-linker, Azide-A-DSBSO (azide-tagged, acid-cleavable disuccinimidyl bis-sulfoxide), and multistage tandem mass spectrometry (MSⁿ). This new XL-MS strategy has been successfully employed to map in vivo PPIs from mammalian cells at both the proteome scale and the targeted protein complex level.     

A Study into the Collision-induced Dissociation (CID) Behavior of Cross-Linked Peptides

Cross-linking/mass spectrometry resolves protein–protein interactions or protein folds by help of distance constraints. Cross-linkers with specific properties such as isotope-labeled or collision-induced dissociation (CID)-cleavable cross-linkers are in frequent use to simplify the identification of cross-linked peptides. Here, we analyzed the mass spectrometric behavior of 910 unique cross-linked peptides in high-resolution MS1 and MS2 from published data and validate the observation by a ninefold larger set from currently unpublished data to explore if detailed understanding of their fragmentation behavior would allow computational delivery of information that otherwise would be obtained via isotope labels or CID cleavage of cross-linkers. Isotope-labeled cross-linkers reveal cross-linked and linear fragments in fragmentation spectra. We show that fragment mass and charge alone provide this information, alleviating the need for isotope-labeling for this purpose. Isotope-labeled cross-linkers also indicate cross-linker-containing, albeit not specifically cross-linked, peptides in MS1. We observed that acquisition can be guided to better than twofold enrich cross-linked peptides with minimal losses based on peptide mass and charge alone. By help of CID-cleavable cross-linkers, individual spectra with only linear fragments can be recorded for each peptide in a cross-link. We show that cross-linked fragments of ordinary cross-linked peptides can be linearized computationally and that a simplified subspectrum can be extracted that is enriched in information on one of the two linked peptides. This allows identifying candidates for this peptide in a simplified database search as we propose in a search strategy here. We conclude that the specific behavior of cross-linked peptides in mass spectrometers can be exploited to relax the requirements on cross-linkers.Cross-linking/mass spectrometry extends the use of mass-spectrometry-based proteomics from identification (1, 2), quantification (3), and characterization of protein complexes (4) into resolving protein structures and protein–protein interactions (5–8). Chemical reagents (cross-linkers) covalently connect amino acid pairs that are within a cross-linker-specific distance range in the native three-dimensional structure of a protein or protein complex. A cross-linking/mass spectrometry experiment is typically conducted in four steps: (1) cross-linking of the target protein or complex, (2) protein digestion (usually with trypsin), (3) LC-MS analysis, and (4) database search. The digested peptide mixture consists of linear and cross-linked peptides, and the latter can be enriched by strong cation exchange (9) or size exclusion chromatography (10). Cross-linked peptides are of high value as they provide direct information on the structure and interactions of proteins.Cross-linked peptides fragment under collision-induced dissociation (CID) conditions primarily into b- and y-ions, as do their linear counterparts. An important difference regarding database searches between linear and cross-linked peptides stems from not knowing which peptides might be cross-linked. Therefore, one has to consider each single peptide and all pairwise combinations of peptides in the database. Having n peptides leads to (n2 + n)/2 possible pairwise combinations. This leads to two major challenges: With increasing size of the database, search time and the risk of identifying false positives increases. One way of circumventing these problems is to use MS2-cleavable cross-linkers (11, 12), at the cost of limited experimental design and choice of cross-linker.In a first database search approach (13), all pairwise combinations of peptides in a database were considered in a concatenated and linearized form. Thereby, all possible single bond fragments are considered in one of the two database entries per peptide pair, and the cross-link can be identified by a normal protein identification algorithm. Already, the second search approach split the peptides for the purpose of their identification (14). Linear fragments were used to retrieve candidate peptides from the database that are then matched based on the known mass of the cross-linked pair and scored as a pair against the spectrum. Isotope-labeled cross-linkers were used to sort the linear and cross-linked fragments apart. Many other search tools and approaches have been developed since (10, 15–19); see (20) for a more detailed list, at least some of which follow the general idea of an open modification search (21–24).As a general concept for open modification search of cross-linked peptides, cross-linked peptides represent two peptides, each with an unknown modification given by the mass of the other peptide and the cross-linker. One identifies both peptides individually and then matches them based on knowing the mass of cross-linked pair (14, 22, 24). Alternatively, one peptide is identified first and, using that peptide and the