Computational learning reveals coiled coil-like motifs in histidine kinase linker domains

Mona Singh, Bonnie Berger, Peter S. Kim, James M. Berger, Andrea G. Cochran

Research output: Contribution to journalArticle

60 Scopus citations

Abstract

The recent rapid growth of protein sequence databases is outpacing the capacity of researchers to biochemically and structurally characterize new proteins. Accordingly, new methods for recognition of motifs and homologies in protein primary sequences may be useful in determining how these proteins might function. We have applied such a method, an iterative learning algorithm, to analyze possible coiled coil domains in histidine kinase receptors. The potential coiled coils have not yet been structurally characterized in any histidine kinase, and they appear outside previously noted kinase homology regions. The learning algorithm uses a combination of established sequence patterns in known coiled coil proteins and histidine kinase sequence data to learn to recognize efficiently this coiled coil-like motif in the histidine kinases. The common appearance of the structural motif in a functionally important part of the receptors suggests hypotheses for kinase regulation and signal transduction.

Original languageEnglish (US)
Pages (from-to)2738-2743
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume95
Issue number6
DOIs
StatePublished - Mar 17 1998

All Science Journal Classification (ASJC) codes

  • General

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