PTM MarkerFinder,a software tool to detect and validate spectra from peptides carrying post‐translational modifications

Mass spectrometry (MS) analysis of peptides carrying post‐translational modifications is challenging due to the instability of some modifications during MS analysis. However, glycopeptides as well as acetylated, methylated and other modified peptides release specific fragment ions during CID (collision‐induced dissociation) and HCD (higher energy collisional dissociation) fragmentation. These fragment ions can be used to validate the presence of the PTM on the peptide. Here, we present PTM MarkerFinder, a software tool that takes advantage of such marker ions. PTM MarkerFinder screens the MS/MS spectra in the output of a database search (i.e., Mascot) for marker ions specific for selected PTMs. Moreover, it reports and annotates the HCD and the corresponding electron transfer dissociation (ETD) spectrum (when present), and summarizes information on the type, number, and ratios of marker ions found in the data set. In the present work, a sample containing enriched N‐acetylhexosamine (HexNAc) glycopeptides from yeast has been analyzed by liquid chromatography‐mass spectrometry on an LTQ Orbitrap Velos using both HCD and ETD fragmentation techniques. The identification result (Mascot .dat file) was submitted as input to PTM MarkerFinder and screened for HexNAc oxonium ions. The software output has been used for high‐throughput validation of the identification results.     

De novo peptide sequencing using CID and HCD spectra pairs

In tandem mass spectrometry (MS/MS), there are several different fragmentation techniques possible, including, collision‐induced dissociation (CID) higher energy collisional dissociation (HCD), electron‐capture dissociation (ECD), and electron transfer dissociation (ETD). When using pairs of spectra for de novo peptide sequencing, the most popular methods are designed for CID (or HCD) and ECD (or ETD) spectra because of the complementarity between them. Less attention has been paid to the use of CID and HCD spectra pairs. In this study, a new de novo peptide sequencing method is proposed for these spectra pairs. This method includes a CID and HCD spectra merging criterion and a parent mass correction step, along with improvements to our previously proposed algorithm for sequencing merged spectra. Three pairs of spectral datasets were used to investigate and compare the performance of the proposed method with other existing methods designed for single spectrum (HCD or CID) sequencing. Experimental results showed that full‐length peptide sequencing accuracy was increased significantly by using spectra pairs in the proposed method, with the highest accuracy reaching 81.31%.     

Enhanced peptide identification by electron transfer dissociation using an improved Mascot Percolator

Peptide identification using tandem mass spectrometry is a core technology in proteomics. Latest generations of mass spectrometry instruments enable the use of electron transfer dissociation (ETD) to complement collision induced dissociation (CID) for peptide fragmentation. However, a critical limitation to the use of ETD has been optimal database search software. Percolator is a post-search algorithm, which uses semi-supervised machine learning to improve the rate of peptide spectrum identifications (PSMs) together with providing reliable significance measures. We have previously interfaced the Mascot search engine with Percolator and demonstrated sensitivity and specificity benefits with CID data. Here, we report recent developments in the Mascot Percolator V2.0 software including an improved feature calculator and support for a wider range of ion series. The updated software is applied to the analysis of several CID and ETD fragmented peptide data sets. This version of Mascot Percolator increases the number of CID PSMs by up to 80% and ETD PSMs by up to 60% at a 0.01 q-value (1% false discovery rate) threshold over a standard Mascot search, notably recovering PSMs from high charge state precursor ions. The greatly increased number of PSMs and peptide coverage afforded by Mascot Percolator has enabled a fuller assessment of CID/ETD complementarity to be performed. Using a data set of CID and ETcaD spectral pairs, we find that at a 1% false discovery rate, the overlap in peptide identifications by CID and ETD is 83%, which is significantly higher than that obtained using either stand-alone Mascot (69%) or OMSSA (39%). We conclude that Mascot Percolator is a highly sensitive and accurate post-search algorithm for peptide identification and allows direct comparison of peptide identifications using multiple alternative fragmentation techniques.     

The Generating Function of CID,ETD, and CID/ETD Pairs of Tandem Mass Spectra: Applications to Database Search

