A highly efficient method for extracting peptides from a single mouse hypothalamus

Nakagawa, Yasuhito; Matsui, Takashi; Konno, Ryuichi; Kawashima, Yusuke; Sato, Toshiya; Itakura, Makoto; Kodera, Yasuhiro

doi:10.1016/j.bbrc.2021.02.041

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Shotgun proteomics using high-resolution MS enables us to conduct MS1-based quantitative comparisons of objective and control peptides from the XIC 1 , 2 . To identify the proteins involved in physiologic and/or pathologic processes based on abundance using shotgun proteomics, poor chromatographic peaks must be excluded from complex LC–MS/MS spectra when using conventional criteria, such as idotP and ∆M 8 – 12 , and then quantifications based on the areas of extracted peaks of identified proteins can be compared. Recently, deep proteomic techniques have been developed that are capable of detecting weak peptide signals, thus introducing the problem of determining how to treat these weak signals for deeper quantification of protein/peptide abundance.…”

Section: Discussionmentioning

confidence: 99%

“…Shotgun proteomics techniques are thus commonly used in biological research (e.g., identification of disease-specific biomarkers) 3 – 5 . Although the isotope dot product (idotP) and mass error (∆M), calculated from MS1 spectra using Skyline 6 , 7 , were adopted as comparative quantification criteria for peptide pairs in some other studies 8 – 12 , these conventional criteria are not sufficient to distinguish peptide peaks from noise. As such, the presence of noise peaks becomes a greater problem as the size of the dataset increases.…”

Section: Introductionmentioning

confidence: 99%

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LC–MS peak assignment based on unanimous selection by six machine learning algorithms

Ito

Matsui

Konno

et al. 2021

Sci Rep

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Recent mass spectrometry (MS)-based techniques enable deep proteome coverage with relative quantitative analysis, resulting in increased identification of very weak signals accompanied by increased data size of liquid chromatography (LC)–MS/MS spectra. However, the identification of weak signals using an assignment strategy with poorer performance results in imperfect quantification with misidentification of peaks and ratio distortions. Manually annotating a large number of signals within a very large dataset is not a realistic approach. In this study, therefore, we utilized machine learning algorithms to successfully extract a higher number of peptide peaks with high accuracy and precision. Our strategy evaluated each peak identified using six different algorithms; peptide peaks identified by all six algorithms (i.e., unanimously selected) were subsequently assigned as true peaks, which resulted in a reduction in the false-positive rate. Hence, exact and highly quantitative peptide peaks were obtained, providing better performance than obtained applying the conventional criteria or using a single machine learning algorithm.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LC–MS peak assignment based on unanimous selection by six machine learning algorithms

Ito

Matsui

Konno

et al. 2021

Sci Rep

Self Cite

View full text Add to dashboard Cite

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Proteomics‐driven identification of short open reading frame‐encoded peptides

Zhang

Yuan

et al. 2022

Proteomics

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Accumulating evidence has shown that a large number of short open reading frames (sORFs) also have the ability to encode proteins. The discovery of sORFs opens up a new research area, leading to the identification and functional study of sORF encoded peptides (SEPs) at the omics level. Besides bioinformatics prediction and ribosomal profiling, mass spectrometry (MS) has become a significant tool as it directly detects the sequence of SEPs. Though MS-based proteomics methods have proved to be effective for qualitative and quantitative analysis of SEPs, the detection of SEPs is still a great challenge due to their low abundance and short sequence. To illustrate the progress in method development, we described and discussed the main steps of largescale proteomics identification of SEPs, including SEP extraction and enrichment, MS detection, data processing and quality control, quantification, and function prediction and validation methods.

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Detection of dynorphin 1-17 biotransformation fragments in human nasal polyps by UPLC-QTOF-MS

Ballouze,

Ismail,

Abu Kassim

et al. 2023

Anal Bioanal Chem

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A highly efficient method for extracting peptides from a single mouse hypothalamus

Cited by 5 publications

References 23 publications

LC–MS peak assignment based on unanimous selection by six machine learning algorithms

LC–MS peak assignment based on unanimous selection by six machine learning algorithms

Proteomics‐driven identification of short open reading frame‐encoded peptides

Detection of dynorphin 1-17 biotransformation fragments in human nasal polyps by UPLC-QTOF-MS

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