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Rotem Rubinstein

dblp:15/4458 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2008
0000-0003-3657-5984ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › molecular evolution
correlated mutation analysis
0.112008
Predicting disulfide bond connectivity in proteins by correlated mutations analysis · Bioinform. 2008
Bioinformatics and computational biology › protein structure prediction
disulfide connectivity prediction
0.112008
Predicting disulfide bond connectivity in proteins by correlated mutations analysis · Bioinform. 2008
Bioinformatics and computational biology › molecular evolution
protein evolution
0.112008
Predicting disulfide bond connectivity in proteins by correlated mutations analysis · Bioinform. 2008
Bioinformatics and computational biology
protein structure prediction
0.112008
Predicting disulfide bond connectivity in proteins by correlated mutations analysis · Bioinform. 2008

Methods — techniques the papers use, named apart from their topics

multiple sequence alignment · 0.1correlated mutation analysis · 0.1
YearPublicationVenuePosition
2008 Predicting disulfide bond connectivity in proteins by correlated mutations analysis
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.
Rotem Rubinstein, András Fiser
Bioinform.1