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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Related Experiment Video

Updated: Mar 16, 2026

Resin-Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation
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Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

Ming-An Sun1, Qing Zhang1, Yejun Wang2

  • 1State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, People's Republic of China.

BMC Bioinformatics
|August 25, 2016
PubMed
Summary

We developed a new computational tool, the Redox-Sensitive Cysteine Predictor (RSCP), to identify redox-sensitive cysteines using only protein sequence data. This method offers broader applicability than structure-dependent tools for understanding protein redox regulation.

Keywords:
Post-translational modificationReactive oxygen speciesRedox-sensitive cysteineSVM-based recursive feature eliminationSupport vector machine

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Proteomics

Background:

  • Reactive oxygen species (ROS) impact protein structure and function, with cysteine oxidation playing key roles in cellular signaling and redox regulation.
  • Existing computational tools for predicting redox-sensitive cysteines are limited, often focusing only on specific enzyme types or requiring protein structural data.

Purpose of the Study:

  • To develop a novel, sequence-based computational method for predicting redox-sensitive cysteines.
  • To overcome the limitations of existing methods that rely on protein structure or are restricted to specific protein families.

Main Methods:

  • Analyzed sequence-based features including sequential distance to nearby cysteines, PSSM profiles, and predicted secondary structures.
  • Employed Support Vector Machine Recursive Feature Elimination (SVM-RFE) for feature selection.
  • Developed the Redox-Sensitive Cysteine Predictor (RSCP), a Support Vector Machine (SVM) classifier utilizing only primary protein sequences.

Main Results:

  • The RSCP achieved an accuracy of 0.679, sensitivity of 0.602, specificity of 0.756, MCC of 0.362, and AUC of 0.727 on the RSC758 dataset using 10-fold cross-validation.
  • Performance on the BALOSCTdb dataset was comparable to structure-based methods.
  • Validation on an independent dataset demonstrated robustness and superior accuracy for predicting redox-sensitive cysteines in non-enzyme proteins.

Conclusions:

  • A sequence-based classifier (RSCP) for predicting redox-sensitive cysteines has been successfully developed.
  • The primary advantage of RSCP is its independence from protein structure data, enabling wider applications.
  • Accurate prediction of redox-sensitive cysteines enhances understanding of cysteine redox biology, complements proteomics, and aids experimental validation.