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Related Concept Videos

Proteomics01:33

Proteomics

7.3K
A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
7.3K

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High-throughput drug target discovery using a fully automated proteomics sample preparation platform.

Qiong Wu1, Jiangnan Zheng1,2, Xintong Sui1

  • 1Department of Chemistry, School of Science, Southern University of Science and Technology Shenzhen 518055 China tianrj@sustech.edu.cn.

Chemical Science
|February 26, 2024
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Summary

This study introduces an automated workflow for drug target discovery using single-temperature thermal proteome profiling (TPP) and mass spectrometry (MS). The method significantly improves throughput for identifying drug targets and off-targets, addressing inefficiencies in drug development.

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Area of Science:

  • Proteomics
  • Drug Discovery
  • Biotechnology

Background:

  • Drug development faces high costs and inefficiencies due to poor efficacy and toxicity.
  • Mass spectrometry (MS)-based proteomics, especially quantitative proteomics, can reveal drug resistance and off-target effects.
  • Thermal proteome profiling (TPP) is valuable for proteome-wide drug target identification but is limited by sample volume and variability.

Purpose of the Study:

  • To develop a high-throughput drug target discovery workflow.
  • To integrate single-temperature TPP, automated sample preparation, and data-independent acquisition (DIA) for enhanced efficiency.
  • To overcome limitations of traditional TPP in large-scale analyses.

Main Methods:

  • Utilized a fully automated proteomics sample preparation platform (autoSISPROT) for high-throughput processing of up to 96 samples.
  • Integrated autoSISPROT with single-temperature TPP and data-independent acquisition (DIA) for quantitative proteomics.
  • Employed TPP to identify drug targets and off-targets for kinase inhibitors.

Main Results:

  • The autoSISPROT platform processed 96 samples in under 2.5 hours with high digestion (>94%) and TMT labeling (>98%) efficiencies.
  • Achieved excellent reproducibility with >0.9 intra- and inter-batch Pearson correlation coefficients.
  • Identified known and potential off-targets for 20 kinase inhibitors across 87 samples, demonstrating over a 10-fold throughput increase compared to classical TPP.

Conclusions:

  • The proposed automated workflow significantly enhances throughput for drug target and off-target identification.
  • This approach offers a robust and efficient solution for proteomics sample preparation in drug discovery.
  • The integrated workflow addresses key challenges in identifying drug mechanisms and potential adverse effects.