cross-linker as a modification mass, the second peptide is identified from the database (21, 23). An important element of the open modification search approach is that it essentially converts the quadratic search space of the cross-linked peptides into a linear search space of modified peptides. Still, many peptides and many modification positions have to be considered, especially when working with large databases or when using highly reactive cross-linkers with limited amino acid selectivity (25).We hypothesize that detailed knowledge of the fragmentation behavior of cross-linked peptides might reveal ways to improve the identification of cross-linked peptides. Detailed analyses of the fragmentation behavior of linear peptides exist (26–28), and the analysis of the fragmentation behavior of cross-linked peptides has guided the design of scores (24, 29). Further, cross-link-specific ions have been observed from higher energy collision dissociation (HCD) data (30). Isotope-labeled cross-linkers are used to distinguish cross-linked from linear fragments, generally in low-resolution MS2 of cross-linked peptides (14).We compared the mass spectrometric behavior of cross-linked peptides to that of linear peptides, using 910 high-resolution fragment spectra matched to unique cross-linked peptides from multiple different public datasets at 5% peptide-spectrum match (PSM)¹ false discovery rate (FDR). In addition, we repeated all experiments with a larger sample set that contains 8,301 spectra—also including data from ongoing studies from our lab (Supplemental material S9-S12). This paper presents the mass spectrometric signature of cross-linked peptides that we identified in our analysis and the resulting heuristics that are incorporated into an integrated strategy for the analysis and identification of cross-linked peptides. We present computational strategies that indicate the possibility of alleviating the need for mass-spectrometrically restricted cross-linker choice.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Online Peptide Fractionation Using a Multiphasic Microfluidic Liquid Chromatography Chip Improves Reproducibility and Detection Limits for Quantitation in Discovery and Targeted Proteomics

Comprehensive proteomic profiling of biological specimens usually requires multidimensional chromatographic peptide fractionation prior to mass spectrometry. However, this approach can suffer from poor reproducibility because of the lack of standardization and automation of the entire workflow, thus compromising performance of quantitative proteomic investigations. To address these variables we developed an online peptide fractionation system comprising a multiphasic liquid chromatography (LC) chip that integrates reversed phase and strong cation exchange chromatography upstream of the mass spectrometer (MS). We showed superiority of this system for standardizing discovery and targeted proteomic workflows using cancer cell lysates and nondepleted human plasma. Five-step multiphase chip LC MS/MS acquisition showed clear advantages over analyses of unfractionated samples by identifying more peptides, consuming less sample and often improving the lower limits of quantitation, all in highly reproducible, automated, online configuration. We further showed that multiphase chip LC fractionation provided a facile means to detect many N- and C-terminal peptides (including acetylated N terminus) that are challenging to identify in complex tryptic peptide matrices because of less favorable ionization characteristics. Given as much as 95% of peptides were detected in only a single salt fraction from cell lysates we exploited this high reproducibility and coupled it with multiple reaction monitoring on a high-resolution MS instrument (MRM-HR). This approach increased target analyte peak area and improved lower limits of quantitation without negatively influencing variance or bias. Further, we showed a strategy to use multiphase LC chip fractionation LC-MS/MS for ion library generation to integrate with SWATH^TM data-independent acquisition quantitative workflows. All MS data are available via ProteomeXchange with identifier PXD001464.Mass spectrometry based proteomic quantitation is an essential technique used for contemporary, integrative biological studies. Whether used in discovery experiments or for targeted biomarker applications, quantitative proteomic studies require high reproducibility at many levels. It requires reproducible run-to-run peptide detection, reproducible peptide quantitation, reproducible depth of proteome coverage, and ideally, a high degree of cross-laboratory analytical reproducibility. Mass spectrometry centered proteomics has evolved steadily over the past decade, now mature enough to derive extensive draft maps of the human proteome (1, 2). Nonetheless, a key requirement yet to be realized is to ensure that quantitative proteomics can be carried out in a timely manner while satisfying the aforementioned challenges associated with reproducibility. This is especially important for recent developments using data independent MS quantitation and multiple reaction monitoring on high-resolution MS (MRM-HR)¹ as they are both highly dependent on LC peptide retention time reproducibility and precursor detectability, while attempting to maximize proteome coverage (3). Strategies