High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.     

Comprehensive Identification of Proteins from MALDI Imaging

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a powerful tool for the visualization of proteins in tissues and has demonstrated considerable diagnostic and prognostic value. One main challenge is that the molecular identity of such potential biomarkers mostly remains unknown. We introduce a generic method that removes this issue by systematically identifying the proteins embedded in the MALDI matrix using a combination of bottom-up and top-down proteomics. The analyses of ten human tissues lead to the identification of 1400 abundant and soluble proteins constituting the set of proteins detectable by MALDI IMS including >90% of all IMS biomarkers reported in the literature. Top-down analysis of the matrix proteome identified 124 mostly N- and C-terminally fragmented proteins indicating considerable protein processing activity in tissues. All protein identification data from this study as well as the IMS literature has been deposited into MaTisse, a new publically available database, which we anticipate will become a valuable resource for the IMS community.Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS)¹ is an emerging technique that can be described as a multi-color molecular microscope as it allows visualizing the distribution of many molecules as mass to charge (m/z) signals in parallel in situ (1). Originally described some 15 years ago (2) the method has been successfully adapted to different analyte classes including small molecule drugs (3), metabolites (4), lipids (5), proteins (6), and peptides (7) using e.g. formalin fixed paraffin embedded (FFPE) as well as fresh frozen tissue (8). Because the tissue stays intact in the process, MALDI IMS is compatible with histochemistry (9) as well as immunohistochemistry and thus adds an additional dimension of molecular information to classical microscopy based tissue analysis (10). Imaging of proteins is appealing as it conceptually allows determining the localization and abundance of proteoforms (11) that naturally occur in the tissue under investigation including modifications such as phosphorylation, acetylation, or ubiquitination, protease mediated cleavage or truncation (12). Therefore a proteinous m/z species detected by MALDI IMS can be viewed as an in situ molecular probe of a particular biological process. In turn, m/z abundance patterns that discriminate different physiological or pathological conditions might be used as diagnostic or even prognostic markers (13, 14). In recent years, MALDI IMS of proteins has been successfully applied to different cancer types from the brain (15), breast (16, 17), kidney (18), prostate (19), and skin (20). Furthermore, the technique has been applied in the context of colon inflammation (21), embryonic development (22), Alzheimer''s disease (23), and amyotrophic lateral sclerosis (24). With a few notable exceptions (13, 14, 16–18, 20, 24–30), the identity of the proteins constituting the observed characteristic m/z patters has generally remained elusive. This not only precludes the validation of the putative biomarkers by, for example, immunohistochemistry, but also the elucidation of the biological processes that might underlie the observed phenotype.Here, we introduce a straightforward extraction and identification method for proteins embedded in the MALDI matrix layer that represent the molecular species amenable to MALDI IMS. Using a bottom-up proteomics approach including tryptic digestion and liquid chromatography tandem mass spectrometry (LC-MS/MS), we first created an inventory list of proteins derived from this layer, which we term the MALDI matrix proteome. Although the bottom-up approach breaks the link between the identified proteins and the m/z species detected in MALDI IMS, the list of identified proteins serves as the pool of proteins from which all potential biomarkers are most likely derived. Indeed we detected >90% of all human MALDI IMS biomarkers reported in the literature by analyzing just ten human tissues. In addition, the results demonstrate that the same inventory can be used as a focused database for direct top-down sequencing and identification of proteins extracted from the MALDI matrix layer. The proposed method is generic and can be applied to any MALDI IMS study, which is why we believe that one of the major challenges in identifying MALDI IMS biomarkers has now been overcome. In addition, we provide a list of all proteins and peptides identified in the MALDI matrices and tissues studied here as well as a comprehensive list of m/z species identified in the literature dealing with MALDI imaging of humans and rodents. This information has been compiled in MaTisse (http://www.wzw.tum.de/bioanalytik/matisse), a new publically available and searchable database, which we believe will become a valuable tool for the MALDI imaging community.     

