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Tandem Mass Spectrometry

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Accurate Prediction of y Ions in Beam-Type Collision-Induced Dissociation Using Deep Learning.

Analytical chemistry
Peptide fragmentation spectra contain critical information for the identification of peptides by mass spectrometry. In this study, we developed an algorithm that more accurately predicts the high-intensity peaks among the peptide spectra. The trainin...

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.

Analytical chemistry
The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tan...

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for determination of free and total dabigatran in human plasma and its application to a pharmacokinetic study.

Drug development and industrial pharmacy
A high-performance liquid chromatography-tandem mass spectrometric method for the determination of free and total dabigatran in human plasma has been developed and validated using a stable labeled internal standard (IS) as dabigatran D. The extractio...

Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics.

Analytical chemistry
Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS, which uses machine learning based on a convoluted neural network to reduce the number and fraction of false peaks. NeatMS comes w...

MSSort-DIA: A deep learning classification tool of the peptide precursors quantified by OpenSWATH.

Journal of proteomics
OpenSWATH is an analysis toolkit commonly used for data independent acquisition (DIA). Although the output of OpenSWATH is controlled at 1% false discovery rate (FDR), the output report still contains many peptide precursors with low similarity fragm...

Rapid detection of α-amanitin and β-amanitin in rat plasma by ultra-performance liquid chromatography-tandem mass spectrometry and its application to the toxicokinetic study of Lepiota brunneoincarnata.

Forensic toxicology
PURPOSE: Lepiota brunneoincarnata is a well-known poisonous mushroom and is responsible for fatal mushroom poisoning cases worldwide. α-Amanitin and β-amanitin are the main amatoxin compounds of Lepiota brunneoincarnata. However, there are no publish...

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation.

Nature communications
Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining he...

Development and validation of a sensitive and high throughput UPLC-MS/MS method for determination of paraquat and diquat in human plasma and urine: application to poisoning cases at emergency departments of hospitals.

Forensic toxicology
PURPOSE: Paraquat and diquat are well-known toxic herbicides, at least responsible for hundreds of fatal poisoning events worldwide. However, the determination of diquat and paraquat in plasma and urine is very challenging because of their high polar...

Rapid and sensitive UHPLC-MS/MS methods for dietary sample analysis of 43 mycotoxins in China total diet study.

Journal of advanced research
INTRODUCTION: Mycotoxins are toxic metabolites produced by fungi that commonly contaminate foods. As recommended by the World Health Organization, total diet study (TDS) is the most efficient and effective way to estimate the dietary intakes of certa...

Determination of new psychoactive substances and other drugs in postmortem blood and urine by UHPLC-MS/MS: method validation and analysis of forensic samples.

Forensic toxicology
PURPOSE: This study aimed to validate a modified QuEChERS method followed by ultra-high performance liquid chromatography-tandem mass spectrometry to determine 79 new psychoactive substances (NPS) and other drugs in blood and urine.