Top-down Mass Spectra Research Articles

FREE: Enhanced Feature Representation for Isotopic Envelope Evaluation in Top-Down Mass Spectra Deconvolution.

The aim of deconvolution of top-down mass spectra is to recognize monoisotopic peaks from the experimental envelopes in raw mass spectra. So accurate assessment of similarity between theoretical and experimental envelopes is a critical step in mass spectra data deconvolution. Existing evaluation methods primarily rely on intensity differences and m/z similarity, potentially lacking a comprehensive assessment. To overcome this constraint and facilitate a comprehensive and refined assessment of the similarity between theoretical and experimental envelopes, there exists an imperative to systematically explore and identify increasingly efficacious features for assessing this correspondence. We present enhanced feature representation for isotopic envelope evaluation (FREE) that derives diverse feature representations, encapsulating fundamental physical attributes of envelopes, including peak intensity and envelope shape. We trained FREE and evaluated its performance on both the ovarian tumor (OT) (human OT cells) data set and zebrafish (ZF) (brain in mature female ZF) data set. Specifically, comparing the state-of-art method, FREE demonstrates higher performance in multiple evaluation metrics across both the OT and ZF data sets, with a particular emphasis on precision, and it demonstrates accurate predictions of a greater number of positive envelopes among the top-ranked envelopes based on their scores. Moreover, within a cross-species data set of ZF, FREE identified a higher number of proteoform-spectrum matches (PrSMs), increasing the count from 50,795 to 52,927 compared to EnvCNN, the amalgamation of FREE with TopFD also exhibits a commendable capacity to discern 117,883 fragment ions, thus surpassing the 97,554 fragment ions identified through the application of EnvCNN in conjunction with TopFD. To further validate the performance of FREE, we have tested 10 a cross-species top-down proteomes containing 36 subdata set from ProteomeXchange. The results reveal that, after deconvolution with TopFD + FREE, TopPIC identifies more PrSMs across these 10 data sets in both the first and second rounds of experiments. These findings underscore the robustness and generalization capabilities of the FREE approach in diverse proteomes.

Spacer Fidelity Assessments of Guide RNA by Top-Down Mass Spectrometry.

The advancement of CRISPR-based gene editing tools into biotherapeutics offers the potential for cures to genetic disorders and for new treatment paradigms for even common diseases. Arguably, the most important component of a CRISPR-based medicine is the guide RNA, which is generally large (>100-mer) synthetic RNA composed of a "tracr" and "spacer" region, the latter of which dictates the on-target editing site as well as potential undesired off-target edits. Aiming to advance contemporary capabilities for gRNA characterization to ensure the spacer region is of high fidelity, top-down mass spectrometry was herein implemented to provide direct and quantitative assessments of highly modified gRNA. In addition to sequencing the spacer region and pinpointing modifications, top-down mass spectra were utilized to quantify single-base spacer substitution impurities down to <1% and to decipher highly dissimilar spacers. To accomplish these results in an automated fashion, we devised custom software capable of sequencing and quantifying impurities in gRNA spacers. Notably, we developed automated tools that enabled the quantification of single-base substitutions, including advanced isotopic pattern matching for C > U and U > C substitutions, and created a de novo sequencing strategy to facilitate the identification and quantification of gRNA impurities with highly dissimilar spacer regions.

Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy.

Top-down mass spectrometry (MS) is a powerful tool for the identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. However, the complex data set generated from top-down MS experiments requires multiple sequential data processing steps to successfully interpret the data for identifying and characterizing proteoforms. One critical step is the deconvolution of the complex isotopic distribution that arises from naturally occurring isotopes. Multiple algorithms are currently available to deconvolute top-down mass spectra, resulting in different deconvoluted peak lists with varied accuracy compared to true positive annotations. In this study, we have designed a machine learning strategy that can process and combine the peak lists from different deconvolution results. By optimizing clustering results, deconvolution results from THRASH, TopFD, MS-Deconv, and SNAP algorithms were combined into consensus peak lists at various thresholds using either a simple voting ensemble method or a random forest machine learning algorithm. For the random forest algorithm, which had better predictive performance, the consensus peak lists on average could achieve a recall value (true positive rate) of 0.60 and a precision value (positive predictive value) of 0.78. It outperforms the single best algorithm, which achieved a recall value of only 0.47 and a precision value of 0.58. This machine learning strategy enhanced the accuracy and confidence in protein identification during database searches by accelerating the detection of true positive peaks while filtering out false positive peaks. Thus, this method shows promise in enhancing proteoform identification and characterization for high-throughput data analysis in top-down proteomics.

Top-down mass spectrometry of intact phosphorylated β-casein: Correlation between the precursor charge state and internal fragments.

Phosphorylated proteins play essential roles in many cellular processes, and identification and characterization of the relevant phosphoproteins can help to understand underlying mechanisms. Herein, we report a collision-induced dissociation top-down approach for characterizing phosphoproteins on a quadrupole time-of-flight mass spectrometer. β-casein, a protein with two major isoforms and five phosphorylatable serine residues, was used as a model. Peaks corresponding to intact β-casein ions with charged states up to 36+ were detected. Tandem mass spectrometry was performed on β-casein ions of different charge states (12+ , and 15+ to 28+ ) in order to determine the effects of charge state on dissociation of this protein. Most of the abundant fragments corresponded to y, b ions, and internal fragments caused by cleavage of the N-terminal amide bond adjacent to proline residues (Xxx-Pro). The abundance of internal fragments increased with the charge state of the protein precursor ion; these internal fragments predominantly arose from one or two Xxx-Pro cleavage events and were difficult to accurately assign. The presence of abundant sodium adducts of β-casein further complicated the spectra. Our results suggest that when interpreting top-down mass spectra of phosphoproteins and other proteins, researchers should consider the potential formation of internal fragments and sodium adducts for reliable characterization.