Intermembrane Space Proteome of Yeast Mitochondria

The intermembrane space (IMS) represents the smallest subcompartment of mitochondria. Nevertheless, it plays important roles in the transport and modification of proteins, lipids, and metal ions and in the regulation and assembly of the respiratory chain complexes. Moreover, it is involved in many redox processes and coordinates key steps in programmed cell death. A comprehensive profiling of IMS proteins has not been performed so far. We have established a method that uses the proapoptotic protein Bax to release IMS proteins from isolated mitochondria, and we profiled the protein composition of this compartment. Using stable isotope-labeled mitochondria from Saccharomyces cerevisiae, we were able to measure specific Bax-dependent protein release and distinguish between quantitatively released IMS proteins and the background efflux of matrix proteins. From the known 31 soluble IMS proteins, 29 proteins were reproducibly identified, corresponding to a coverage of >90%. In addition, we found 20 novel intermembrane space proteins, out of which 10 had not been localized to mitochondria before. Many of these novel IMS proteins have unknown functions or have been reported to play a role in redox regulation. We confirmed IMS localization for 15 proteins using in organello import, protease accessibility upon osmotic swelling, and Bax-release assays. Moreover, we identified two novel mitochondrial proteins, Ymr244c-a (Coa6) and Ybl107c (Mic23), as substrates of the MIA import pathway that have unusual cysteine motifs and found the protein phosphatase Ptc5 to be a novel substrate of the inner membrane protease (IMP). For Coa6 we discovered a role as a novel assembly factor of the cytochrome c oxidase complex. We present here the first and comprehensive proteome of IMS proteins of yeast mitochondria with 51 proteins in total. The IMS proteome will serve as a valuable source for further studies on the role of the IMS in cell life and death.Mitochondria are double-membrane-bound organelles that fulfill a multitude of important cellular functions. Proteomic analysis of purified mitochondria revealed that they contain approximately 1000 (yeast) to 1500 (human) different proteins (1–3). However, the distribution of these proteins among the four mitochondrial subcompartments (outer membrane, inner membrane, matrix, and intermembrane space) has been only marginally studied through global approaches. This is attributed to the high complexity of purifying submitochondrial fractions to a grade suitable for proteomic analysis. The best-studied submitochondrial proteomes comprise the outer membranes of S. cerevisae, N. crassa, and A. thaliana (4–6). The mitochondrial intermembrane space (IMS)¹ represents a highly interesting compartment for several reasons: it provides a redox active space that promotes oxidation of cysteine residues similar to the endoplasmic reticulum and the bacterial periplasm, but unlike cytosol, nucleus, or the mitochondrial matrix where the presence of thioredoxins or glutaredoxins prevents the risk of unwanted cysteine oxidation (7, 8). Furthermore in higher eukaryotes IMS proteins are released into the cytosol upon apoptotic induction, which triggers the activation of a cell-killing protease activation cascade (9, 10). The IMS can also exchange proteins, lipids, metal ions, and various metabolites with other cellular compartments, allowing mitochondrial metabolism to adapt to cellular homeostasis. In particular, the biogenesis and activity of the respiratory chain were shown to be controlled by various proteins of the IMS (11–13). Most of the currently known IMS proteins are soluble proteins; however, some inner membrane proteins have been annotated as IMS proteins as well, such as proteins that are peripherally attached to the inner membrane or membrane proteins that expose enzyme activity toward the IMS (8).All IMS proteins are encoded in the nuclear DNA and have to be imported after translation in the cytosol (14–19). Two main pathways are known to mediate the import and sorting of proteins into the IMS. One class of proteins contains bipartite presequences that consist of a matrix targeting signal and a hydrophobic sorting signal. These signals arrest the incoming preprotein at the inner membrane translocase TIM23. After insertion into the inner membrane, the soluble, mature protein can be released into the IMS by the inner membrane protease (IMP) (20–22). The second class of IMS proteins possesses characteristic cysteine motifs that typically are either twin CX₉C or twin CX₃C motifs (23, 24). Upon translocation across the outer membrane via the TOM complex, disulfide bonds are formed within the preproteins, which traps them in the IMS. Disulfide bond formation is mediated by the MIA machinery, which consists of the inner-membrane-anchored Mia40 and the soluble IMS protein Erv1 (25–28).The release of cytochrome c from the IMS upon binding and insertion of Bax at the outer membrane is a hallmark of programmed cell death. Although Bax is found only in higher eukaryotes, it was shown that recombinant mammalian Bax induces the release of cytochrome c upon incubation with isolated yeast mitochondria (29, 30). Furthermore, we found that not only cytochrome c but also other soluble IMS proteins are released from Bax-treated yeast mitochondria, whereas soluble matrix proteins largely remain within the organelle (30).We used this apparently conserved mechanism to systematically profile the protein composition of the yeast mitochondrial IMS by employing an experimental approach based on stable isotope labeling, which allowed for the specific identification of Bax-dependent protein release. Almost the entire set of known soluble IMS proteins was identified, and 20 additional, novel soluble IMS proteins were found. We confirmed IMS localization for 15 proteins through biochemical assays. Among these proteins, we identified novel proteins that fall into several classes: (i) those that are involved in maintaining protein redox homeostasis (thioredoxins, thioredoxin reductases, or thiol peroxidases), (ii) those that undergo proteolytic processing by IMP (Ptc5), (iii) those that utilize the MIA pathway for their import (Mic23 and Coa6), and (iv) those that play a role in the assembly of cytochrome c oxidase (Coa6).     

