large-scale-top-down-proteomics-using-capillary-zone-electrophoresis

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has been recognized as a useful tool for top-down ...

Indiana University-Purdue University Indianapolis

There are two approaches for de novo protein sequencing: Edman degradation and mass spectrometry (MS). Existing MS-based methods characterize a novel protein by assembling tandem mass spectra of overlapping peptides generated from multiple proteolytic digestions of the protein. Because each tandem mass spectrum covers only a short peptide of the target protein, the key to high coverage protein sequencing is to find spectral pairs from overlapping peptides in order to assemble tandem mass spectra to long ones. However, overlapping regions of peptides may be too short to be confidently identified. High-resolution mass spectrometers have become accessible to many laboratories. These mass spectrometers are capable of analyzing molecules of large mass values, boosting the development of top-down MS. Top-down tandem mass spectra cover whole proteins. However, top-down tandem mass spectra, even combined, rarely provide full ion fragmentation coverage of a protein. We propose an algorithm, TBNovo, for de novo protein sequencing by combining top-down and bottom-up MS. In TBNovo, a top-down tandem mass spectrum is utilized as a scaffold, and bottom-up tandem mass spectra are aligned to the scaffold to increase sequence coverage. Experiments on data sets of two proteins showed that TBNovo achieved high sequence coverage and high sequence accuracy. 

De novo protein sequencing by combining top-down and bottom-up tandem mass spectra.

Top-down mass spectrometry plays an important role in intact protein identification and characterization. Top-down mass spectra are more complex than bottom-up mass spectra because they often contain many isotopomer envelopes from highly charged ions, which may overlap with one another. As a result, spectral deconvolution, which converts a complex top-down mass spectrum into a monoisotopic mass list, is a key step in top-down spectral interpretation.

A new scoring function for top-down spectral deconvolution.

Various proteoforms may be generated from a single gene due to primary structure alterations (PSAs) such as genetic variations, alternative splicing, and post-translational modifications (PTMs). Top-down mass spectrometry is capable of analyzing intact proteins and identifying patterns of multiple PSAs, making it the method of choice for studying complex proteoforms. In top-down proteomics, proteoform identification is often performed by searching tandem mass spectra against a protein sequence database that contains only one reference protein sequence for each gene or transcript variant in a proteome. Because of the incompleteness of the protein database, an identified proteoform may contain unknown PSAs compared with the reference sequence. Proteoform characterization is to identify and localize PSAs in a proteoform. Although many software tools have been proposed for proteoform identification by top-down mass spectrometry, the characterization of proteoforms in identified proteoform-spectrum matches still relies mainly on manual annotation. We propose to use the Modification Identification Score (MIScore), which is based on Bayesian models, to automatically identify and localize PTMs in proteoforms. Experiments showed that the MIScore is accurate in identifying and localizing one or two modifications.

Characterization of Proteoforms with Unknown Post-translational Modifications Using the MIScore.

Top-down mass spectrometry enables the observation of whole complex proteoforms in biological samples and provides crucial information complementary to bottom-up mass spectrometry. Because of the complexity of top-down mass spectra and proteoforms, it is a challenging problem to efficiently interpret top-down tandem mass spectra in high-throughput proteome-level proteomics studies. We present TopPIC, a tool that efficiently identifies and characterizes complex proteoforms with unknown primary structure alterations, such as amino acid mutations and post-translational modifications, by searching top-down tandem mass spectra against a protein database.

TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization.

Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a 'bird's eye view' of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem.

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra.

Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has been recognized as an invaluable platform for top-down proteomics. However, the scale of top-down proteomics using CZE-MS/MS is still limited due to the low loading capacity and narrow separation window of CZE. In this work, for the first time we systematically evaluated the dynamic pH junction method for focusing of intact proteins during CZE-MS. The optimized dynamic pH junction-based CZE-MS/MS approached a 1 μL loading capacity, 90 min separation window, and high peak capacity (∼280) for characterization of an Escherichia coli proteome. The results represent the largest loading capacity and the highest peak capacity of CZE for top-down characterization of complex proteomes. Single-shot CZE-MS/MS identified about 2800 proteoform-spectrum matches, nearly 600 proteoforms, and 200 proteins from the Escherichia coli proteome with spectrum-level false discovery rate (FDR) less than 1%. The number of identified proteoforms in this work is over three times higher than that in previous single-shot CZE-MS/MS studies. Truncations, N-terminal methionine excision, signal peptide removal, and some post-translational modifications including oxidation and acetylation were detected.

Single-Shot Top-Down Proteomics with Capillary Zone Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry for Identification of Nearly 600 Escherichia coli Proteoforms.

Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top-down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top-down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum-based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top-down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome-level proteoform analyses.

Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top-Down Mass Spectrometry.

Database search is the main approach for identifying proteoforms using top-down tandem mass spectra. However, it is extremely slow to align a query spectrum against all protein sequences in a large database when the target proteoform that produced the spectrum contains post-translational modifications and/or mutations. As a result, efficient and sensitive protein sequence filtering algorithms are essential for speeding up database search. In this paper, we propose a novel filtering algorithm, which generates spectrum graphs from subspectra of the query spectrum and searches them against the protein database to find good candidates. Compared with the sequence tag and gaped tag approaches, the proposed method circumvents the step of tag extraction, thus simplifying data processing. Experimental results on real data showed that the proposed method achieved both high speed and high sensitivity in protein sequence filtration.

A Spectrum Graph-Based Protein Sequence Filtering Algorithm for Proteoform Identification by Top-Down Mass Spectrometry.

Capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS) has been recognized as a useful tool for top-down proteomics. However, its performance for deep top-down proteomics is still dramatically lower than widely used reversed-phase liquid chromatography (RPLC)-MS/MS. We present an orthogonal multidimensional separation platform that couples size exclusion chromatography (SEC) and RPLC based protein prefractionation to CZE-MS/MS for deep top-down proteomics of Escherichia coli. The platform generated high peak capacity (∼4000) for separation of intact proteins, leading to the identification of 5700 proteoforms from the Escherichia coli proteome. The data represents a 10-fold improvement in the number of proteoform identifications compared with previous CZE-MS/MS studies and represents the largest bacterial top-down proteomics data set reported to date. The performance of the CZE-MS/MS based platform is comparable to the state-of-the-art RPLC-MS/MS based systems in terms of the number of proteoform identifications and the instrument time.

Deep Top-Down Proteomics Using Capillary Zone Electrophoresis-Tandem Mass Spectrometry: Identification of 5700 Proteoforms from the Escherichia coli Proteome.

Native proteomics aims to characterize complex proteomes under native conditions and ultimately produces a full picture of endogenous protein complexes in cells. It requires novel analytical platforms for high-resolution and liquid-phase separation of protein complexes prior to native mass spectrometry (MS) and MS/MS. In this work, size-exclusion chromatography (SEC)-capillary zone electrophoresis (CZE)-MS/MS was developed for native proteomics in discovery mode, resulting in the identification of 144 proteins, 672 proteoforms, and 23 protein complexes from the Escherichia coli proteome. The protein complexes include four protein homodimers, 16 protein-metal complexes, two protein-[2Fe-2S] complexes, and one protein-glutamine complex. Half of them have not been reported in the literature. This work represents the first example of online liquid-phase separation-MS/MS for the characterization of a complex proteome under the native condition, offering the proteomics community an efficient and simple platform for native proteomics.

Article	Year
De novo protein sequencing by combining top-down and bottom-up tandem mass spectra. Journal of proteome research\| PubMed ID: 24874765	2014
A new scoring function for top-down spectral deconvolution. BMC genomics\| PubMed ID: 25523396	2014
Characterization of Proteoforms with Unknown Post-translational Modifications Using the MIScore. Journal of proteome research\| PubMed ID: 27291504	2016
TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization. Bioinformatics (Oxford, England)\| PubMed ID: 27423895	2016
A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra. Bioinformatics (Oxford, England)\| PubMed ID: 28453668	2017
Single-Shot Top-Down Proteomics with Capillary Zone Electrophoresis-Electrospray Ionization-Tandem Mass Spectrometry for Identification of Nearly 600 Escherichia coli Proteoforms. Analytical chemistry\| PubMed ID: 29064224	2017
Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top-Down Mass Spectrometry. Proteomics\| PubMed ID: 29327814	2018
A Spectrum Graph-Based Protein Sequence Filtering Algorithm for Proteoform Identification by Top-Down Mass Spectrometry. Proceedings. IEEE International Conference on Bioinformatics and Biomedicine\| PubMed ID: 29503761	2017
Deep Top-Down Proteomics Using Capillary Zone Electrophoresis-Tandem Mass Spectrometry: Identification of 5700 Proteoforms from the Escherichia coli Proteome. Analytical chemistry\| PubMed ID: 29620868	2018
Native Proteomics in Discovery Mode Using Size-Exclusion Chromatography-Capillary Zone Electrophoresis-Tandem Mass Spectrometry. Analytical chemistry\| PubMed ID: 30085653	2018

Qiang Kou

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