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.

Informed-Proteomics: open-source software package for top-down proteomics.

Top-down proteomics is the analysis of intact proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down LC-MS/MS datasets is rapidly increasing due to advances in instrumentation and sample processing protocols. However, the top-down mass spectra are substantially more complex compared to conventional bottom-up data. To take full advantage of the increasing data quality, there is an urgent need to develop algorithms and software tools for confident proteoform identification and quantification. In this study, we present a new open source software suite for top-down proteomics analysis consisting of an LC-MS feature finding algorithm, a database search algorithm, and an interactive results viewer. The presented tool along with several other popular tools were evaluated using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.

Top-down analysis of immunoglobulin G isotypes 1 and 2 with electron transfer dissociation on a high-field Orbitrap mass spectrometer

The increasing importance of immunoglobulins G (IgGs) as biotherapeutics calls for improved structural characterization methods designed for these large (~150kDa) macromolecules. Analysis workflows have to be rapid, robust, and require minimal sample preparation. In a previous work we showed the potential of Orbitrap Fourier transform mass spectrometry (FTMS) combined with electron transfer dissociation (ETD) for the top-down investigation of an intact IgG1, resulting in ~30% sequence coverage. Here, we describe a top-down analysis of two IgGs1 (adalimumab and trastuzumab) and one IgG2 (panitumumab) performed with ETD on a mass spectrometer equipped with a high-field Orbitrap mass analyzer. For the IgGs1, sequence coverage comparable to the previous results was achieved in a two-fold reduced number of summed transients, which corresponds, taken together with the significantly increased spectra acquisition rate, to ~six-fold improvement in analysis time. Furthermore, we studied the influence of ion-ion interaction times on ETD product ions for IgGs1, and the differences in fragmentation behavior between IgGs1 and IgG2, which present structural differences. Overall, these results reinforce the hypothesis that gas phase dissociation using both energy threshold-based and radical-driven ion activations is directed to specific regions of the polypeptide chains mostly by the location of disulfide bonds. Significance of the studyCompared with our previous report, the results presented herein demonstrate the power of technological advances of the next generation Orbitrap™ platform, including the use of a high-field compact (i.e., D20) Orbitrap mass analyzer, and a dedicated manipulation strategy for large protein ions (via their trapping in the HCD collision cell along with reduction of the pressure in the cell). Notably, these important developments became recently commercially available in the top-end Orbitrap platforms under the name of “Protein Mode”. Furthermore, we continued exploring the advantages offered by the summation (averaging) of transients (time-domain data) for improving the signal-to-noise ratio of top-down mass spectra. Finally, for the first time we report the application of the hybrid ion activation technique that combines electron transfer dissociation and higher energy collisional dissociation, known as EThcD, on intact monoclonal antibodies. Under these specific instrumental parameters, EThcD produces a partially complementary fragmentation pattern compared to ETD, increasing the overall sequence coverage especially at the protein termini.

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

http://proteomics.informatics.iupui.edu/software/toppic/ CONTACT: xwliu@iupui.eduSupplementary information: Supplementary data are available at Bioinformatics online.

A new scoring function for top-down spectral deconvolution.

BackgroundTop-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.ResultsIn this paper, we propose a new scoring function, L-score, for evaluating isotopomer envelopes. By combining L-score with MS-Deconv, a new software tool, MS-Deconv+, was developed for top-down spectral deconvolution. Experimental results showed that MS-Deconv+ outperformed existing software tools in top-down spectral deconvolution.ConclusionsL-score shows high discriminative ability in identification of isotopomer envelopes. Using L-score, MS-Deconv+ reports many correct monoisotopic masses missed by other software tools, which are valuable for proteoform identification and characterization.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1140) contains supplementary material, which is available to authorized users.

Deconvolution and Database Search of Complex Tandem Mass Spectra of Intact Proteins

Top-down proteomics studies intact proteins, enabling new opportunities for analyzing post-translational modifications. Because tandem mass spectra of intact proteins are very complex, spectral deconvolution (grouping peaks into isotopomer envelopes) is a key initial stage for their interpretation. In such spectra, isotopomer envelopes of different protein fragments span overlapping regions on the m/z axis and even share spectral peaks. This raises both pattern recognition and combinatorial challenges for spectral deconvolution. We present MS-Deconv, a combinatorial algorithm for spectral deconvolution. The algorithm first generates a large set of candidate isotopomer envelopes for a spectrum, then represents the spectrum as a graph, and finally selects its highest scoring subset of envelopes as a heaviest path in the graph. In contrast with other approaches, the algorithm scores sets of envelopes rather than individual envelopes. We demonstrate that MS-Deconv improves on Thrash and Xtract in the number of correctly recovered monoisotopic masses and speed. We applied MS-Deconv to a large set of top-down spectra from Yersinia rohdei (with a still unsequenced genome) and further matched them against the protein database of related and sequenced bacterium Yersinia enterocolitica. MS-Deconv is available at http://proteomics.ucsd.edu/Software.html.

Interpreting Top-Down Mass Spectra Using Spectral Alignment

Recent advances in mass spectrometry instrumentation, such as FTICR and OrbiTrap, have made it possible to generate high-resolution spectra of entire proteins. While these methods offer new opportunities for performing "top-down" studies of proteins, the computational tools for analyzing top-down data are still scarce. In this paper we investigate the application of spectral alignment to the problem of identifying protein forms in top-down mass spectra (i.e., identifying the modifications, mutations, insertions, and deletions). We demonstrate how spectral alignment efficiently discovers protein forms even in the presence of numerous modifications and how the algorithm can be extended to discover positional isomers from spectra of mixtures of isobaric protein forms.