Biology subjects

Levitsky, L. I.

Publications and source records attributed to Levitsky, L. I..

9 recordsLinked to original sources

bioRxiv2023

IQMMA: an efficient MS1 intensity extraction using multiple feature detection algorithms for DDA proteomics

One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called < >, in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years each relying on its own matching algorithm. Here, we provide an integrated solution, Intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of E. Coli digest spiked at different concentrations into the HeLa digest used as a background and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2 and OpenMS FeatureFinder. Neither of them was found optimal when applied individually to all the datasets employed in this work, however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.

bioinformatics↗

bioRxiv2022

Massive proteogenomic reanalysis of publicly available proteomic datasets of human tissues in search for protein recoding via adenosine-to-inosine RNA editing

The proteogenomic search pipeline developed in this work has been applied for re-analysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8,000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. The scope of this re-analysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origin, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets representing the core human protein editome. In agreement with prior art works, neural and cancer tissues were found being enriched with recoded proteins. Quantitative analysis indicated that recoding of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was provided by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between human and rodents were validated by targeted proteomics using stable isotope standards in murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.

bioinformatics↗

bioRxiv2022

Unified and standardized mass spectrometry data processing in Python using spectrum_utils

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications, to spectrum plotting from online data repositories, and assisting data analysis tasks for dozens of other projects. Here we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

bioinformatics↗

bioRxiv2022

DirectMS1Quant: ultrafast quantitative proteomics with MS/MS-free mass spectrometry

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at 1% false discovery rate (FDR) when using 5-min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000 to 5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue we performed one-by-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free quantification (LFQ), tandem mass tag (TMT), both based on data dependent acquisition (DDA), and data independent acquisition (DIA). For the comparison we performed a series of proteome-wide analysis of well-characterized (ground truth) and biological relevant samples, including a mix of UPS1 proteins spiked at different concentrations into E. coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods utilizing 10 to 20-fold longer instrumentation time.

bioinformatics↗

bioRxiv2022

A comprehensive evaluation of consensus spectrum generation methods in proteomics

Spectrum clustering is a powerful strategy to minimize redundant mass spectral data by grouping highly similar mass spectra corresponding to repeatedly measured analytes. Based on spectrum similarity, near-identical spectra are grouped in clusters, after which each cluster can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public datasets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for datasets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

bioinformatics↗

bioRxiv2022

Validating amino acid variants in proteogenomics using sequence coverage by multiple reads

Mass spectrometry-based proteome analysis usually implies matching mass spectra of proteolytic peptides to amino acid sequences predicted from nucleic acid sequences. At the same time, due to the stochastic nature of the method when it comes to proteome-wide analysis, in which only a fraction of peptides are selected for sequencing, the completeness of protein sequence identification is undermined. Likewise, the reliability of peptide variant identification in proteogenomic studies is suffering. We propose a way to interpret shotgun proteomics results, specifically in data-dependent acquisition mode, as protein sequence coverage by multiple reads, just as it is done in the field of nucleic acid sequencing for the calling of single nucleotide variants. Multiple reads for each position in a sequence could be provided by overlapping distinct peptides, thus, confirming the presence of certain amino acid residues in the overlapping stretch with much lower false discovery rate than conventional 1%. The source of overlapping distinct peptides are, first, miscleaved tryptic peptides in combination with their properly cleaved counterparts, and, second, peptides generated by several proteases with different specificities after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease proteomic datasets and our own data generated for HEK-293 cell line digests obtained using trypsin, LysC and GluC proteases. From 5000 to 8000 protein groups are identified for each digest corresponding to up to 30% of the whole proteome coverage. Most of this coverage was provided by a single read, while up to 7% of the observed protein sequences were covered two-fold and more. The proteogenomic analysis of HEK-293 cell line revealed 36 peptide variants associated with SNP, seven of which were supported by multiple reads. The efficiency of the multiple reads approach depends strongly on the depth of proteome analysis, the digesting features such as the level of miscleavages, and will increase with the number of different proteases used in parallel proteome digestion. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/475497v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1d6ee2org.highwire.dtl.DTLVardef@5ae8baorg.highwire.dtl.DTLVardef@652216org.highwire.dtl.DTLVardef@1a0d49b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

