Expanding Proteoform Identifications in Top-Down Proteomic Analyses by Constructing Proteoform Families
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Saccharomyces Cerevisiae (ncbitaxon:4932)
SUBMITTER: 
Lloyd M. Smith
PROVIDER: MSV000081592 | MassIVE | Mon Oct 09 09:57:00 BST 2017
REPOSITORIES: MassIVE
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Project description:In top-down proteomics, intact proteins are analyzed by tandem mass spectrometry and proteoforms, which are defined forms of a protein with specific sequences of amino acids and localized post-translational modifications, are identified using precursor mass and fragmentation data. Many proteoforms that are detected in the precursor scan (MS1) are not selected for fragmentation by the instrument and therefore remain unidentified in typical top-down proteomic workflows. Our laboratory has developed the open source software program Proteoform Suite to analyze MS1-only intact proteoform data. Here, we have adapted it to provide identifications of proteoform masses in precursor MS1 spectra of top-down data, supplementing the top-down identifications obtained using the MS2 fragmentation data. Proteoform Suite performs mass calibration using high-scoring top-down identifications and identifies additional proteoforms using calibrated, accurate intact masses. Proteoform families, the set of proteoforms from a given gene, are constructed and visualized from proteoforms identified by both top-down and intact-mass analyses. Using this strategy, we constructed proteoform families and identified 1861 proteoforms in yeast lysate, yielding an approximately 40% increase over the original 1291 proteoform identifications observed using traditional top-down analysis alone.
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Project description:Cellular functions are performed by a vast and diverse set of proteoforms. Proteoforms are the specific forms of proteins produced as a result of genetic variations, RNA splicing, and post-translational modifications (PTMs). Top-down mass spectrometric analysis of intact proteins enables proteoform identification, including proteoforms derived from sequence cleavage events or harboring multiple PTMs. In contrast, bottom-up proteomics identifies peptides, which necessitates protein inference and does not yield proteoform identifications. We seek here to exploit the synergies between these two data types to improve the quality and depth of the overall proteomic analysis. To this end, we automated the large-scale integration of results from multiprotease bottom-up and top-down analyses in the software program Proteoform Suite and applied it to the analysis of proteoforms from the human Jurkat T lymphocyte cell line. We implemented the recently developed proteoform-level classification scheme for top-down tandem mass spectrometry (MS/MS) identifications in Proteoform Suite, which enables users to observe the level and type of ambiguity for each proteoform identification, including which of the ambiguous proteoform identifications are supported by bottom-up-level evidence. We used Proteoform Suite to find instances where top-down identifications aid in protein inference from bottom-up analysis and conversely where bottom-up peptide identifications aid in proteoform PTM localization. We also show the use of bottom-up data to infer proteoform candidates potentially present in the sample, allowing confirmation of such proteoform candidates by intact-mass analysis of MS1 spectra. The implementation of these capabilities in the freely available software program Proteoform Suite enables users to integrate large-scale top-down and bottom-up data sets and to utilize the synergies between them to improve and extend the proteomic analysis.
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Project description:This data set contains the following:
1) 66 raw files for NeuCode-labeled human proteoforms from the Jurkat cell line analyzed via "intact-mass" (i.e., LC-MS, with no precursor fragmentation). Three biological replicates were performed (files from the different replicates are labeled with 022317, 031617, or 031817, respectively). For each replicate, proteoforms were separated offline using a 12% Tris-acetate Gelfree cartridge and 11 fractions were collected. Two technical replicate LC-MS injections of each fraction were performed, yielding a total of 66 raw files (3 biological replicates x 11 fractions x 2 injections).
2) 22 raw files for label-free human proteoforms from the Jurkat cell line analyzed via "top-down" (i.e., LC-MS/MS, with precursor fragmentation). One biological replicate was performed (labeled 032017). Proteoforms were separated offline using a 12% Tris-acetate Gelfree cartridge and 11 fractions were collected. Two technical replicate LC-MS/MS injections of each fraction were performed, yielding a total of 22 raw files (1 biological replicate x 11 fractions x 2 injections).
3) Multi-protease and trypsin-only pruned G-PTM-D databases used for proteoform identification. Note that the bottom-up data used to generate these databases can be found elsewhere on MassIVE (MSV000083304).