usually employed to increase the depth of proteome coverage utilize various sample fractionation methods including gel-based separation, affinity enrichment or depletion, protein or peptide chemical modification-based enrichment, and various peptide chromatography methods, particularly ion exchange chromatography (4–10). In comparison to an unfractionated “naive” sample, the trade-off in using these enrichments/fractionation approaches are higher risk of sample losses, introduction of undesired chemical modifications (e.g. oxidation, deamidation, N-terminal lactam formation), and the potential for result skewing and bias, as well as numerous time and human resources required to perform the sample preparation tasks. Online-coupled approaches aim to minimize those risks and address resource constraints. A widely practiced example of the benefits of online sample fractionation has been the decade long use of combining strong cation exchange chromatography (SCX) with C18 reversed-phase (RP) for peptide fractionation (known as MudPIT – multidimensional protein identification technology), where SCX and RP is performed under the same buffer conditions and the SCX elution performed with volatile organic cations compatible with reversed phase separation (11). This approach greatly increases analyte detection while avoiding sample handling losses. The MudPIT approach has been widely used for discovery proteomics (12–14), and we have previously shown that multiphasic separations also have utility for targeted proteomics when configured for selected reaction monitoring MS (SRM-MS). We showed substantial advantages of MudPIT-SRM-MS with reduced ion suppression, increased peak areas and lower limits of detection (LLOD) compared with conventional RP-SRM-MS (15).To improve the reproducibility of proteomic workflows, increase throughput and minimize sample loss, numerous microfluidic devices have been developed and integrated for proteomic applications (16, 17). These devices can broadly be classified into two groups: (1) microfluidic chips for peptide separation (18–25) and; (2) proteome reactors that combine enzymatic processing with peptide based fractionation (26–30). Because of the small dimension of these devices, they are readily able to integrate into nanoLC workflows. Various applications have been described including increasing proteome coverage (22, 27, 28) and targeting of phosphopeptides (24, 31, 32), glycopeptides and released glycans (29, 33, 34).In this work, we set out to take advantage of the benefits of multiphasic peptide separations and address the reproducibility needs required for high-throughput comparative proteomics using a variety of workflows. We integrated a multiphasic SCX and RP column in a “plug-and-play” microfluidic chip format for online fractionation, eliminating the need for users to make minimal dead volume connections between traps and columns. We show the flexibility of this format to provide robust peptide separation and reproducibility using conventional and topical mass spectrometry workflows. This was undertaken by coupling the multiphase liquid chromatography (LC) chip to a fast scanning Q-ToF mass spectrometer for data dependent MS/MS, data independent MS (SWATH) and for targeted proteomics using MRM-HR, showing clear advantages for repeatable analyses compared with conventional proteomic workflows.     

Cerebrospinal Fluid Peptides as Potential Parkinson Disease Biomarkers: A Staged Pipeline for Discovery and Validation

Finding robust biomarkers for Parkinson disease (PD) is currently hampered by inherent technical limitations associated with imaging or antibody-based protein assays. To circumvent the challenges, we adapted a staged pipeline, starting from our previous proteomic profiling followed by high-throughput targeted mass spectrometry (MS), to identify peptides in human cerebrospinal fluid (CSF) for PD diagnosis and disease severity correlation. In this multicenter study consisting of training and validation sets, a total of 178 subjects were randomly selected from a retrospective cohort, matching age and sex between PD patients, healthy controls, and neurological controls with Alzheimer disease (AD). From ∼14,000 unique peptides displaying differences between PD and healthy control in proteomic investigations, 126 peptides were selected based on relevance and observability in CSF using bioinformatic analysis and MS screening, and then quantified by highly accurate and sensitive selected reaction monitoring (SRM) in the CSF of 30 PD patients versus 30 healthy controls (training set), followed by diagnostic (receiver operating characteristics) and disease severity correlation analyses. The most promising candidates were further tested in an independent cohort of 40 PD patients, 38 AD patients, and 40 healthy controls (validation set). A panel of five peptides (derived from SPP1, LRP1, CSF1R, EPHA4, and TIMP1) was identified to provide an area under curve (AUC) of 0.873 (sensitivity = 76.7%, specificity = 80.0%) for PD versus healthy controls in the training set. The performance was essentially confirmed in the validation set (AUC = 0.853, sensitivity = 82.5%, specificity = 82.5%). Additionally, this panel could also differentiate the PD and AD groups (AUC = 0.990, sensitivity = 95.0%, specificity = 97.4%). Furthermore, a combination of