AA_stat enables extensive characterization of artefact and post-translational modifications in bottom-up proteomics

Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source command-line tool available at https://github.com/SimpleNumber/aa_stat.

bioinformatics↗

bioRxiv2020

Proteome-Wide Analysis of ADAR-mediated Messenger RNA Editing During Fruit Fly Ontogeny

Adenosine-to-inosine RNA editing is an enzymatic post-transcriptional modification which modulates immunity and neural transmission in multicellular organisms. Some of its functions are enforced through editing of mRNA codons with the resulting amino acid substitutions. We identified these sites originated from the RNA editing for developmental proteomes of Drosophila melanogaster at the protein level using available proteomic data for fifteen stages of fruit fly development from egg to imago and fourteen time points of embryogenesis. In total, 42 sites each belonging to a unique protein were found including four sites related to embryogenesis. The interactome analysis has revealed that most of the edited proteins are associated with synaptic vesicle trafficking and actomyosin organization. Quantitation data analysis suggested the existence of phase-specific RNA editing regulation by yet unknown mechanisms. These results support transcriptome analyses showing that a burst in RNA editing occurs during insect metamorphosis from pupa to imago. Further, targeted proteomics was employed to quantify edited and genomically encoded versions of five proteins in brains of larvae, pupae, and imago insects showing a clear trend towards an increase in editing rate for all of them. Our results may help to reveal the protein functions in physiological effects of RNA editing. SignificanceAdenosine-to-inosine RNA editing has multiple effects on body functions in many multicellular organisms from insects and molluscs to humans. Recent studies show that at least some of these effects are mediated by changes in protein sequences due to editing of codons in mRNA. However, it is not known how exactly the edited proteins can participate in RNA editing-mediated pathways. Moreover, most studies of edited proteins are based on the deduction of protein sequence changes from analysis of transcriptome without measurements of proteins themselves. Earlier, we explored for the first time the edited proteins of Drosophila melanogaster proteome. In this work, we continued the proteome-wide analysis of RNA editome using shotgun proteomic data of ontogeny phases of this model insect. It was found that non-synonymous RNA editing, which led to translation of changed proteins, is specific to the life cycle phase. Identification of tryptic peptides containing edited protein sites provides a basis for further direct and quantitative analysis of their editing rate by targeted proteomics. The latter was demonstrated in this study by multiple reaction monitoring experiments which were used to observe the dynamics of editing in selected brain proteins during developmental phases of fruit fly. HighlightsO_LIProteogenomic approach was applied to shotgun proteomics data of fruit fly ontogeny for identification of proteoforms originating from adenosine-to-inosine RNA editing. C_LIO_LIEdited proteins identified at all life cycle stages are enriched in annotated protein-protein interactions at statistically significant level with many of them associated with actomyosin and synaptic vesicle functions. C_LIO_LIProteome-wide RNA editing event profiles were found specific to life cycle phase and independent of the protein abundances. C_LIO_LIA majority of RNA editing events at the protein level was observed after metamorphosis in late pupae to adult insects, which was consistent with transcriptome data. C_LIO_LITargeted proteomic analysis of five selected edited sites and their genomic counterparts in brains for three phases of the fruit fly life cycle have demonstrated a clear increase in editing rate of up to 80% for the endophilin A protein in adult flies. C_LI

systems biology↗

bioRxiv2019

DirectMS1: MS/MS-free identification of 1000 proteins of cellular proteomes in 5 minutes

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty-cycle. It was always tempting to implement approaches which do not require MS/MS, yet, they were constantly failing in achieving meaningful depth of quantitative proteome coverage within short experimental times, which is particular important for clinical or biomarker discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free and label-free method for bottom-up proteomics. We demonstrate identification of 1000 protein groups for a standard HeLa cell line digest using 5-minute LC gradients. The amount of loaded sample was varied in a range from 1 ng to 500 ng, and the method demonstrated 10-fold higher sensitivity compared with the standard MS/MS-based approach. Due to significantly higher sequence coverage obtained by the developed method, it outperforms all popular MS/MS-based label-free quantitation approaches.

biochemistry↗