Recent emergence of new mass spectrometry techniques (e.g. electron transfer dissociation, ETD) and improved availability of additional proteases (e.g. Lys-N) for protein digestion in high-throughput experiments raised the challenge of designing new algorithms for interpreting the resulting new types of tandem mass (MS/MS) spectra. Traditional MS/MS database search algorithms such as SEQUEST and Mascot were originally designed for collision induced dissociation (CID) of tryptic peptides and are largely based on expert knowledge about fragmentation of tryptic peptides (rather than machine learning techniques) to design CID-specific scoring functions. As a result, the performance of these algorithms is suboptimal for new mass spectrometry technologies or nontryptic peptides. We recently proposed the generating function approach (MS-GF) for CID spectra of tryptic peptides. In this study, we extend MS-GF to automatically derive scoring parameters from a set of annotated MS/MS spectra of any type (e.g. CID, ETD, etc.), and present a new database search tool MS-GFDB based on MS-GF. We show that MS-GFDB outperforms Mascot for ETD spectra or peptides digested with Lys-N. For example, in the case of ETD spectra, the number of tryptic and Lys-N peptides identified by MS-GFDB increased by a factor of 2.7 and 2.6 as compared with Mascot. Moreover, even following a decade of Mascot developments for analyzing CID spectra of tryptic peptides, MS-GFDB (that is not particularly tailored for CID spectra or tryptic peptides) resulted in 28% increase over Mascot in the number of peptide identifications. Finally, we propose a statistical framework for analyzing multiple spectra from the same precursor (e.g. CID/ETD spectral pairs) and assigning p values to peptide-spectrum-spectrum matches.Since the introduction of electron capture dissociation (ECD)1 in 1998 (1), electron-based peptide dissociation technologies have played an important role in analyzing intact proteins and post-translational modifications (2). However, until recently, this research-grade technology was available only to a small number of laboratories because it was commercially unavailable, required experience for operation, and could be implemented only with expensive FT-ICR instruments. The discovery of electron-transfer dissociation (ETD) (3) enabled an ECD-like technology to be implemented in (relatively cheap) ion-trap instruments. Nowadays, many researchers are employing the ETD technology for tandem mass spectra generation (4–9).Although the hardware technologies to generate ETD spectra are maturing rapidly, software technologies to analyze ETD spectra are still in infancy. There are two major approaches to analyzing tandem mass spectra: de novo sequencing and database search. Both approaches find the best-scoring peptide either among all possible peptides (de novo sequencing) or among all peptides in a protein database (database search). Although de novo sequencing is emerging as an alternative to database search, database search remains a more accurate (and thus preferred) method of spectral interpretation, so here we focus on the database search approach.Numerous database search engines are currently available, including SEQUEST (10), Mascot (11), OMSSA (12), X!Tandem (13), and InsPecT (14). However, most of them are inadequate for the analysis of ETD spectra because they are optimized for collision induced dissociation (CID) spectra that show different fragmentation propensities than those of ETD spectra. Additionally, the existing tandem mass spectrometry (MS/MS) tools are biased toward the analysis of tryptic peptides because trypsin is usually used for CID, and thus not suitable for the analysis of nontryptic peptides that are common for ETD. Therefore, even though some database search engines support the analysis of ETD spectra (e.g. SEQUEST, Mascot, and OMSSA), their performance remains suboptimal when it comes to analyzing ETD spectra. Recently, an ETD-specific database search tool (Z-Core) was developed; however it does not significantly improve over OMSSA (15).We present a new database search tool (MS-GFDB) that significantly outperforms existing database search engines in the analysis of ETD spectra, and performs equally well on nontryptic peptides. MS-GFDB employs the generating function approach (MS-GF) that computes rigorous p values of peptide-spectrum matches (PSMs) based on the spectrum-specific score histogram of all peptides (16).² MS-GF p values are dependent only on the PSM (and not on the database), thus can be used as an alternative scoring function for the database search.Computing p values requires a scoring model evaluating qualities of PSMs. MS-GF adopts a probabilistic scoring model (MS-Dictionary scoring model) described in Kim et al., 2009 (17), considering multiple features including product ion types, peak intensities and mass errors. To define the parameters of this scoring model, MS-GF only needs a set of training PSMs.³ This set of PSMs can be obtained in a variety of ways: for example, one can generate CID/ETD pairs and use peptides identified by CID to form PSMs for ETD. Alternatively, one can generate spectra from a purified protein (when PSMs can be inferred from the accurate parent mass alone) or use a previously developed (not necessary optimal) tool to generate training PSMs. From these training PSMs, MS-GF automatically derives scoring parameters without assuming any prior knowledge about the specifics of a particular peptide fragmentation method (e.g. ETD, CID, etc.) and/or proteolytic origin of the peptides. MS-GF was originally designed for the analysis of CID spectra, but now it has been extended to other types of spectra generated by various fragmentation techniques and/or various enzymes. We show that MS-GF can be successfully applied to novel types of spectra (e.g. ETD of Lys-N peptides (18, 19)) by simply retraining scoring parameters without any modification. Note that although the same scoring model is used for different types of spectra, the parameters derived to score different types of spectra are dissimilar.We compared the performance of MS-GFDB with Mascot on a large ETD data set and found that it generated many more peptide identifications for the same false discovery rates (FDR). For example, at 1% peptide level FDR, MS-GFDB identified 9450 unique peptides from 81,864 ETD spectra of Lys-N peptides whereas Mascot only identified 3672 unique peptides, ≈160% increase in the number of peptide identifications (a similar improvement is observed for ETD spectra of tryptic peptides).⁴ MS-GFDB also showed a significant 28% improvement in the number of identified peptides from CID spectra of tryptic peptides (16,203 peptides as compared with 12,658 peptides identified by Mascot).The ETD technology complements rather than replaces CID because both technologies have some advantages: CID for smaller peptides with small charges, ETD for larger and multiply charged peptides (20, 21). An alternative way to utilize ETD is to use it in conjunction with CID because CID and ETD generate complementary sequence information (20, 22, 23). ETD-enabled instruments often support generating both CID and ETD spectra (CID/ETD pairs) for the same peptide. Although the CID/ETD pairs promise a great improvement in peptide identification, the full potential of such pairs has not been fully realized yet. In the case of de novo sequencing, de novo sequencing tools utilizing CID/ETD pairs indeed result in more accurate de novo peptide sequencing than traditional CID-based algorithms (23, 24, 25). However, in the case of database search, the argument that the use of CID/ETD pairs improves peptide identifications remains poorly substantiated. A few tools are developed to use CID/ETD (or CID/ECD) pairs for the database search but they are limited to preprocessing/postprocessing of the spectral data before or following running a traditional database search tool (26, 27). Nielsen et al., 2005 (22) pioneered the combined use of CID and ECD for the database search. Given a CID/ECD pair, they generated a combined spectrum comprised only of complementary pairs of peaks, and searched it with Mascot.⁵ However, this approach is hard to generalize to less accurate CID/ETD pairs generated by ion-trap instruments because there is a higher chance that the identified complementary pairs of peaks are spurious. More importantly, using traditional MS/MS tools (such as Mascot) for the database search of the combined spectrum is inappropriate, because they are not optimized for analyzing such combined spectra; a better approach would be to develop a new database search tool tailored for the combined spectrum. Recently, Molina et al., 2008 (26) studied database search of CID/ETD pairs using Spectrum Mill (Agilent Technologies, Santa Clara, CA) and came to a counterintuitive conclusion that using only CID spectra identifies 12% more unique peptides than using CID/ETD pairs. We believe that it is an acknowledgment of limitations of the traditional MS/MS database search tools for the analysis of multiple spectra generated from a single peptide.In this paper, we modify the generating function approach for interpreting CID/ETD pairs and further apply it to improve the database search with CID/ETD pairs. In contrast to previous approaches, our scoring is specially designed to interpret CID/ETD pairs and can be generalized to analyzing any type of multiple spectra generated from a single peptide. When CID/ETD pairs from trypsin digests are used, MS-GFDB identified 13% and 27% more peptides compared with the case when only CID spectra and only ETD spectra are used, respectively. The difference was even more prominent when CID/ETD pairs from Lys-N digests were used, with 41% and 33% improvement over CID only and ETD only, respectively.Assigning a p value to a PSM greatly helped researchers to evaluate the quality of peptide identifications. We now turn to the problem of assigning a p value to a peptide-spectrum-spectrum match (PS²M) when two spectra in PS²M are generated by different fragmentation technologies (e.g. ETD and CID). We argue that assigning statistical significance to a PS²M (or even PSⁿM) is a prerequisite for rigorous CID/ETD analyses. To our knowledge, MS-GFDB is the first tool to generate statistically rigorous p values of PSⁿMs.The MS-GFDB executable and source code is available at the website of Center for Computational Mass Spectrometry at UCSD (http://proteomics.ucsd.edu). It takes a set of spectra (CID, ETD, or CID/ETD pairs) and a protein database as an input and outputs peptide matches. If the input is a set of CID/ETD pairs, it outputs the best scoring peptide matches and their p values (1) using only CID spectra, (2) using only ETD spectra, and (3) using combined spectra of CID/ETD pairs.     

