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Bioinformatics. 2008 Feb 15;24(4):498-504. doi: 10.1093/bioinformatics/btm637. Epub 2008 Jan 18.

Predicting disulfide bond connectivity in proteins by correlated mutations analysis.

Author information

1
Department of Biochemistry, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA. rrubinst@aecom.yu.edu

Abstract

MOTIVATION:

Prediction of disulfide bond connectivity facilitates structural and functional annotation of proteins. Previous studies suggest that cysteines of a disulfide bond mutate in a correlated manner.

RESULTS:

We developed a method that analyzes correlated mutation patterns in multiple sequence alignments in order to predict disulfide bond connectivity. Proteins with known experimental structures and varying numbers of disulfide bonds, and that spanned various evolutionary distances, were aligned. We observed frequent variation of disulfide bond connectivity within members of the same protein families, and it was also observed that in 99% of the cases, cysteine pairs forming non-conserved disulfide bonds mutated in concert. Our data support the notion that substitution of a cysteine in a disulfide bond prompts the substitution of its cysteine partner and that oxidized cysteines appear in pairs. The method we developed predicts disulfide bond connectivity patterns with accuracies of 73, 69 and 61% for proteins with two, three and four disulfide bonds, respectively.

PMID:
18203772
DOI:
10.1093/bioinformatics/btm637
[Indexed for MEDLINE]

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