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Project description:Intact mass analyses (HPLC-ESI-MS) of GELFREE separated fractions of yeast. A total of 66 raw files, representing two replicate analyses.
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Project description:Top-down mass spectrometry is capable of identifying whole proteoform sequences with multiple post-translational modifications because it generates tandem mass spectra directly from intact proteoforms. Many software tools, such as ProSightPC, MSPathFinder, and TopMG, have been proposed for identifying proteoforms with modifications. In these tools, various methods are employed to estimate the statistical significance of identifications. However, most existing methods are designed for proteoform identifications without modifications, and the challenge remains for accurately estimating the statistical significance of proteoform identifications with modifications. Here we propose TopMCMC, a method that combines a Markov chain random walk algorithm and a greedy algorithm for assigning statistical significance to matches between spectra and protein sequences with variable modifications. Experimental results showed that TopMCMC achieved high accuracy in estimating E-values and false discovery rates of identifications in top-down mass spectrometry. Coupled with TopMG, TopMCMC identified more spectra than the generating function method from an MCF-7 top-down mass spectrometry data set.
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Project description:Intact mass (66 files), top-down (18 files) and bottom-up (66 files) .raw files of GELFREE separated fractions of yeast.
2016-01-14 | MSV000079456 | MassIVE
Project description:We used reversed-phase liquid chromatography to separate the yeast proteome into 23 fractions. These fractions were then analyzed using capillary zone electrophoresis (CZE) coupled to a Q-Exactive HF mass spectrometer using an electrokinetically pumped sheath flow interface. The parameters of the mass spectrometer were first optimized for top-down proteomics using a mixture of seven model proteins; we observed that intact protein mode with a trapping pressure of 0.2 and normalized collision energy of 20% produced the highest intact protein signals and most protein identifications. Then, we applied the optimized parameters for analysis of the fractionated yeast proteome. From this, 580 proteoforms and 180 protein groups were identified via database searching of the MS/MS spectra. This number of proteoform identifications is two times larger than that of previous CZE-MS/MS studies. An additional 3,243 protein species were detected based on the parent ion spectra. Post-translational modifications including N-terminal acetylation, signal peptide removal, and oxidation were identified.
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Project description:1) 144 intact-mass and top-down raw LC-MS and LC-MS/MS files (myoblast and myotube, 3 biological replicates, 6 fractions, 2 top-down MS/MS technical replicates and 2 MS1-only technical replicates) from 12% GELFrEE fractions of C2C12 mitochondrial extracts. 2) 12 intact-mass raw LC-MS files from unfractionated C2C12 mitochondrial extracts (myoblast and myotube, 3 biological replicates, 2 MS1-only technical replicates). 3) 12 bottom-up raw LC-MS/MS files from unfractionated C2C12 mitochondrial extracts digested with trypsin (myoblast and myotube, 3 biological replicates, 2 technical replicates).
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Project description:Identification of proteoforms, the different forms of a protein, is important to understand biological processes. A proteoform family is the set of different proteoforms from the same gene. We previously developed the software program Proteoform Suite, which constructs proteoform families and identifies proteoforms by intact-mass analysis. Here, we have applied this approach to top-down proteomic data acquired at the National High Magnetic Field Laboratory 21 tesla Fourier transform ion cyclotron resonance mass spectrometer (data available on the MassIVE platform with identifier MSV000085978). We explored the ability to construct proteoform families and identify proteoforms from the high mass accuracy data that this instrument provides for a complex cell lysate sample from the MCF-7 human breast cancer cell line. There were 2830 observed experimental proteforms, of which 932 were identified, 44 were ambiguous, and 1854 were unidentified. Of the 932 unique identified proteoforms, 766 were identified by top-down MS2 analysis at 1% false discovery rate (FDR) using TDPortal, and 166 were additional intact-mass identifications (∼4.7% calculated global FDR) made using Proteoform Suite. We recently published a proteoform level schema to represent ambiguity in proteoform identifications. We implemented this proteoform level classification in Proteoform Suite for intact-mass identifications, which enables users to determine the ambiguity levels and sources of ambiguity for each intact-mass proteoform identification.
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