ETD fragmentation features improve algorithm

Electron transfer dissociation (ETD) is an alternative technique used in mass spectrometry-based proteomics experiments. Because it is newer, most of the protein identification algorithms for ETD are still a simple derivation of well-established collision-activated dissociation algorithms without the consideration of many unique ETD spectral features. Sridhara and coworkers recently reported removing the charge-reduced precursors and corresponding neutral loss peaks to improve ETD peptide identification with the Open Mass Spectrometry Search Algorithm (OMSSA). These peaks were also used to deduce the charge of the precursors for low resolution data. The scheme is a concrete example of implementing known ETD fragmentation features to improve a protein identification algorithm.     

Precision mapping of the human O‐GalNAc glycoproteome through SimpleCell technology

Glycosylation is the most abundant and diverse posttranslational modification of proteins. While several types of glycosylation can be predicted by the protein sequence context, and substantial knowledge of these glycoproteomes is available, our knowledge of the GalNAc‐type O‐glycosylation is highly limited. This type of glycosylation is unique in being regulated by 20 polypeptide GalNAc‐transferases attaching the initiating GalNAc monosaccharides to Ser and Thr (and likely some Tyr) residues. We have developed a genetic engineering approach using human cell lines to simplify O‐glycosylation (SimpleCells) that enables proteome‐wide discovery of O‐glycan sites using ‘bottom‐up’ ETD‐based mass spectrometric analysis. We implemented this on 12 human cell lines from different organs, and present a first map of the human O‐glycoproteome with almost 3000 glycosites in over 600 O‐glycoproteins as well as an improved NetOGlyc4.0 model for prediction of O‐glycosylation. The finding of unique subsets of O‐glycoproteins in each cell line provides evidence that the O‐glycoproteome is differentially regulated and dynamic. The greatly expanded view of the O‐glycoproteome should facilitate the exploration of how site‐specific O‐glycosylation regulates protein function.     

