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Tandem Mass Spectrometry

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Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics.

PLoS computational biology
Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains ...

Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC-MS.

Analytical chemistry
Region of interest (ROI) extraction is a fundamental step in analyzing metabolomic datasets acquired by liquid chromatography-mass spectrometry (LC-MS). However, noises and backgrounds in LC-MS data often affect the quality of extracted ROIs. Therefo...

Improving SWATH-MS analysis by deep-learning.

Proteomics
Data-independent acquisition (DIA) of tandem mass spectrometry spectra has emerged as a promising technology to improve coverage and quantification of proteins in complex mixtures. The success of DIA experiments is dependent on the quality of spectra...

DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.

Analytical chemistry
In tandem mass spectrometry-based proteomics, proteins are digested into peptides by specific protease(s), but generally only a fraction of peptides can be detected. To characterize detectable proteotypic peptides, we have developed a series of metho...

Fast screening and identification of illegal adulteration in dietary supplements and herbal medicines using molecular networking with deep-learning-based similarity algorithms.

Analytical and bioanalytical chemistry
Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical tool used for adulteration inspection. Nevertheless, it is a challenging task to identify illegal adulterants that are not included in the library or are unexp...

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

Nature communications
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 MSFrag...

DbyDeep: Exploration of MS-Detectable Peptides via Deep Learning.

Analytical chemistry
Predicting peptide detectability is useful in a variety of mass spectrometry (MS)-based proteomics applications, particularly targeted proteomics. However, most machine learning-based computational methods have relied solely on information from the p...

Microextraction by packed sorbent of N-nitrosamines from Losartan tablets using a high-throughput robot platform followed by liquid chromatography-tandem mass spectrometry.

Journal of separation science
The development of a fast, cost-effective, and efficient microextraction by packed sorbent setup was achieved by combining affordable laboratory-repackable devices of microextraction with a high-throughput cartesian robot. This setup was evaluated fo...

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra.

Journal of chemical information and modeling
Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends o...

DrugBank 6.0: the DrugBank Knowledgebase for 2024.

Nucleic acids research
First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and cl...