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.     

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]     

A Universal Chemical Enrichment Method for Mapping the Yeast N-glycoproteome by Mass Spectrometry (MS)

Glycosylation is one of the most common and important protein modifications in biological systems. Many glycoproteins naturally occur at low abundances, which makes comprehensive analysis extremely difficult. Additionally, glycans are highly heterogeneous, which further complicates analysis in complex samples. Lectin enrichment has been commonly used, but each lectin is inherently specific to one or several carbohydrates, and thus no single or collection of lectin(s) can bind to all glycans. Here we have employed a boronic acid-based chemical method to universally enrich glycopeptides. The reaction between boronic acids and sugars has been extensively investigated, and it is well known that the interaction between boronic acid and diols is one of the strongest reversible covalent bond interactions in an aqueous environment. This strong covalent interaction provides a great opportunity to catch glycopeptides and glycoproteins by boronic acid, whereas the reversible property allows their release without side effects. More importantly, the boronic acid-diol recognition is universal, which provides great capability and potential for comprehensively mapping glycosylation sites in complex biological samples. By combining boronic acid enrichment with PNGase F treatment in heavy-oxygen water and MS, we have identified 816 N-glycosylation sites in 332 yeast proteins, among which 675 sites were well-localized with greater than 99% confidence. The results demonstrated that the boronic acid-based chemical method can effectively enrich glycopeptides for comprehensive analysis of protein glycosylation. A general trend seen within the large data set was that there were fewer glycosylation sites toward the C termini of proteins. Of the 332 glycoproteins identified in yeast, 194 were membrane proteins. Many proteins get glycosylated in the high-mannose N-glycan biosynthetic and GPI anchor biosynthetic pathways. Compared with lectin enrichment, the current method is more cost-efficient, generic, and effective. This method can be extensively applied to different complex samples for the comprehensive analysis of protein glycosylation.Glycosylation is an extremely important protein modification that frequently regulates protein folding, trafficking, and stability. It is also involved in a wide range of cellular events (1) such as immune response (2, 3), cell proliferation (4), cell-cell interactions (5), and signal transduction (6). Aberrant protein glycosylation is believed to have a direct correlation with the development of several diseases, including diabetes, infectious diseases, and cancer (7–11). Secretory proteins frequently get glycosylated, including those in body fluids such as blood, saliva, and urine (12, 13). Samples containing these proteins can be easily obtained and used for diagnostic and therapeutic purposes. Several glycoproteins have previously been identified as biomarkers, including Her2/Neu in breast cancer (14), prostate-specific antigen (PSA) in prostate cancer (15), and CA125 in ovarian cancer (16, 17), which highlights the clinical importance of identifying glycoproteins as indicators or biomarkers of diseases. Therefore, effective methods for systematic analysis of protein glycosylation are essential to understand the mechanisms of glycobiology, identify drug targets and discover biomarkers.Approximately half of mammalian cell proteins are estimated to be glycosylated at any given time (18). There have been many reports regarding identification of protein glycosylation sites and elucidation of glycan structures (19–30). Glycan structure analysis can lead to potential therapeutic and diagnostic applications (31, 32), but it is also critical to identify which proteins are glycosylated as well as the sites at which the modification occurs. Despite progress in recent years, the large-scale analysis of protein glycosylation sites using MS-based proteomics methods is still a challenge. Without an effective enrichment method, the low abundance of glycoproteins prohibits the identification of the majority of sites using the popular intensity-dependent MS sequence method.About a decade ago, a very beautiful and elegant method based on hydrazide chemistry was developed to enrich glycopeptides. Hydrazide conjugated beads reacted with aldehydes formed from the oxidation of cis-diols in glycans (33). This method has been extensively applied to many different types of biological samples (34–41). Besides the hydrazide-based enrichment method, lectins have also been frequently used to enrich glycopeptides or glycoproteins before MS analysis (28, 29, 42–46). However, there are many different types of lectins, and each is specific to certain glycans (47, 48). Therefore, no combination of lectins can bind to all glycosylated peptides or proteins, which prevents comprehensive analysis of protein glycosylation. Because of the complexity of biological samples, effective enrichment methods are critical for the comprehensive analysis of protein glycosylation before MS analysis.One common feature of all glycoproteins and glycopeptides is that they contain multiple hydroxyl groups in their glycans. From a chemistry point of view, this can be exploited to effectively enrich them. Ideally, chemical enrichment probes must have both strong and specific interactions with multiple hydroxyl groups. The reaction between boronic acids and 1,2- or 1,3-cis-diols in sugars has been extensively studied (49–52) and applied for the small-scale analysis of glycoproteins (53–55). Furthermore, boronate affinity chromatography has been employed for the analysis of nonenzymatically glycated peptides (56, 57). Boronic acid-based chemical enrichment methods are expected to have great potential for global analysis of glycopeptides when combined with modern MS-based proteomics techniques. However, the method has not yet been used for the comprehensive analysis of protein N-glycosylation in complex biological samples (58).Yeast is an excellent model biological system that has been extensively used in a wide range of experiments. Last year, two papers reported the large-scale analysis of protein N-glycosylation in yeast (59, 60). In one study, a new MS-based method was developed based on N-glycopeptide mass envelopes with a pattern via metabolic incorporation of a defined mixture of N-acetylglucosamine isotopologs into N-glycans. Peptides with the recoded envelopes were specifically targeted for fragmentation, facilitating high confidence site mapping (59). Using this method, 133 N-glycosylation sites were confidently identified in 58 yeast proteins. When combined with an effective enrichment method, this MS-based analysis will provide a more complete coverage of the N-glycoproteome. The other work combined lectin enrichment with digestion by two enzymes (Glu_c and trypsin) to increase the peptide coverage, and 516 well-localized N-glycosylation sites were identified in 214 yeast proteins by MS (60).Here we have comprehensively identified protein N-glycosylation sites in yeast by combining a boronic acid-based chemical enrichment method with MS-based proteomics techniques. Magnetic beads conjugated with boronic acid were systematically optimized to selectively enrich glycosylated peptides from yeast whole cell lysates. The enriched peptides were subsequently treated with Peptide-N4-(N-acetyl-beta-glucosaminyl)asparagine amidase (PNGase F)¹ in heavy-oxygen water. Finally, peptides were analyzed by an on-line LC-MS system. Over 800 protein N-glycosylation sites were identified in the yeast proteome, which clearly demonstrates that the boronic acid-based chemical method is an effective enrichment method for large-scale analysis of protein glycosylation by MS.     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[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]     

