ABSTRACT: This is a top-down MS data set with 2 027 CID and 2 027 electron-transfer dissociation (ETD) MS/MS spectra from Escherichia coli (EC) K-12 MG1655. The EC sample was analyzed by a liquid chromatography system coupled with an LTQ Orbitrap Velos mass spectrometer. MS and MS/MS spectra were collected at a 60 000 resolution and the top 4 ions in each MS spectrum were selected for MS/MS analysis.
Project description:High-throughput top-down proteomic experiments directly identify proteoforms in complex mixtures, making high quality tandem mass spectra necessary to deeply characterize proteins with many sources of variation. Collision-based dissociation methods offer expedient data acquisition but often fail to extensively fragment proteoforms for thorough analysis. Electron-driven dissociation methods are a popular alternative approach, especially for precursor ions with high charge density. Combining infrared photoactivation concurrent with electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), can significantly improve ETD characterization of intact proteins, but benefits of AI-ETD have yet to be quantified in high-throughput top-down proteomics. Here, we report the first application of AI-ETD to LC-MS/MS characterization of intact proteins (<20 kDa), highlighting improved proteoform identification the method offers over higher energy-collisional dissociation (HCD), standard ETD, and ETD followed by supplemental HCD activation (EThcD). We identified 935 proteoforms from 295 proteins from human colorectal cancer cell line HCT116 using AI-ETD compared to 1014 proteoforms, 915 proteoforms, and 871 proteoforms with HCD, ETD, and EThcD, respectively. Importantly, AI-ETD outperformed each of the three other methods in MS/MS success rates and spectral quality metrics (e.g., sequence coverage achieved and proteoform characterization scores). In all, this four-method analysis offers the most extensive comparisons to date and demonstrates that AI-ETD both increases identifications over other ETD methods and improves proteoform characterization via higher sequence coverage, positioning it as a premier method for high-throughput top-down proteomics.
Project description:The analysis of intact proteins via mass spectrometry can offer several benefits to proteome characterization, although the majority of top-down experiments focus on proteoforms in a relatively low mass range (<30 kDa). Recent studies have focused on improving the analysis of larger intact proteins (up to ~75 kDa), but they have also highlighted several challenges to be addressed. One major hurdle is the efficient dissociation of larger protein ions, which often to do not yield extensive fragmentation via conventional tandem MS methods. Here we describe the first application of activated ion electron transfer dissociation (AI-ETD) to proteins in the 30-70 kDa range. AI-ETD leverages infrared photo-activation concurrent to ETD reactions to improve sequence-informative product ion generation. This method generates more product ions and greater sequence coverage than conventional ETD, higher-energy collisional dissociation (HCD), and ETD combined with supplemental HCD activation (EThcD). Importantly, AI-ETD provides the most thorough protein characterization for every precursor ion charge state investigated in this study, making it suitable as a universal fragmentation method in top-down experiments. Additionally, we highlight several acquisition strategies that can benefit characterization of larger proteins with AI-ETD, including combination of spectra from multiple ETD reaction times for a given precursor ion, multiple spectral acquisitions of the same precursor ion, and combination of spectra from two different dissociation methods (e.g., AI-ETD and HCD). In all, AI-ETD shows great promise as a method for dissociating larger intact protein ions as top-down proteomics continues to advance into larger mass ranges. Graphical Abstract ?.
Project description:Full-length de novo sequencing of unknown proteins remains a challenging open problem. Traditional methods that sequence spectra individually are limited by short peptide length, incomplete peptide fragmentation, and ambiguous de novo interpretations. We address these issues by determining consensus sequences for assembled tandem mass (MS/MS) spectra from overlapping peptides (e.g., by using multiple enzymatic digests). We have combined electron-transfer dissociation (ETD) with collision-induced dissociation (CID) and higher-energy collision-induced dissociation (HCD) fragmentation methods to boost interpretation of long, highly charged peptides and take advantage of corroborating b/y/c/z ions in CID/HCD/ETD. Using these strategies, we show that triplet CID/HCD/ETD MS/MS spectra from overlapping peptides yield de novo sequences of average length 70 AA and as long as 200 AA at up to 99% sequencing accuracy.
Project description:For structural identification of glycans, the classic collision-induced dissociation (CID) spectra are dominated by product ions that derived from glycosidic cleavages, which provide only sequence information. The peaks from cross-ring fragmentation are often absent or have very low abundances in such spectra. Electron transfer dissociation (ETD) is being applied to structural identification of carbohydrates for the first time, and results in some new and detailed information for glycan structural studies. A series of linear milk sugars was analyzed by a variety of fragmentation techniques such as MS/MS by CID and ETD, and MS(3) by sequential CID/CID, CID/ETD, and ETD/CID. In CID spectra, the detected peaks were mainly generated via glycosidic cleavages. By comparison, ETD generated various types of abundant cross-ring cleavage ions. These complementary cross-ring cleavages clarified the different linkage types and branching patterns of the representative milk sugar samples. The utilization of different MS(3) techniques made it possible to verify initial assignments and to detect the presence of multiple components in isobaric peaks. Fragment ion structures and pathways could be proposed to facilitate the interpretation of carbohydrate ETD spectra, and the main mechanisms were investigated. ETD should contribute substantially to confident structural analysis of a wide variety of oligosaccharides.