Modeling to discern nitrogen fertilization impacts on carbon sequestration in a Pacific Northwest Douglas‐fir forest in the first‐postfertilization year

This study investigated how nitrogen (N) fertilization with 200 kg N ha^?1 of urea affected ecosystem carbon (C) sequestration in the first‐postfertilization year in a Pacific Northwest Douglas‐fir (Pseudotsuga menziesii) stand on the basis of multiyear eddy‐covariance (EC) and soil‐chamber measurements before and after fertilization in combination with ecosystem modeling. The approach uses a data‐model fusion technique which encompasses both model parameter optimization and data assimilation and minimizes the effects of interannual climatic perturbations and focuses on the biotic and abiotic factors controlling seasonal C fluxes using a prefertilization 9‐year‐long time series of EC data (1998–2006). A process‐based ecosystem model was optimized using the half‐hourly data measured during 1998–2005, and the optimized model was validated using measurements made in 2006 and further applied to predict C fluxes for 2007 assuming the stand was not fertilized. The N fertilization effects on C sequestration were then obtained as differences between modeled (unfertilized stand) and EC or soil‐chamber measured (fertilized stand) C component fluxes. Results indicate that annual net ecosystem productivity in the first‐post‐N fertilization year increased by～83%, from 302 ± 19 to 552 ± 36 g m^?2 yr^?1, which resulted primarily from an increase in annual gross primary productivity of～8%, from 1938 ± 22 to 2095 ± 29 g m^?2 yr^?1 concurrent with a decrease in annual ecosystem respiration (R_e) of～5.7%, from 1636 ± 17 to 1543 ± 31 g m^?2 yr^?1. Moreover, with respect to respiration, model results showed that the fertilizer‐induced reduction in R_e (～93 g m^?2 yr^?1) principally resulted from the decrease in soil respiration R_s (～62 g m^?2 yr^?1).     

Improved identification of <Emphasis Type="Italic">O</Emphasis>-linked glycopeptides from ETD data with optimized scoring for different charge states and cleavage specificities

This article describes the effect of re-interrogation of electron-transfer dissociation (ETD) data with newly developed analytical tools. MS/MS-based characterization of O-linked glycopeptides is discussed using data acquired from a complex mixture of O-linked glycopeptides, featuring mucin core 1-type carbohydrates with and without sialic acid, as well as after partial deglycosylation to leave only the core GalNAc units (Darula and Medzihradszky in Mol Cell Proteomics 8:2515, 2009). Information content of collision-induced dissociation spectra generated in collision cell (in QqTOF instruments) and in ion traps is compared. Interpretation of the corresponding ETD data using Protein Prospector is also presented. Search results using scoring based on the frequency of different fragment ions occurring in ETD spectra of tryptic peptides are compared with results obtained after ion weightings were adjusted to accommodate differential ion frequencies in spectra of differing charge states or cleavage specificities. We show that the improved scoring is more than doubled the glycopeptide assignments under very strict acceptance criteria. This study illustrates that “old” proteomic data may yield significant new information when re-interrogated with new, improved tools.     

Improving Software Performance for Peptide Electron Transfer Dissociation Data Analysis by Implementation of Charge State- and Sequence-Dependent Scoring

The use of electron transfer dissociation (ETD) fragmentation for analysis of peptides eluting in liquid chromatography tandem mass spectrometry experiments is increasingly common and can allow identification of many peptides and proteins in complex mixtures. Peptide identification is performed through the use of search engines that attempt to match spectra to peptides from proteins in a database. However, software for the analysis of ETD fragmentation data is currently less developed than equivalent algorithms for the analysis of the more ubiquitous collision-induced dissociation fragmentation spectra. In this study, a new scoring system was developed for analysis of peptide ETD fragmentation data that varies the ion type weighting depending on the precursor ion charge state and peptide sequence. This new scoring regime was applied to the analysis of data from previously published results where four search engines (Mascot, Open Mass Spectrometry Search Algorithm (OMSSA), Spectrum Mill, and X!Tandem) were compared (Kandasamy, K., Pandey, A., and Molina, H. (2009) Evaluation of several MS/MS search algorithms for analysis of spectra derived from electron transfer dissociation experiments. Anal. Chem. 81, 7170–7180). Protein Prospector identified 80% more spectra at a 1% false discovery rate than the most successful alternative searching engine in this previous publication. These results suggest that other search engines would benefit from the application of similar rules.The recently developed fragmentation approach of electron transfer dissociation (ETD)¹ has become a genuine alternative to the more ubiquitous collision-induced dissociation (CID) for high throughput and high sensitivity proteomic analysis (1–3). ETD (4) and the related fragmentation process electron capture dissociation (ECD) (5) have been demonstrated to have particular advantages for the analysis of large peptides and small proteins (6–8) as well as the analysis of peptides bearing labile post-translational modifications (9–11). The results achieved through ETD and ECD analysis have been shown to be highly complementary to those obtained through CID fragmentation analysis, both through increasing confidence in particular identifications of peptides and also by allowing identification of extra components in complex mixtures (10, 12, 13). As CID and ETD can be sequentially or alternatively performed on precursor ions in the same mass spectrometric run, it is expected that the combined use of these two fragmentation analysis techniques will become increasingly common to enable more comprehensive sample analysis.Software for analysis of CID spectra is significantly more advanced than that for ECD/ETD data. This is partly because the behavior of peptides under CID fragmentation is better characterized and understood so software has been developed that is better able to predict the fragment ions expected. The fragment ion types observed in ETD and ECD are largely known (5, 14, 15), but information about the frequency and peak intensities of the different ion types observed is less well documented.We recently performed a study to characterize how frequently the different fragment ion types are detected in ETD spectra when analyzing complex digest mixtures produced by proteolytic enzymes or chemical cleavage reagents of different sequence specificity (16). These results were analyzed with respect to precursor charge state and location of basic residues, which were both shown to be significant factors in controlling the fragment ion types observed. The results showed that ETD spectra of doubly charged precursor ions produced very different fragment ions depending on the location of a basic residue in the sequence.Based on this statistical analysis of ETD data from a diverse range of peptides (16), in the present study, a new scoring system was developed and implemented in the search engine Batch-Tag within Protein Prospector that adjusts the weighting for different fragment ion types based on the precursor charge state and the presence of basic amino acid residues at either peptide terminus. The results using this new scoring system were compared with the previous generation of Batch-Tag, which used ion score weightings based on the average frequency of observation of different fragment types in ETD spectra of tryptic peptides and used the same scoring irrespective of precursor charge and sequence. The performance of this new scoring was also compared with those reported by other search engines using results previously published from a large standard data set (17). The new scoring system allowed identification of significantly more spectra than achieved with the previous scoring system. It also assigned 80% more spectra than the most successful of the compared search engines when using the same false discovery rate threshold.     