From Whole-body Sections Down to Cellular Level,Multiscale Imaging of Phospholipids by MALDI Mass Spectrometry

Significant progress in instrumentation and sample preparation approaches have recently expanded the potential of MALDI imaging mass spectrometry to the analysis of phospholipids and other endogenous metabolites naturally occurring in tissue specimens. Here we explore some of the requirements necessary for the successful analysis and imaging of phospholipids from thin tissue sections of various dimensions by MALDI time-of-flight mass spectrometry. We address methodology issues relative to the imaging of whole-body sections such as those cut from model laboratory animals, sections of intermediate dimensions typically prepared from individual organs, as well as the requirements for imaging areas of interests from these sections at a cellular scale spatial resolution. We also review existing limitations of MALDI imaging MS technology relative to compound identification. Finally, we conclude with a perspective on important issues relative to data exploitation and management that need to be solved to maximize biological understanding of the tissue specimen investigated.Since its introduction in the late 90s (1), MALDI imaging mass spectrometry (MS) technology has witnessed a phenomenal expansion. Initially introduced for the mapping of intact proteins from fresh frozen tissue sections (2), imaging MS is now routinely applied to a wide range of different compounds including peptides, proteins, lipids, metabolites, and xenobiotics (3–7). Numerous compound-specific sample preparation protocols and analytical strategies have been developed. These include tissue sectioning and handling (8–14), automated matrix deposition approaches and data acquisition strategies (15–21), and the emergence of in situ tissue chemistries (22–25). Originally performed on sections cut from fresh frozen tissue specimens, methodologies incorporating an in situ enzymatic digestion step prior to matrix application have been optimized to access the proteome locked in formalin-fixed paraffin-embedded tissue biopsies (25–29). The possibility to use tissues preserved using non-cross-linking approaches has also been demonstrated (30–32). These methodologies are of high importance for the study of numerous diseases because they potentially allow the retrospective analysis for biomarker validation and discovery of the millions of tissue biopsies currently stored worldwide in tissue banks and repositories.In the past decade, instrumentation for imaging MS has also greatly evolved. Whereas the first MS images were collected with time-of-flight instruments (TOF) capable of repetition rates of a few hertz, modern systems are today capable of acquiring data in the kilohertz range and above with improved sensitivity, mass resolving power, and accuracy, significantly reducing acquisition time and improving image quality (33, 34). Beyond time-of-flight analyzers, other MALDI-based instruments have been used such as ion traps (35–37), Qq TOF instruments (38–40), and trap-TOF (16, 41). Ion mobility technology has also been used in conjunction with imaging MS (42–44). More recently, MALDI FT/ICR and Orbitrap mass spectrometers have been demonstrated to be extremely valuable instruments for the performance of imaging MS at very high mass resolving power (45–47). These non-TOF-based systems have proven to be extremely powerful for the imaging of lower molecular weight compounds such as lipids, drugs, and metabolites. Home-built instrumentation and analytical approaches to probe tissues at higher spatial resolution (1–10 μm) have also been described (48–50). In parallel to instrumentation developments, automated data acquisition, image visualization, and processing software packages have now also been developed by most manufacturers.To date, a wide range of biological systems have been studied using imaging MS as a primary methodology. Of strong interest are the organization and identification of the molecular composition of diseased tissues in direct correlation with the underlying histology and how it differs from healthy tissues. Such an approach has been used for the study of cancers (51–54), neurologic disorders (55–57), and other diseases (58, 59). The clinical potential of the imaging MS technology is enormous (7, 60, 61). Results give insights into the onset and progression of diseases, identify novel sets of disease-specific markers, and