Project description:Electron transfer dissociation (ETD) has improved the mass spectrometric analysis of proteins and peptides with labile post-translational modifications and larger intact masses. Here, the parameters governing the reaction rate of ETD are examined experimentally. Currently, due to reagent injection and isolation events as well as longer reaction times, ETD spectra require significantly more time to acquire than collision-induced dissociation (CID) spectra (>100 ms), resulting in a trade-off in the dynamic range of tandem MS analyses when ETD-based methods are compared to CID-based methods. Through fine adjustment of reaction parameters and the selection of reagents with optimal characteristics, we demonstrate a drastic reduction in the time taken per ETD event. In fact, ETD can be performed with optimal efficiency in nearly the same time as CID at low precursor charge state (z = +3) and becomes faster at higher charge state (z > +3).
Project description:Mass spectrometry (MS) instruments and experimental protocols are rapidly advancing, but de novo peptide sequencing algorithms to analyze tandem mass (MS/MS) spectra are lagging behind. Although existing de novo sequencing tools perform well on certain types of spectra [e.g. Collision Induced Dissociation (CID) spectra of tryptic peptides], their performance often deteriorates on other types of spectra, such as Electron Transfer Dissociation (ETD), Higher-energy Collisional Dissociation (HCD) spectra or spectra of non-tryptic digests. Thus, rather than developing a new algorithm for each type of spectra, we develop a universal de novo sequencing algorithm called UniNovo that works well for all types of spectra or even for spectral pairs (e.g. CID/ETD spectral pairs). UniNovo uses an improved scoring function that captures the dependences between different ion types, where such dependencies are learned automatically using a modified offset frequency function.The performance of UniNovo is compared with PepNovo+, PEAKS and pNovo using various types of spectra. The results show that the performance of UniNovo is superior to other tools for ETD spectra and superior or comparable with others for CID and HCD spectra. UniNovo also estimates the probability that each reported reconstruction is correct, using simple statistics that are readily obtained from a small training dataset. We demonstrate that the estimation is accurate for all tested types of spectra (including CID, HCD, ETD, CID/ETD and HCD/ETD spectra of trypsin, LysC or AspN digested peptides).UniNovo is implemented in JAVA and tested on Windows, Ubuntu and OS X machines. UniNovo is available at http://proteomics.ucsd.edu/Software/UniNovo.html along with the manual.
Project description: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.
Project description: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.
Project description:Thrombospondin type 1 repeats (TSRs), small adhesive protein domains with a wide range of functions, are usually modified with O-linked fucose, which may be extended to O-fucose-?1,3-glucose. Collision-induced dissociation (CID) spectra of O-fucosylated peptides cannot be sequenced by standard tandem mass spectrometry (MS/MS) sequence database search engines because O-linked glycans are highly labile in the gas phase and are effectively absent from the CID peptide fragment spectra, resulting in a large mass error. Electron transfer dissociation (ETD) preserves O-linked glycans on peptide fragments, but only a subset of tryptic peptides with low m/ z can be reliably sequenced from ETD spectra compared to CID. Accordingly, studies to date that have used MS to identify O-fucosylated TSRs have required manual interpretation of CID mass spectra even when ETD was also employed. In order to facilitate high-throughput, automatic identification of O-fucosylated peptides from CID spectra, we re-engineered the MS/MS sequence database search engine Comet and the MS data analysis suite Trans-Proteomic Pipeline to enable automated sequencing of peptides exhibiting the neutral losses characteristic of labile O-linked glycans. We used our approach to reanalyze published proteomics data from Plasmodium parasites and identified multiple glycoforms of TSR-containing proteins.
Project description:Electron transfer dissociation (ETD) is a valuable tool for protein sequence analysis, especially for the fragmentation of intact proteins. However, low product ion signal-to-noise often requires some degree of signal averaging to achieve high quality MS/MS spectra of intact proteins. Here we describe a new implementation of ETD on the newest generation of quadrupole-Orbitrap-linear ion trap Tribrid, the Orbitrap Fusion Lumos, for improved product ion signal-to-noise via ETD reactions on larger precursor populations. In this new high precursor capacity ETD implementation, precursor cations are accumulated in the center section of the high pressure cell in the dual pressure linear ion trap prior to charge-sign independent trapping, rather than precursor ion sequestration in only the back section as is done for standard ETD. This new scheme increases the charge capacity of the precursor accumulation event, enabling storage of approximately 3-fold more precursor charges. High capacity ETD boosts the number of matching fragments identified in a single MS/MS event, reducing the need for spectral averaging. These improvements in intra-scan dynamic range via reaction of larger precursor populations, which have been previously demonstrated through custom modified hardware, are now available on a commercial platform, offering considerable benefits for intact protein analysis and top down proteomics. In this work, we characterize the advantages of high precursor capacity ETD through studies with myoglobin and carbonic anhydrase.