Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 107, Issue 9, Pages 4069-4074Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.0909950107
Keywords
remote homology detection; motif recognition; structure; signal transduction; histidine kinase
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Funding
- National Institutes of Health [1R01GM080330-01A1]
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The recent explosion in newly sequenced bacterial genomes is outpacing the capacity of researchers to try to assign functional annotation to all the new proteins. Hence, computational methods that can help predict structural motifs provide increasingly important clues in helping to determine how these proteins might function. We introduce a Markov Random Field approach tailored for recognizing proteins that fold into mainly beta-structural motifs, and apply it to build recognizers for the beta-propeller shapes. As an application, we identify a potential class of hybrid two-component sensor proteins, that we predict contain a double-propeller domain.
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