can provide a molecular confirmation of diagnosis as well as aide in outcome prediction (62–64). Imaging MS has also been extensively used to study the development, functioning, and aging of different organs such as the kidney, prostate, epididymis, and eye lens (65–70). Beyond the study of isolated tissues or organs, whole-body sections from several model animals such as leeches, mice, and rats have been investigated (71–74). For these analyses, specialized instrumentation and protocols are necessary for tissue sectioning and handling (72, 73). Whole-body imaging MS opens the door to the study of the localization and accumulation of administered pharmaceuticals and their known metabolites at the level of entire organisms as well as the monitoring of their efficacy or toxicity as a function of time or dose (72, 73, 75, 76).There is considerable interest in determining the identification and localization of small biomolecules such as lipids in tissues because they are involved in many essential biological functions including cell signaling, energy storage, and membrane structure and function. Defects in lipid metabolism play a role in many diseases such as muscular dystrophy and cardiovascular disease. Phospholipids in tissues have been intensively studied by several groups (37, 40, 77–83). In this respect, for optimal recovery of signal, several variables such as the choice of matrix for both imaging and fragmentation, solvent system, and instrument polarity have been investigated (20, 84). Particularly, the use of lithium cation adducts to facilitate phospholipid identification by tandem MS directly from tissue has also been reported (85). Of significant interest is the recent emergence of two new solvent-free matrix deposition approaches that perform exceptionally well for phospholipid imaging analyses. The first approach, described by Hankin et al. (86), consists in depositing the matrix on the sections through a sublimation process. The described sublimation system consists of sublimation glassware, a heated sand or oil bath (100–200 °C), and a primary vacuum pump (∼5 × 10⁻² torr). Within a few minutes of initiating the sublimation process, an exceptionally homogeneous film of matrix forms on the section. The thickness of the matrix may be controlled by regulating pressure, temperature, and sublimation time. The second approach, described by Puolitaival et al.(87), uses a fine mesh sieve (≤20 μm) to filter finely ground matrix on the tissue sections. Agitation of the sieve results in passage of the matrix through the mesh and the deposition of a fairly homogeneous layer of submicrometer matrix crystals of the surface of the sections. The matrix density on the sections is controlled by direct observation using a standard light microscope. This matrix deposition approach was also found to be ideal to image certain drug compounds (88, 89). Both strategies allow very rapid production of homogeneous matrix coatings on tissue sections with a fairly inexpensive setup. Signal recovery was found to be comparable with those obtained by conventional spray deposition. With the appropriate size sublimation device or sieve, larger sections with dimensions of several centimeters such as those cut from mouse or rat whole bodies can also be rapidly and homogeneously coated.Here we present several examples of MALDI imaging MS of phospholipids from tissue sections using TOF mass spectrometers over a wide range of dimensions from whole-body sections (several centimeters), to individual organs (several millimeters), down to high spatial resolution imaging of selected tissue areas (hundreds of micrometers) at 10-μm lateral resolution and below. For all of these dimension ranges, technological considerations and practical aspects are discussed. In light of the imaging MS results, we also address issues faced for compound identification by tandem MS analysis performed directly on the sections. Finally, we discuss under “Perspective” our vision of the future of the field as well as the technological improvements and analytical tools that need to be improved upon and developed.     

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.     

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.     

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.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[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]     

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.