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Related Concept Videos

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Updated: Oct 28, 2025

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Prediction and analysis of redox-sensitive cysteines using machine learning and statistical methods.

Marcus Keßler1, Ilka Wittig2, Jörg Ackermann1

  • 1Molecular Bioinformatics Group, Institute of Computer Science, Goethe-University, Robert-Mayer-Str. 11-15, 60325, Frankfurt am Main, Germany.

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Summary

Researchers identified key structural features predicting cysteine sensitivity to reactive oxygen species (ROS) modifications. These findings aid in understanding cellular signaling and predicting redox-sensitive sites in proteins.

Keywords:
cysteinehuman mitochondrial complex Imachine learningpost-translational modificationproteomicsredox

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Area of Science:

  • Biochemistry
  • Molecular Biology
  • Computational Biology

Background:

  • Reactive oxygen species (ROS) induce both damage and signaling via cysteine post-translational modifications.
  • Factors influencing cysteine sensitivity to redox modification remain largely unknown.

Purpose of the Study:

  • To investigate structural and sequence features governing cysteine redox modifiability.
  • To develop predictive models for identifying redox-sensitive cysteines.

Main Methods:

  • Utilized statistical and machine learning approaches.
  • Analyzed structural and sequence features of cysteines.
  • Developed predictive models based on identified features.

Main Results:

  • Higher cysteine exposure, lack of secondary structure, and proximity to positively charged amino acids predict redox sensitivity.
  • Modified cysteines are often located near other post-translational modifications, like phosphorylated serines.
  • Successfully predicted redox-modifiable cysteines in human mitochondrial complex I.

Conclusions:

  • Cysteine modifiability by ROS is strongly influenced by local structural and sequence context.
  • Predictive models can identify novel redox-sensitive cysteines in proteins.
  • Understanding these features is crucial for deciphering redox signaling pathways.