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Related Concept Videos

Phosphorylation01:02

Phosphorylation

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The addition or removal of phosphate groups from proteins is the most common chemical modification that regulates cellular processes. These modifications can affect the structure, activity, stability, and localization of proteins within cells as well as their interactions with other proteins.
During phosphorylation, protein kinases transfer the terminal phosphate group of ATP to specific amino acid side chains of substrate proteins. Serine, threonine, and tyrosine are the most commonly...
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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
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Protein Kinases and Phosphatases02:54

Protein Kinases and Phosphatases

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Proteins undergo chemical modifications that trigger changes in the charge, structure, and conformation of the proteins. Phosphorylation, acetylation, glycosylation, nitrosylation, ubiquitination, lipidation, methylation, and proteolysis are various protein modifications that regulate protein activity. Such modifications are usually enzyme-driven.
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Updated: Jul 11, 2025

Resin-Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation
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Predicting cysteine reactivity changes upon phosphorylation using XGBoost.

Jing Cao1, Yan Xu1

  • 1Department of Statistics, University of Science and Technology Beijing, China.

FEBS Open Bio
|November 15, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning predicts how phosphorylation changes cysteine reactivity in proteins, aiding in understanding protein function and disease links. This approach accelerates proteome-wide analysis for clinical insights.

Keywords:
XGBoostcysteine reactivitymachine learningphosphorylation proteinsprotein functions

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Cysteine reactivity is crucial for protein function and is modulated by phosphorylation.
  • Current experimental methods to study these changes are limited in scale.
  • Machine learning offers a promising avenue to accelerate these investigations.

Purpose of the Study:

  • To develop a machine learning model to predict changes in cysteine reactivity due to phosphorylation.
  • To identify proteins and pathways affected by phosphorylation-induced cysteine reactivity changes.
  • To provide a computational tool for proteome-wide analysis of cysteine reactivity.

Main Methods:

  • Utilized protein sequence, proximity to phosphorylation sites, and intrinsically disordered region scores to represent cysteines.
  • Employed elastic net for feature selection and XGBoost for building binary classifiers.
  • Performed function enrichment analysis on predicted proteins with altered cysteine reactivity.

Main Results:

  • XGBoost achieved high performance with AUCs of 0.8192 (occurrence) and 0.9203 (direction) in independent tests.
  • A successive two-classifier approach yielded 0.7568 accuracy for predicting no change, increase, or decrease in reactivity.
  • Enrichment analysis linked altered cysteine reactivity to cancer, autosomal dominant diseases, and viral infections.

Conclusions:

  • Phosphorylation-induced cysteine reactivity changes are site-specific and predictable using XGBoost algorithms.
  • The developed model offers an efficient method for proteome-wide exploration of cysteine reactivity.
  • This facilitates deeper understanding of protein functions and potential clinical applications.