Contributions of water transfer energy to protein‐ligand association and dissociation barriers: Watermap analysis of a series of p38α MAP kinase inhibitors

In our previous work, we proposed that desolvation and resolvation of the binding sites of proteins can serve as the slowest steps during ligand association and dissociation, respectively, and tested this hypothesis on two protein‐ligand systems with known binding kinetics behavior. In the present work, we test this hypothesis on another kinetically‐determined protein‐ligand system—that of p38α and eight Type II BIRB 796 inhibitor analogs. The k_on values among the inhibitor analogs are narrowly distributed (10⁴ ≤ k_on ≤ 10⁵ M^?1 s^?1), suggesting a common rate‐determining step, whereas the k_off values are widely distributed (10^?1 ≤ k_off ≤ 10^?6 s^?1), suggesting a spectrum of rate‐determining steps. We calculated the solvation properties of the DFG‐out protein conformation using an explicit solvent molecular dynamics simulation and thermodynamic analysis method implemented in WaterMap to predict the enthalpic and entropic costs of water transfer to and from bulk solvent incurred upon association and dissociation of each inhibitor. The results suggest that the rate‐determining step for association consists of the transfer of a common set of enthalpically favorable solvating water molecules from the binding site to bulk solvent. The rate‐determining step for inhibitor dissociation consists of the transfer of water from bulk solvent to specific binding site positions that are unfavorably solvated in the apo protein, and evacuated during ligand association. Different sets of unfavorable solvation are evacuated by each ligand, and the observed dissociation barriers are qualitatively consistent with the calculated solvation free energies of those sets.     

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

LC‐MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re‐assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.     

The utility of osteon shape and circularity for differentiating human and non-human Haversian bone

Distinguishing human from non‐human bone fragments is usually accomplished by observation of gross morphology. When macroscopic analysis is insufficient, histological approaches can be applied. Microscopic features, like plexiform bone or osteon banding, are characteristic of non‐humans. In the absence of such features, distinguishing Haversian bone as either human or non‐human proves problematic. This study proposes a histomorphometric approach for classifying species from Haversian bone. Two variables, osteon area (On.Ar.) and circularity (On.Cr.), are examined. Measurements were collected from three species (deer, dog, human) represented by various skeletal elements; only ribs were available for humans (ribs: deer n = 6, dog n = 6, human n = 26; humeri: deer n = 6, dog n = 6; femora: deer n = 6, dog n = 6). Qualitative analysis comparing human to non‐human On.Ar. demonstrated that human ribs have larger mean On.Ar. (0.036 mm²) than non‐human ribs (deer = 0.017 mm², dog = 0.013 mm²). On.Cr. in the ribs showed minor differences between species (deer = 0.877; dog = 0.885; human = 0.898). Results demonstrated no significant difference across long bone quadrants in long bones. Discriminant analyses run on the means for each sample demonstrated overlap in deer and dog samples, clustering the non‐human and human groups apart from each other. Mean On.Cr. proved a poor criterion (ribs only: 76.3%, pooled elements: 66.1%), while mean On.Ar. proved useful in identifying human from non‐human samples (ribs only: 92.1%, pooled elements: 93.5%). When variables were combined, accuracy increased to 100% correct classification for rib data and 98.4% when considering data from all elements. These results indicate that On.Ar. and On.Cr. are valuable histomorphometric tools for distinguishing human from non‐human Haversian bone. Am J Phys Anthropol, 2012. © 2012 Wiley Periodicals, Inc.     

