Native Proteomics in Discovery Mode Using Size-Exclusion Chromatography–Capillary Zone Electrophoresis–Tandem Mass Spectrometry

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 characterization of a complex proteome under the native condition, offering the proteomics community an efficient and simple platform for native proteomics

A Markov Chain Monte Carlo Method for Estimating the Statistical Significance of Proteoform Identifications by Top-Down Mass Spectrometry

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 <i>E</i>-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

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

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 <i>Escherichia coli</i>. The platform generated high peak capacity (∼4000) for separation of intact proteins, leading to the identification of 5700 proteoforms from the <i>Escherichia coli</i> 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 <i>Escherichia coli</i> Proteome

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 <i>Escherichia coli</i>. The platform generated high peak capacity (∼4000) for separation of intact proteins, leading to the identification of 5700 proteoforms from the <i>Escherichia coli</i> 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

<i>De Novo</i> Protein Sequencing by Combining Top-Down and Bottom-Up Tandem Mass Spectra

There are two approaches for <i>de novo</i> 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 <i>de novo</i> 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