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Schwämmle, V.

Publications and source records attributed to Schwämmle, V..

8 recordsLinked to original sources

How to deal with internal fragment ions?

1Tandem mass spectrometry of peptides and proteins generates mass spectra of their gas-phase fragmentation product ions, including N-terminal, C-terminal, and internal fragment ions. Whereas N- and C-terminal ions are routinely assigned and identified using computational methods, internal fragment ions are often difficult to annotate correctly. They become particularly relevant for long peptides and full proteoforms where the peptide backbone is more likely to be fragmented multiple times. Internal fragment ions potentially offer tremendous information regarding amino acid sequences and positions of post-translational modifications of peptides and intact proteins. However, their practical application is challenged by the vast number of theoretical internal fragments that exist for long amino acid sequences, leading to a high risk of false-positive annotations. We analyze the mass spectral contributions of internal fragment ions in spectra from middle-down and top-down experiments and introduce a novel graph-based annotation approach designed to manage the complexity of internal fragments. Our graph-based representation allows us to compare multiple candidate proteoforms in a single graph, and to assess different candidate annotations in a fragment ion spectrum. We demonstrate cases from middle-down and top-down data where internal ions enhance amino acid sequence coverage of polypeptides and proteins and accurate localization of post-translational modifications. We conclude that our graph-based method provides a general approach to process complex tandem mass spectra, enhance annotation of internal fragment ions, and improve proteoform sequencing and characterization by mass spectrometry.

bioinformatics↗

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

Proteomics research encompasses a wide array of experimental designs, resulting in diverse datasets varying in structure and properties. This diversity has led to a considerable variety of software solutions for data analysis, each of them using multiple tools with different algorithms for operations like peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. Computational workflows combine these algorithms to facilitate end-to-end analysis, spanning from raw data to detecting differentially regulated proteins. We introduce WOMBAT-P, a versatile platform designed for the automatic benchmarking and comparison of bottom-up label-free proteomics workflows. By standardizing software parameterization and workflow outputs, WOMBAT-P empowers an objective comparison of four commonly utilized data analysis workflows. Furthermore, WOMBAT-P streamlines the processing of public data based on the provided metadata, with an optional specification of 30 parameters. Wombat-P can use Sample and Data Relationship Format for Proteomics (SDRF-Proteomics) as the file input to simply process annotated local or ProteomeXchange deposited datasets. This feature offers a shortcut for data analysis and facilitates comparisons among diverse outputs. Through an examination of experimental ground truth data and a realistic biological dataset, we unveil significant disparities and a low overlap between identified and quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of four workflows (on the same dataset) using a wide range of benchmarking metrics but also provides insights into the capabilities of different software solutions. These metrics support researchers in selecting the most suitable workflow for their specific dataset. The modular architecture of WOMBAT-P promotes extensibility and customization, making it an ideal platform for testing newly developed software tools within a realistic data analysis context.

bioinformatics↗

Distinct and diverse chromatin-proteomes of ageing mouse organs reveal protein signatures that correlate with physiological functions

Temporal molecular changes in ageing mammalian organs are of relevance to disease etiology because many age-related diseases are linked to changes in the transcriptional and epigenetic machinery that regulate gene expression. We performed quantitative proteome analysis of chromatin-enriched protein extracts to investigate the dynamics of the chromatin-proteomes of the mouse brain, heart, lung, kidney, liver, and spleen at 3, 5, 10, and 15 months of age. Each organ exhibited a distinct chromatin-proteome and sets of unique proteins. The brain and spleen chromatin-proteomes were the most extensive, diverse, and heterogenous among the six organs. The spleen chromatin proteome appeared static during the lifespan, presenting a young phenotype that reflects the permanent alertness state and important role of this organ in physiological defense and immunity. We identified a total of 5928 proteins, including 2472 nuclear or chromatin associated proteins across the six mouse organs. Up to 3125 proteins were quantified in each organ demonstrating distinct and organ-specific temporal protein expression timelines and regulation at the post-translational level. Bioinformatics meta- analysis of these chromatin proteomes revealed distinct physiological and ageing- related features for each organ. Our results demonstrate the efficiency of organelle specific proteomics for in vivo studies of a model organism and consolidate the hypothesis that chromatin-associated proteins are involved in distinct and specific physiological functions in ageing organs. HIGHLIGHTSO_LIQuantitative chromatin-proteome analysis during mouse lifespan; C_LIO_LIChromatin analysis in vitro and in vivo mouse models; C_LIO_LIDistinct chromatin proteomes of six organs during mouse lifespan; C_LIO_LICorrelations between ageing and chromatin regulation in mammalian lifespan. C_LI

