A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules – Proof of concept study using an artificial neural network for sample classification

2020 ◽  
Vol 12 (6) ◽  
pp. 836-845 ◽  
Author(s):  
Gabriel L. Streun ◽  
Marco P. Elmiger ◽  
Akos Dobay ◽  
Lars Ebert ◽  
Thomas Kraemer
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Small Molecules ◽  
High Resolution Mass Spectrometry ◽  
Learning Approach ◽  
Proof Of Concept ◽  
Data Independent Acquisition ◽  
Sample Classification ◽  
Machine Learning Approach ◽  
Resolution Mass

scholarly journals Quantification and Identification of Post-Translational Modifications Using Modern Proteomics

2021 ◽  
pp. 225-235
Author(s):  
Anja Holtz ◽  
Nathan Basisty ◽  
Birgit Schilling
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Throughput ◽  
High Resolution Mass Spectrometry ◽  
Label Free ◽  
Post Translational Modifications ◽  
Data Independent Acquisition ◽  
Lysine Residues ◽  
Detailed Protocol ◽  
Resolution Mass

AbstractPost-translational modifications (PTMs) occur dynamically, allowing cells to quickly respond to changes in the environment. Lysine residues can be targeted by several modifications including acylations (acetylation, succinylation, malonylation, glutarylation, and others), methylation, ubiquitination, and other modifications. One of the most efficient methods for the identification of post-translational modifications is utilizing immunoaffinity enrichment followed by high-resolution mass spectrometry. This workflow can be coupled with comprehensive data-independent acquisition (DIA) mass spectrometry to be a high-throughput, label-free PTM quantification approach. Below we describe a detailed protocol to process tissue by homogenization and proteolytically digest proteins, followed by immunoaffinity enrichment of lysine-acetylated peptides to identify and quantify relative changes of acetylation comparing different conditions.

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