Top-down Mass Spectrometry Data Research Articles

ProSight Native: Defining Protein Complex Composition from Native Top-Down Mass Spectrometry Data.

Native mass spectrometry has recently moved alongside traditional structural biology techniques in its ability to provide clear insights into the composition of protein complexes. However, to date, limited software tools are available for the comprehensive analysis of native mass spectrometry data on protein complexes, particularly for experiments aimed at elucidating the composition of an intact protein complex. Here, we introduce ProSight Native as a start-to-finish informatics platform for analyzing native protein and protein complex data. Combining mass determination via spectral deconvolution with a top-down database search and stoichiometry calculations, ProSight Native can determine the complete composition of protein complexes. To demonstrate its features, we used ProSight Native to successfully determine the composition of the homotetrameric membrane complex Aquaporin Z. We also revisited previously published spectra and were able to decipher the composition of a heterodimer complex bound with two noncovalently associated ligands. In addition to determining complex composition, we developed new tools in the software for validating native mass spectrometry fragment ions and mapping top-down fragmentation data onto three-dimensional protein structures. Taken together, ProSight Native will reduce the informatics burden on the growing field of native mass spectrometry, enabling the technology to further its reach.

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules.

Biotherapeutic masses are a means of verifying identity and structural integrity. Mass spectrometry (MS) of intact proteins or protein subunits provides an easy analytical tool for different stages of biopharmaceutical development. The protein's identity is confirmed when the experimental mass from MS is within a pre-defined mass error range of the theoretical mass. While several computational tools exist for the calculation of protein and peptide molecular weights, they either were not designed for direct application to biotherapeutic entities, have access limitations due to paid licenses, or require uploading protein sequences to host servers. We have developed a modular mass calculation routine that enables the easy determination of the average or monoisotopic masses and elemental compositions of therapeutic glycoproteins, including monoclonal antibodies (mAb), bispecific antibodies (bsAb), and antibody-drug conjugates (ADC). The modular nature of this Python-based calculation framework will allow the extension of this platform to other modalities such as vaccines, fusion proteins, and oligonucleotides in the future, and this framework could also be useful for the interrogation of top-down mass spectrometry data. By creating an open-source standalone desktop application with a graphical user interface (GUI), we hope to overcome the restrictions around use in environments where proprietary information cannot be uploaded to web-based tools. This article describes the algorithms and application of this tool, mAbScale, to different antibody-based therapeutic modalities.

Top-Down Mass Spectrometry Data Analysis Using TopPIC Suite.

With the advances of mass spectrometry (MS) techniques, top-down MS-based proteomics has gained increasing attention because of its advantages over bottom-up MS in studying complex proteoforms. TopPIC Suite is a widely used software package for top-down MS-based proteoform identification and quantification. Here, we present the methods for top-down MS data analysis using TopPIC Suite.

Characterization of Structural Hemoglobin Variants by Top-Down Mass Spectrometry and R Programming Tools for Rapid Identification.

Hemoglobinopathies are one of the most prevalent genetic disorders, affecting millions throughout the world. These are caused by pathogenic variants in genes that control the production of hemoglobin (Hb) subunits. As the number of known Hb variants has increased, it has become more challenging to obtain unambiguous results from routine chromatographic assays employed in the clinical laboratory. Top-down proteomic analysis of Hb by mass spectrometry is a definitive method to directly characterize the sequences of intact subunits. Here, we apply "chimeric ion loading" to characterize Hb β subunit variants. In this technique, product ions derived from complementary dissociation techniques are accumulated in a multipole storage device before delivery to a 21 T Fourier-transform ion cyclotron resonance mass spectrometer for simultaneous detection. To further improve the efficiency of identification of Hb variants and localization of the mutation site(s), we developed an R programming script, "Variants Identifier", to search top-down data against a database containing accurate intact mass differences and diagnostic ions from investigated Hb variants. A second R script, "PredictDiag", was developed and employed to determine relevant diagnostic ions for additional Hb variants with known sequences. These two R scripts were successfully applied to the identification of a Hb δ-β fusion protein and other Hb variants. The combination of chimeric ion loading and the above R scripts enables rapid and reliable interpretation of top-down mass spectrometry data, regardless of activation type, for Hb variant identification.

Proteoform Identification by Combining RNA-Seq and Top-Down Mass Spectrometry.

In proteogenomic studies, genomic and transcriptomic variants are incorporated into customized protein databases for the identification of proteoforms, especially proteoforms with sample-specific variants. Most proteogenomic research has been focused on combining genomic or transcriptomic data with bottom-up mass spectrometry data. In the last decade, top-down mass spectrometry has attracted increasing attention because of its capacity to identify various proteoforms with alterations. However, top-down proteogenomics, in which genomic or transcriptomic data are combined with top-down mass spectrometry data, has not been widely adopted, and there is still a lack of software tools for top-down proteogenomic data analysis. In this paper, we introduce TopPG, a proteogenomic tool for generating proteoform sequence databases with genetic alterations and alternative splicing events. Experiments on top-down proteogenomic data of DLD-1 colorectal cancer cells showed that TopPG coupled with database search confidently identified proteoforms with sample-specific alterations.

EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution.

Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.

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 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.

Evaluation of top-down mass spectral identification with homologous protein sequences

BackgroundTop-down mass spectrometry has unique advantages in identifying proteoforms with multiple post-translational modifications and/or unknown alterations. Most software tools in this area search top-down mass spectra against a protein sequence database for proteoform identification. When the species studied in a mass spectrometry experiment lacks its proteome sequence database, a homologous protein sequence database can be used for proteoform identification. The accuracy of homologous protein sequences affects the sensitivity of proteoform identification and the accuracy of mass shift localization.ResultsWe tested TopPIC, a commonly used software tool for top-down mass spectral identification, on a top-down mass spectrometry data set of Escherichia coli K12 MG1655, and evaluated its performance using an Escherichia coli K12 MG1655 proteome database and a homologous protein database. The number of identified spectra with the homologous database was about half of that with the Escherichia coli K12 MG1655 database. We also tested TopPIC on a top-down mass spectrometry data set of human MCF-7 cells and obtained similar results.ConclusionsExperimental results demonstrated that TopPIC is capable of identifying many proteoform spectrum matches and localizing unknown alterations using homologous protein sequences containing no more than 2 mutations.

A graph-based filtering method for top-down mass spectral identification

BackgroundDatabase search has been the main approach for proteoform identification by top-down tandem mass spectrometry. However, when the target proteoform that produced the spectrum contains post-translational modifications (PTMs) and/or mutations, it is quite time consuming to align a query spectrum against all protein sequences without any PTMs and mutations in a large database. Consequently, it is essential to develop efficient and sensitive filtering algorithms for speeding up database search.ResultsIn this paper, we propose a spectrum graph matching (SGM) based protein sequence filtering method for top-down mass spectral identification. It uses the subspectra of a query spectrum to generate spectrum graphs and searches them against a protein database to report the best candidates. As the sequence tag and gaped tag approaches need the preprocessing step to extract and select tags, the SGM filtering method circumvents this preprocessing step, thus simplifying data processing. We evaluated the filtration efficiency of the SGM filtering method with various parameter settings on an Escherichia coli top-down mass spectrometry data set and compared the performances of the SGM filtering method and two tag-based filtering methods on a data set of MCF-7 cells.ConclusionsExperimental results on the data sets show that the SGM filtering method achieves high sensitivity in protein sequence filtration. When coupled with a spectral alignment algorithm, the SGM filtering method significantly increases the number of identified proteoform spectrum-matches compared with the tag-based methods in top-down mass spectrometry data analysis.

A graph-based approach for proteoform identification and quantification using top-down homogeneous multiplexed tandem mass spectra

BackgroundTop-down homogeneous multiplexed tandem mass (HomMTM) spectra are generated from modified proteoforms of the same protein with different post-translational modification patterns. They are frequently observed in the analysis of ultramodified proteins, some proteoforms of which have similar molecular weights and cannot be well separated by liquid chromatography in mass spectrometry analysis.ResultsWe formulate the top-down HomMTM spectral identification problem as the minimum error k-splittable flow problem on graphs and propose a graph-based algorithm for the identification and quantification of proteoforms using top-down HomMTM spectra.ConclusionsExperiments on a top-down mass spectrometry data set of the histone H4 protein showed that the proposed method identified many proteoform pairs that better explain the query spectra than single proteoforms.

Bioinformatics Analysis of Top-Down Mass Spectrometry Data with ProSight Lite.

Traditional bottom-up mass spectrometry-based proteomics relies on the use of an enzyme, often trypsin, to generate small peptides (typically < 25 amino acids long). In top-down proteomics, proteins remain intact and are directly measured within the mass spectrometer. This technique, while inherently simpler than bottom-up proteomics, generates data which must be processed and analyzed using software tools "purpose-built" for the job. In this chapter, we will show the analysis of intact protein spectra through deconvolution, deisotoping, and searching with ProSight Lite, a free, vendor-agnostic tool for the analysis of top-down mass spectrometry data. We will illustrate with two examples of intact protein fragmentation spectra and discuss the iterative use of the software to characterize proteoforms and discover the sites of post-translational modifications.

ProSight Lite: graphical software to analyze top-down mass spectrometry data.

Many top-down proteomics experiments focus on identifying and localizing PTMs and other potential sources of “mass shift” on a known protein sequence. A simple application to match ion masses and facilitate the iterative hypothesis testing of PTM presence and location would assist with the data analysis in these experiments. ProSight Lite is a free software tool for matching a single candidate sequence against a set of mass spectrometric observations. Fixed or variable modifications, including both PTMs and a select number of glycosylations, can be applied to the amino acid sequence. The application reports multiple scores and a matching fragment list. Fragmentation maps can be exported for publication in either portable network graphic (PNG) or scalable vector graphic (SVG) format. ProSight Lite can be freely downloaded from http://prosightlite.northwestern.edu, installs and updates from the web, and requires Windows 7 or a higher version.