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Updated: Jul 8, 2026

Synthesis and Structure Determination of µ-Conotoxin PIIIA Isomers with Different Disulfide Connectivities
Published on: October 2, 2018
Predicting disulfide bond connectivity in proteins by correlated mutations analysis
Rotem Rubinstein1, Andras Fiser
1Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA. rrubinst@aecom.yu.edu
Predicting disulfide bond connectivity in proteins is aided by analyzing correlated mutations. This method accurately identifies cysteine pairs that form disulfide bonds, improving protein structure and function annotation.
Area of Science:
- Proteomics
- Bioinformatics
- Structural Biology
Background:
- Disulfide bond connectivity prediction is crucial for protein structural and functional annotation.
- Previous research indicates correlated mutations between cysteines within disulfide bonds.
Purpose of the Study:
- To develop and evaluate a method for predicting disulfide bond connectivity using correlated mutation patterns.
- To investigate the relationship between cysteine mutations and disulfide bond formation.
Main Methods:
- Analysis of correlated mutation patterns in multiple sequence alignments of proteins.
- Utilizing proteins with known experimental structures and varying disulfide bond numbers across different evolutionary distances.
Main Results:
- Observed frequent variation in disulfide bond connectivity within protein families.
- Identified that 99% of non-conserved disulfide bonds involve cysteine pairs that mutate in concert.
- Demonstrated that cysteine substitutions in disulfide bonds occur as pairs.
Conclusions:
- The developed method accurately predicts disulfide bond connectivity based on correlated mutations.
- The findings support the hypothesis that oxidized cysteines form pairs and co-mutate.
- The method achieves prediction accuracies of 73%, 69%, and 61% for proteins with two, three, and four disulfide bonds, respectively.
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