Trapped Ion Mobility Spectrometry and Parallel Accumulation–Serial Fragmentation in Proteomics

Molecular & Cellular Proteomics, Год журнала: 2021, Номер 20, С. 100138 - 100138

Опубликована: Янв. 1, 2021

Recent advances in efficiency and ease of implementation have rekindled interest ion mobility spectrometry, a technique that separates gas phase ions by their size shape can be hybridized with conventional LC MS. Here, we review the recent development trapped spectrometry (TIMS) coupled to TOF mass analysis. In particular, parallel accumulation–serial fragmentation (PASEF) operation mode offers unique advantages terms sequencing speed sensitivity. Its defining feature is it synchronizes release from TIMS device downstream selection precursors for quadrupole configuration. As are compressed into narrow peaks, number peptide fragment spectra obtained data-dependent or targeted analyses increased an order magnitude without compromising Taking advantage correlation between mass, PASEF principle also multiplies data-independent acquisition. This makes technology well suited rapid proteome profiling, increasingly important attribute clinical proteomics, as ultrasensitive measurements down single cells. The accuracy enable precise collisional cross section values at scale more than million data points neural networks capable predicting them based only on sequences. Peptide differ isobaric sequences positional isomers post-translational modifications. additional information may leveraged real time direct acquisition postprocessing increase confidence identifications. These developments make powerful expandable platform proteomics beyond.

Язык: Английский

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AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Ноя. 24, 2022

Machine learning and in particular deep (DL) are increasingly important mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility fragment intensities of a peptide just from amino acid sequence with good accuracy. However, is very rapidly developing field new neural network architectures frequently appearing, which challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, modular Python framework built on PyTorch library that learns predicts properties peptides ( https://github.com/MannLabs/alphapeptdeep ). It features model shop enables non-specialists create few lines code. AlphaPeptDeep represents post-translational modifications generic manner, even if only chemical composition known. Extensive use transfer obviates need large data sets refine experimental conditions. The predicting collisional cross sections at least par existing tools. Additional sequence-based also be predicted by as demonstrated HLA prediction improve identification data-independent acquisition https://github.com/MannLabs/PeptDeep-HLA

Язык: Английский

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Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Янв. 6, 2023

A plethora of software suites and multiple classes spectral libraries have been developed to enhance the depth robustness data-independent acquisition (DIA) data processing. However, how combination a DIA tool library impacts outcome proteomics phosphoproteomics analysis has rarely investigated using benchmark that mimics biological complexity. In this study, we create sets simulating regulation thousands proteins in complex background, which are collected on both an Orbitrap timsTOF instruments. We evaluate four commonly used (DIA-NN, Spectronaut, MaxDIA Skyline) combined with seven different global proteome analysis. Moreover, assess their performances analyzing phosphopeptide standards TNF-α-induced phosphoproteome regulation. Our study provides practical guidance construct robust pipeline for studies implementing technique.

Язык: Английский

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Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

ACS Measurement Science Au, Год журнала: 2024, Номер 4(4), С. 338 - 417

Опубликована: Июнь 4, 2024

Proteomics is the large scale study of protein structure and function from biological systems through identification quantification."Shotgun proteomics" or "bottom-up prevailing strategy, in which proteins are hydrolyzed into peptides that analyzed by mass spectrometry.Proteomics studies can be applied to diverse ranging simple proteoforms, protein-protein interactions, structural alterations, absolute relative quantification, post-translational modifications, stability.To enable this range different experiments, there strategies for proteome analysis.The nuances how proteomic workflows differ may challenging understand new practitioners.Here, we provide a comprehensive overview proteomics methods.We cover biochemistry basics extraction interpretation orthogonal validation.We expect Review will serve as handbook researchers who field bottom-up proteomics.

Язык: Английский

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The hidden bacterial microproteome

Molecular Cell, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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Mass-spectrometry-based proteomics: from single cells to clinical applications

Nature, Год журнала: 2025, Номер 638(8052), С. 901 - 911

Опубликована: Фев. 26, 2025

Язык: Английский

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A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

Scientific Data, Год журнала: 2022, Номер 9(1)

Опубликована: Март 30, 2022

In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with introduction of dozens novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, turn inspiring specialized analysis pipelines. Simultaneously, growing number datasets have been made publicly available repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this assessing performance on different platforms is currently hampered by lack single benchmark experimental design. Therefore, we acquired hybrid proteome mixture instrument all families acquisition. Here, present comprehensive Data-Dependent Data-Independent Acquisition (DDA/DIA) dataset using several most commonly used current day instrumental platforms. The consists over 700 LC-MS runs, including adequate replicates allowing robust statistics covering nearly 10 formats, scanning quadrupole ion mobility enabled acquisitions. Datasets are via ProteomeXchange (PXD028735).

Язык: Английский

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Accurate Label-Free Quantification by directLFQ to Compare Unlimited Numbers of Proteomes

Molecular & Cellular Proteomics, Год журнала: 2023, Номер 22(7), С. 100581 - 100581

Опубликована: Май 23, 2023

Recent advances in mass spectrometry-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks bioinformatics pipeline. Although peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with sample numbers, may even preclude analysis large-scale data. Here we introduce directLFQ, a ratio-based approach for normalization and calculation protein intensities. It estimates quantities via aligning samples ion traces by shifting them on top each other logarithmic space. Importantly, directLFQ scales linearly number samples, allowing analyses studies to finish minutes instead days months. We quantify 10,000 proteomes 10 min 100,000 less than 2 h, 1000-fold faster some implementations popular LFQ algorithm MaxLFQ. In-depth characterization reveals excellent properties benchmark results, comparing favorably MaxLFQ both data-dependent data-independent acquisition. In addition, provides normalized intensity peptide-level comparisons. an important part overall quantitative proteomic pipeline that also needs include high sensitive statistical leading proteoform resolution. Available as open-source Python package graphical user interface one-click installer, it can be used AlphaPept ecosystem well downstream common computational pipelines.

Язык: Английский

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Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 3, 2024

Abstract A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each for searching such data uses its own internally implemented methodology reporting and controlling error. Many these tools are closed source, with incompletely documented methodology, strategies validating inconsistent across tools. In this work, we identify three different methods false discovery rate (FDR) control use field, one which invalid, can only provide lower bound rather than an upper bound, valid but under-powered. The result that has very poor understanding well doing respect FDR control, particularly analysis data-independent acquisition (DIA) data. We therefore propose new, more powerful method evaluating setting, then employ method, along existing bounding technique, characterize variety popular search find data-dependent (DDA) generally seem at peptide level, whereas none DIA consistently controls level all datasets investigated. Furthermore, problem becomes much worse when latter evaluated protein level. These results may have significant implications various downstream analyses, since proper potential reduce noise lists thereby boost power.

Язык: Английский

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Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors into Peptide Identification

Molecular & Cellular Proteomics, Год журнала: 2024, Номер 23(7), С. 100798 - 100798

Опубликована: Июнь 12, 2024

Язык: Английский

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