Effectiveness of CID, HCD, and ETD with FT MS/MS for degradomic-peptidomic analysis: comparison of peptide identification methods

We report on the effectiveness of CID, HCD, and ETD for LC-FT MS/MS analysis of peptides using a tandem linear ion trap-Orbitrap mass spectrometer. A range of software tools and analysis parameters were employed to explore the use of CID, HCD, and ETD to identify peptides (isolated from human blood plasma) without the use of specific "enzyme rules". In the evaluation of an FDR-controlled SEQUEST scoring method, the use of accurate masses for fragments increased the number of identified peptides (by ~50%) compared to the use of conventional low accuracy fragment mass information, and CID provided the largest contribution to the identified peptide data sets compared to HCD and ETD. The FDR-controlled Mascot scoring method provided significantly fewer peptide identifications than SEQUEST (by 1.3-2.3 fold) and CID, HCD, and ETD provided similar contributions to identified peptides. Evaluation of de novo sequencing and the UStags method for more intense fragment ions revealed that HCD afforded more contiguous residues (e.g., ≥ 7 amino acids) than either CID or ETD. Both the FDR-controlled SEQUEST and Mascot scoring methods provided peptide data sets that were affected by the decoy database used and mass tolerances applied (e.g., identical peptides between data sets could be limited to ~70%), while the UStags method provided the most consistent peptide data sets (>90% overlap). The m/z ranges in which CID, HCD, and ETD contributed the largest number of peptide identifications were substantially overlapping. This work suggests that the three peptide ion fragmentation methods are complementary and that maximizing the number of peptide identifications benefits significantly from a careful match with the informatics tools and methods applied. These results also suggest that the decoy strategy may inaccurately estimate identification FDRs.     

Strategies for identifying new prions in yeast

The unexpected discovery of two prions, [URE3] and [PSI⁺], in Saccharomyces cerevisiae led to questions about how many other proteins could undergo similar prion-based structural conversions. However, [URE3] and [PSI⁺] were discovered by serendipity in genetic screens. Cataloging the full range of prions in yeast or in other organisms will therefore require more systematic search methods. Taking advantage of some of the unique features of prions, various researchers have developed bioinformatic and experimental methods for identifying novel prion proteins. These methods have generated long lists of prion candidates. The systematic testing of some of these prion candidates has led to notable successes; however, even in yeast, where rapid growth rate and ease of genetic manipulation aid in testing for prion activity, such candidate testing is laborious. Development of better methods to winnow the field of prion candidates will greatly aid in the discovery of new prions, both in yeast and in other organisms, and help us to better understand the role of prions in biology.     

Tracing metabolic pathways of lipid biosynthesis in ectomycorrhizal fungi from position‐specific 13C‐labelling in glucose

Six position‐specific ¹³C‐labelled isotopomers of glucose were supplied to the ectomycorrhizal fungi Suillus pungens and Tricholoma flavovirens. From the resulting distribution of ¹³C among fungal PLFAs, the overall order and contribution of each glucose atom to fatty acid ¹³C enrichment was: C6 (～31%) > C5 (～25%) > C1 (～18%) > C2 (～18%) > C3 (～8%) > C4 (～1%). These data were used to parameterize a metabolic model of the relative fluxes from glucose degradation to lipid synthesis. Our data revealed that a higher amount of carbon is directed to glycolysis than to the oxidative pentose phosphate pathway (60% and 40% respectively) and that a significant part flows through these pathways more than once (73%) due to the reversibility of some glycolysis reactions. Surprisingly, 95% of carbon cycled through glyoxylate prior to incorporation into lipids, possibly to consume the excess of acetyl‐CoA produced during fatty acid turnover. Our approach provides a rigorous framework for analysing lipid biosynthesis in fungi. In addition, this approach could ultimately improve the interpretation of isotopic patterns at natural abundance in field studies.     

High-throughput Database Search and Large-scale Negative Polarity Liquid Chromatography–Tandem Mass Spectrometry with Ultraviolet Photodissociation for Complex Proteomic Samples