cell biology↗

MS2AI: Automated repurposing of public peptide LC-MS data for ML applications

MotivationLiquid-chromatography mass-spectrometry (LC-MS) is the established standard for analyzing the proteome in biological samples by identification and quantification of thousands of proteins. Machine learning (ML) promises to considerably improve the analysis of the resulting data, however, there is yet to be any tool that mediates the path from raw data to modern ML applications. More specifically, ML applications are currently hampered by three major limitations: (1) absence of balanced training data with large sample size; (2) unclear definition of sufficiently information-rich data representations for e.g. peptide identification; (3) lack of benchmarking of ML methods on specific LC-MS problems. ResultsWe created the MS2AI pipeline that automates the process of gathering vast quantities of mass spectrometry (MS) data for large scale ML applications. The software retrieves raw data from either in-house sources or from the proteomics identifications database, PRIDE. Subsequently, the raw data is stored in a standardized format amenable for ML encompassing MS1/MS2 spectra and peptide identifications. This tool bridges the gap between MS and AI, and to this effect we also present an ML application in the form of a convolutional neural network for the identification of oxidized peptides. AvailabilityAn open source implementation of the software can be found freely available for non-commercial use at https://gitlab.com/roettgerlab/ms2ai. Contactveits@bmb.sdu.dk Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Synaptic transmission induces site-specific changes in sialylation on N-linked glycoproteins in rat nerve terminals

Synaptic transmission leading to release of neurotransmitters in the nervous system is a fast and highly dynamic process. Previously, protein interaction and phosphorylation have been thought to be the main regulators of synaptic transmission. Here we show a novel potential modulator of synaptic transmission, sialylation of N-linked glycosylation. The negatively charged sialic acids can be modulated, similarly to phosphorylation, by the action of sialyltransferases and sialidases thereby changing local structure and function of membrane glycoproteins. We characterized site-specific alteration in sialylation on N-linked glycoproteins in isolated rat nerve terminals after brief depolarization using quantitative sialiomics. We identified 1965 formerly sialylated N-linked glycosites in synaptic proteins and found that the abundances of 430 glycosites changed after five seconds depolarization. We observed changes on essential synaptic proteins such as synaptic vesicle proteins, ion channels and transporters, neurotransmitter receptors and cell adhesion molecules. This study is to our knowledge the first to describe ultra-fast site-specific modulation of the sialiome after brief stimulation of a biological system.

neuroscience↗

Visualization of the Dynamics of Histone Modifications and Their Crosstalk Using PTM-CrossTalkMapper

