Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation

Molecular Systems Biology, Journal Year: 2022, Volume and Issue: 18(3)

Published: Feb. 28, 2022

Language: Английский

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Artificial intelligence for proteomics and biomarker discovery

Cell Systems, Journal Year: 2021, Volume and Issue: 12(8), P. 759 - 770

Published: Aug. 1, 2021

Language: Английский

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

Molecular & Cellular Proteomics, Journal Year: 2021, Volume and Issue: 20, P. 100138 - 100138

Published: Jan. 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.

Language: Английский

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Mass Spectrometry-Based Techniques to Elucidate the Sugar Code

Chemical Reviews, Journal Year: 2021, Volume and Issue: 122(8), P. 7840 - 7908

Published: Sept. 7, 2021

Cells encode information in the sequence of biopolymers, such as nucleic acids, proteins, and glycans. Although glycans are essential to all living organisms, surprisingly little is known about “sugar code” biological roles these molecules. The reason glycobiology lags behind its counterparts dealing with acids proteins lies complexity carbohydrate structures, which renders their analysis extremely challenging. Building blocks that may differ only configuration a single stereocenter, combined vast possibilities connect monosaccharide units, lead an immense variety isomers, poses formidable challenge conventional mass spectrometry. In recent years, however, combination innovative ion activation methods, commercialization mobility–mass spectrometry, progress gas-phase spectroscopy, advances computational chemistry have led revolution spectrometry-based glycan analysis. present review focuses on above techniques expanded traditional glycomics toolkit provided spectacular insight into structure fascinating biomolecules. To emphasize specific challenges associated them, major classes mammalian discussed separate sections. By doing so, we aim put spotlight most important element glycobiology: themselves.

Language: Английский

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Rapid and In-Depth Coverage of the (Phospho-)Proteome With Deep Libraries and Optimal Window Design for dia-PASEF

Molecular & Cellular Proteomics, Journal Year: 2022, Volume and Issue: 21(9), P. 100279 - 100279

Published: Aug. 6, 2022

Language: Английский

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

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 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

Language: Английский

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MSBooster: improving peptide identification rates using deep learning-based features

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: July 27, 2023

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, rescoring peptide-to-spectrum matches additional features incorporating deep learning-based predictions peptide properties, LC retention time, ion mobility, and spectra. We demonstrate the utility tandem with MSFragger Percolator, several different workflows, including nonspecific searches (immunopeptidomics), direct from data independent acquisition data, single-cell proteomics, generated an mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, fully integrated into widely used FragPipe

Language: Английский

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

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Jan. 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.

Language: Английский

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Toward an Integrated Machine Learning Model of a Proteomics Experiment

Journal of Proteome Research, Journal Year: 2023, Volume and Issue: 22(3), P. 681 - 696

Published: Feb. 6, 2023

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and experts a workshop with the goals to evaluate explore applications for realistic from multidimensional spectrometry-based analysis any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps define needs. Being able generate bespoke synthetic legitimate important uses system suitability, method development, algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature informed discussions what is currently possible future opportunities challenges. following perspective we summarize these hope conveying our excitement about potential inspire research.

Language: Английский

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Sensing prior constraints in deep neural networks for solving exploration geophysical problems

Proceedings of the National Academy of Sciences, Journal Year: 2023, Volume and Issue: 120(23)

Published: June 1, 2023

One of the key objectives in geophysics is to characterize subsurface through process analyzing and interpreting geophysical field data that are typically acquired at surface. Data-driven deep learning methods have enormous potential for accelerating simplifying but also face many challenges, including poor generalizability, weak interpretability, physical inconsistency. We present three strategies imposing domain knowledge constraints on neural networks (DNNs) help address these challenges. The first strategy integrate into by generating synthetic training datasets geological forward modeling properly encoding prior as part input fed DNNs. second design nontrainable custom layers operators preconditioners DNN architecture modify or shape feature maps calculated within network make them consistent with knowledge. final implement information laws regularization terms loss functions discuss implementation detail demonstrate their effectiveness applying processing, imaging, interpretation, model building.

Language: Английский

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