The use of ultraviolet photodissociation (UVPD) for the activation and dissociation of peptide anions is evaluated for broader coverage of the proteome. To facilitate interpretation and assignment of the resulting UVPD mass spectra of peptide anions, the MassMatrix database search algorithm was modified to allow automated analysis of negative polarity MS/MS spectra. The new UVPD algorithms were developed based on the MassMatrix database search engine by adding specific fragmentation pathways for UVPD. The new UVPD fragmentation pathways in MassMatrix were rigorously and statistically optimized using two large data sets with high mass accuracy and high mass resolution for both MS¹ and MS² data acquired on an Orbitrap mass spectrometer for complex Halobacterium and HeLa proteome samples. Negative mode UVPD led to the identification of 3663 and 2350 peptides for the Halo and HeLa tryptic digests, respectively, corresponding to 655 and 645 peptides that were unique when compared with electron transfer dissociation (ETD), higher energy collision-induced dissociation, and collision-induced dissociation results for the same digests analyzed in the positive mode. In sum, 805 and 619 proteins were identified via UVPD for the Halobacterium and HeLa samples, respectively, with 49 and 50 unique proteins identified in contrast to the more conventional MS/MS methods. The algorithm also features automated charge determination for low mass accuracy data, precursor filtering (including intact charge-reduced peaks), and the ability to combine both positive and negative MS/MS spectra into a single search, and it is freely open to the public. The accuracy and specificity of the MassMatrix UVPD search algorithm was also assessed for low resolution, low mass accuracy data on a linear ion trap. Analysis of a known mixture of three mitogen-activated kinases yielded similar sequence coverage percentages for UVPD of peptide anions versus conventional collision-induced dissociation of peptide cations, and when these methods were combined into a single search, an increase of up to 13% sequence coverage was observed for the kinases. The ability to sequence peptide anions and cations in alternating scans in the same chromatographic run was also demonstrated. Because ETD has a significant bias toward identifying highly basic peptides, negative UVPD was used to improve the identification of the more acidic peptides in conjunction with positive ETD for the more basic species. In this case, tryptic peptides from the cytosolic section of HeLa cells were analyzed by polarity switching nanoLC-MS/MS utilizing ETD for cation sequencing and UVPD for anion sequencing. Relative to searching using ETD alone, positive/negative polarity switching significantly improved sequence coverages across identified proteins, resulting in a 33% increase in unique peptide identifications and more than twice the number of peptide spectral matches.The advent of new high-performance tandem mass spectrometers equipped with the most versatile collision- and electron-based activation methods and ever more powerful database search algorithms has catalyzed tremendous progress in the field of proteomics (1–4). Despite these advances in instrumentation and methodologies, there are few methods that fully exploit the information available from the acidic proteome or acidic regions of proteins. Typical high-throughput, bottom-up workflows consist of the chromatographic separation of complex mixtures of digested proteins followed by online mass spectrometry (MS) and MSⁿ analysis. This bottom-up approach remains the most popular strategy for protein identification, biomarker discovery, quantitative proteomics, and elucidation of post-translational modifications. To date, proteome characterization via mass spectrometry has overwhelmingly focused on the analysis of peptide cations (5), resulting in an inherent bias toward basic peptides that easily ionize under acidic mobile phase conditions and positive polarity MS settings. Given that ∼50% of peptides/proteins are naturally acidic (6) and that many of the most important post-translational modifications (e.g. phosphorylation, acetylation, sulfonation, etc.) significantly decrease the isoelectric points of peptides (7, 8), there is a compelling need for better analytical methodologies for characterization of the acidic proteome.A principal reason for the shortage of methods for peptide anion characterization is the lack of MS/MS techniques suitable for the efficient and predictable dissociation of peptide anions. Although there are a growing array of new ion activation methods for the dissociation of peptides, most have been developed for the analysis of positively charged peptides. Collision-induced dissociation (CID)¹ of peptide anions, for example, often yields unpredictable or uninformative fragmentation behavior, with spectra dominated by neutral losses from both precursor and product ions (9), resulting in insufficient peptide sequence information. The two most promising new electron-based methods, electron-capture dissociation and electron-transfer dissociation (ETD), are applicable only to positively charged ions, not to anions (10–13). Because of the known inadequacy of CID and the lack of feasibility of electron-capture dissociation and ETD for peptide anion sequencing, several alternative MSⁿ methods have been developed recently. Electron detachment dissociation using high-energy electrons to induce backbone cleavages was developed for peptide anions (14, 15). Another new technique, negative ETD, entails reactions of radical cation reagents with peptide anions to promote electron transfer from the peptide to the reagent that causes radical-directed dissociation (16, 17). Activated-electron photodetachment dissociation, an MS³ technique, uses UV irradiation to produce intact peptide radical anions, which are then collisionally activated (18, 19). Although they represent inroads in the characterization of peptide anions, these methods also suffer from several significant shortcomings. Electron detachment dissociation and activated-electron photodetachment dissociation are both low-efficiency methods that require long averaging cycles and activation times that range from half a second to multiple seconds, impeding the integration of these methods with chromatographic timescales (14–19). In addition, the fragmentation patterns frequently yield many high-abundance neutral losses from product ions, which clutter the spectra (14–17), and few sequence ions (14, 18, 19). Recently, we reported the use of 193-nm photons (ultraviolet photodissociation (UVPD)) for peptide anion activation, which was shown to yield rich and predictable fragmentation patterns with high sequence coverage on a fast liquid chromatographic timeline (20). This method showed promise for a range of peptide charge states (i.e. from 3- to 1-), as well as for both unmodified and phosphorylated species.Several widely used or commercial database searching techniques are available for automated “bottom-up” analysis of peptide cations; SEQUEST (21), MASCOT (22), OMSSA (23), X! Tandem (24), and MASPIC (25) are all popular choices and yield comparable results (26). MassMatrix (27), a recently introduced searching algorithm, uses a mass accuracy sensitive probability-based scoring scheme for both the total number of matched product ions and the total abundance of matched products. This searching method also utilizes LC retention times to filter false positive peptide matches (28) and has been shown to yield results comparable to or better than those obtained with SEQUEST, MASCOT, OMSSA, and X! Tandem (29). Despite the ongoing innovation in automated peptide cation analysis, there is a lack of publically available methods for automated peptide anion analysis.In this work, we have modified the mass accuracy sensitive probabilistic MassMatrix algorithms to allow database searching of negative polarity MS/MS spectra. The algorithm is specific to the fragmentation behavior generated from 193-nm UVPD of peptide anions. The UVPD pathways in MassMatrix were rigorously and statistically optimized using two large data sets with high mass accuracy and high mass resolution for both MS¹ and MS² data acquired on an Orbitrap mass spectrometer for complex HeLa and Halo proteome samples. For low mass accuracy/low mass resolution data, we also incorporated a charge-state-filtering algorithm that identifies the charge state of each MS/MS spectrum based on the fragmentation patterns prior to searching. MassMatrix not only can analyze both positive and negative polarity LC-MS/MS files separately, but also can combine files from different polarities and different dissociation methods into a single search, thus maximizing the information content for a given proteomics experiment. The explicit incorporation of mass accuracy in the scores for the UVPD MS/MS spectra of peptide anions increases peptide assignments and identifications. Finally, we showcase the utility of integrating MassMatrix searching with positive/negative polarity MS/MS switching (i.e. data-dependent positive ETD and negative UVPD during a single proteomic LC-MS/MS run). MassMatrix is available to the public as a free search engine online.     