Visualization of large-scale, multi-dimensional omics data is a major challenge. Experimental study designs in proteomics research produce multiple data layers, e.g. relationships between hundreds of proteins, their interactions and abundances as a function of time or perturbation, as well as dynamics of post-translational modifications (PTMs) and allelic variants (proteoforms). These different levels and types of information of proteins and proteoforms complicate data analysis and generation of insightful and comprehensible graphic visualizations. Middle-down mass spectrometry of histone proteins now allows quantifying hundreds of histone proteoforms including co-existing methylation, acetylation and phosphorylation events at distinct amino acid residues within histone molecules. The histone PTM landscape plays a dominant role in the regulation of chromatin activity and transcriptional and epigenetic control. The dynamics of these reversible modifications are governed by reader, writer and eraser enzymes that cooperate to regulate molecular mechanism that rely on multiple interdependent PTM marks in histones and nucleosomes in chromatin. This PTM crosstalk can be quantified and provides a detailed picture of the underlying rules for setting the histone PTM landscape and chromatin activity, and is available to the community via our CrosstalkDB platform. Here, we developed a new computational method, PTM-CrossTalkMapper, to visualize the dynamics of histone PTMs for different experimental conditions, replicates and proteoforms onto a landscape, thereby describing the crosstalk and interplay between PTMs in a more comprehensible manner. We show the power of combining different levels of information on such crosstalk maps for histone PTM dynamics in mouse organs during the aging process. The PTM-CrossTalkMapper toolkit provides flexible functions to create these maps in various scenarios of multi-dimensional experimental designs, including histone PTM patterns and PTM crosstalk.\n\nThe source code is available at https://github.com/veitveit/CrossTalkMapper.

bioinformatics↗

PolySTest: Robust statistical testing of proteomics data with missing values improves detection of biologically relevant features

Statistical testing remains one of the main challenges for high-confidence detection of differentially regulated proteins or peptides in large-scale quantitative proteomics experiments by mass spectrometry. Statistical tests need to be sufficiently robust to deal with experiment intrinsic data structures and variations and often also reduced feature coverage across different biological samples due to ubiquitous missing values. A robust statistical test provides accurate confidence scores of large-scale proteomics results, regardless of instrument platform, experimental protocol and software tools. However, the multitude of different combinations of experimental strategies, mass spectrometry techniques and informatics methods complicate the decision of choosing appropriate statistical approaches. We address this challenge by introducing PolySTest, a user-friendly web service for statistical testing, data browsing and data visualization. We introduce a new method, Miss Test, that simultaneously tests for missingness and feature abundance, thereby complementing common statistical tests by rescuing otherwise discarded data features. We demonstrate that PolySTest with integrated Miss Test achieves higher confidence and higher sensitivity for artificial and experimental proteomics data sets with known ground truth. Application of PolySTest to mass spectrometry based large-scale proteomics data obtained from differentiating muscle cells resulted in the rescue of 10%-20% additional proteins in the identified molecular networks relevant to muscle differentiation. We conclude that PolySTest is a valuable addition to existing tools and instrument enhancements that improve coverage and depth of large-scale proteomics experiments. A fully functional demo version of PolySTest and Miss Test is available via http://computproteomics.bmb.sdu.dk/Apps/PolySTest.

bioinformatics↗

Quantitative behavior of protein complexes in human cells

Translational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. To better understand and predict these functional states, extensive characterization of complex composition, behavior, and abundance is necessary. Mass spectrometry provides an unbiased approach to directly determine protein abundances across cell populations and thus to profile a comprehensive abundance map of proteins. We investigated the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. After thorough assessment of different randomization methods and statistical scoring algorithms, we developed a computational tool to quantify the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types. We identified the intrinsic structures in complex behavior that allow to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. With the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes. CoExpresso can be accessed through http://computproteomics/Apps/CoExpresso. Source code and R scripts for database generation are available at https://bitbucket.org/veitveit/coexpresso.\n\nAuthor summaryMany proteins form multi-functional assemblies called protein complexes instead of working as singly units. These complexes control most processes in the cell making the full characterization of their behavior inevitable to understand cellular control mechanisms. Detailed knowledge about complex behavior will elucidate biomarkers and drug targets that exhibit and correct aberrant cell states, respectively. We investigated abundance changes of the protein complex components over more than 100 different human cell types. By using statistical scoring models, we estimated the evidence for the co-regulation of the proteins and revealed which proteins form subunits with impact on complex function and composition. By providing the interactive web service CoExpresso, any combination of proteins can be tested for their co-regulation in human cells.

bioinformatics↗