two peptides belonging to proteins TIMP1 and APLP1 significantly correlated with disease severity as determined by the Unified Parkinson''s Disease Rating Scale motor scores in both the training (r = 0.381, p = 0.038)j and the validation (r = 0.339, p = 0.032) sets. The novel panel of CSF peptides, if validated in independent cohorts, could be used to assist in clinical diagnosis of PD and has the potential to help monitoring or predicting disease progression.Parkinson disease (PD)¹, the second most common neurodegenerative disease after Alzheimer disease (AD), afflicts roughly 2% of persons over the age of 65 years (1, 2). Currently, PD diagnosis is mainly based on observation of the cardinal motor indicators of the disease, patient response to drug treatment, and medical history (3, 4). There is an appreciable misdiagnosis rate (4), particularly at early disease stages. Additionally, no objective measure of disease progression or treatment effects has been established. Thus, objective, reliable, and reproducible biomarkers are clearly needed to aid in the diagnosis of PD and tracking or predicting the disease progression.The most sensitive tests developed to date are based on imaging modalities, which can detect functional and structural abnormalities even prior to the onset of motor dysfunction (5, 6). However, the usefulness of neuroimaging techniques is limited by high cost, limited accessibility, difficulty in reliable differentiation of PD from other atypical parkinsonian disorders and subjection to confounding factors such as medication and compensatory responses (4–7). Biochemical and molecular markers in cerebrospinal fluid (CSF) and other body fluids have also been actively investigated (5, 8–12). The most extensively studied candidate in CSF is probably α-synuclein, the major protein component of Lewy bodies and Lewy neurites, the pathological hallmarks of PD (2). The current consensus is that CSF α-synuclein concentrations are generally lower in patients with PD compared with controls (5, 8–10); the sensitivity and specificity, however, appear to be only moderate, and no correlation with PD severity or progression has been observed (8, 9). Notably, all these CSF protein markers are measured using antibody-based assays, which are often associated with relatively high variability, particularly when different detection techniques (different antibodies, sample preparation, calibrators, etc.) are used, leading to discrepant results across laboratories (5). It should also be stressed that this high variability in immunoassays is not unique to PD, because similar difficulty is encountered in AD and other related disorders (13, 14).One strategy to avoid the inherent technical limitations associated with antibodies is to use alternative techniques in which unique peptides are selected and precisely quantified with mass spectrometry (MS) techniques, for example, accurate inclusion mass screening (AIMS) (15) and selected reaction monitoring (SRM) (16–18). To this end, in the last few years, we and others have utilized proteomic technologies to identify novel proteins and peptides associated with different disease states and stages (5, 6, 19–25). Using brain tissue or CSF, these unbiased proteomic profiling studies have revealed disease-related alterations in hundreds of peptides derived from many proteins (19–25). However, there are no quantitative assays for the majority of these candidate proteins/peptides, and development of such assays is limited by the lack of antibodies available for many of them. Thus, although a large library of potential peptide biomarkers has been developed, the vast majority never reach the stage of validation and clinical testing, hampered by the difficulty of de novo development of immunoassays, a process that is time consuming, prohibitively expensive to develop and very difficult to multiplex.In this study, we aim to establish a PD biomarker identification and verification pipeline, with the goal of prioritizing candidates and swiftly developing reliable quantitative assays. We focused on identifying peptides by SRM and AIMS, because these targeted proteomic technologies have been proposed as the basis of a viable biomarker pipeline (16) and have become a powerful tool in biomarker discovery because of their high sensitivity, accuracy and specificity. SRM, in particular, has emerged as an alternative to immunoaffinity-based measurements of defined protein sets with excellent reproducibility across different laboratories and instrument platforms (17, 18). The staged pipeline in the current investigation (Fig. 1) includes: (1) data-dependent and bioinformatic prioritization of thousands of candidate biomarkers identified in our previous profiling studies, (2) de novo development of antibody-free multiplex SRM assays to reliably measure tens to hundreds of peptides simultaneously, and (3) multiplex biomarker verification studies allowing identification and validation of models or panels of candidates in independent sample sets, two of which were used in this study.Open in a separate windowFig. 1.Overview of the workflow used for CSF peptide biomarker discovery and validation. AD, Alzheimer disease; AIMS, accurate inclusion mass screening; CO, healthy controls; DDA, data-dependent acquisition; PD, Parkinson disease; SRM, selected reaction monitoring.