Enhancement of insulin-induced PI3K/Akt/GSK-3beta and ERK signaling by neuronal nicotinic receptor/PKC-alpha/ERK pathway: up-regulation of IRS-1/-2 mRNA and protein in adrenal chromaffin cells

In cultured bovine adrenal chromaffin cells treated with nicotine (10 µm for 24 h), phosphorylation of Akt, glycogen synthase kinase‐3β (GSK‐3β) and extracellular signal‐regulated kinase (ERK)1/2 induced by insulin (100 nm for 10 min) was enhanced by ～ 62%, without altering levels of these protein kinases. Nicotine produced time (> 12 h)‐ and concentration (EC₅₀ 3.6 and 13 µm )‐dependent increases in insulin receptor substrate (IRS)‐1 and IRS‐2 levels by ～ 125 and 105%, without altering cell surface density of insulin receptors. In these cells, insulin‐induced tyrosine phosphorylation of IRS‐1/IRS‐2 and recruitment of phosphoinositide 3‐kinase (PI3K) to IRS‐1/IRS‐2 were augmented by ～ 63%. The increase in IRS‐1/IRS‐2 levels induced by nicotine was prevented by nicotinic acetylcholine receptor (nAChR) antagonists, the Ca²⁺ chelator 1,2‐bis(2‐aminophenoxy)‐ethane‐N,N,N′,N′‐tetra‐acetic acid tetrakis‐acetoxymethyl ester, cycloheximide or actinomycin D. Nicotine increased IRS‐1 and IRS‐2 mRNA levels by ～ 57 and ～ 50%, and this was prevented by conventional protein kinase C (cPKC) inhibitor Gö6976, or ERK kinase inhibitors PD98059 and U0126. Nicotine phosphorylated cPKC‐α, thereby increasing phosphorylation of ERK1/ERK2, as demonstrated by using Gö6976, PD98059 or U0126. Selective activation of cPKC‐α by thymeleatoxin mimicked these effects of nicotine. Thus, stimulation of nAChRs up‐regulated expression of IRS‐1/IRS‐2 via Ca²⁺‐dependent sequential activation of cPKC‐α and ERK, and enhanced insulin‐induced PI3K/Akt/GSK‐3β and ERK signaling pathways.     

The Aspergillus nidulans syntaxin PepAPep12 is regulated by two Sec1/Munc‐18 proteins to mediate fusion events at early endosomes,late endosomes and vacuoles

Syntaxins are target‐SNAREs that crucially contribute to determine membrane compartment identity. Three syntaxins, Tlg2p, Pep12p and Vam3p, organize the yeast endovacuolar system. Remarkably, filamentous fungi lack the equivalent of the yeast vacuolar syntaxin Vam3p, making unclear how these organisms regulate vacuole fusion. We show that the nearly essential Aspergillus nidulans syntaxin PepA^Pep12, present in all endocytic compartments between early endosomes and vacuoles, shares features of Vam3p and Pep12p, and is capable of forming compositional equivalents of all known yeast endovacuolar SNARE bundles including that formed by yeast Vam3p for vacuolar fusion. Our data further indicate that regulation by two Sec1/Munc‐18 proteins, Vps45 in early endosomes and Vps33 in early and late endosomes/vacuoles contributes to the wide domain of PepA^Pep12 action. The syntaxin TlgB^Tlg2 localizing to the TGN appears to mediate retrograde traffic connecting post‐Golgi (sorting) endosomes with the TGN. TlgB^Tlg2 is dispensable for growth but becomes essential if the early Golgi syntaxin SedV^Sed5 is compromised, showing that the Golgi can function with a single syntaxin, SedV^Sed5. Remarkably, its pattern of associations with endosomal SNAREs is consistent with SedV^Sed5 playing roles in retrograde pathway(s) connecting endocytic compartments downstream of the post‐Golgi endosome with the Golgi, besides more conventional intra‐Golgi roles.