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

^*Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) and Department of Computer Science, Princeton University, Princeton, NJ 08544; ^†Mathematics Department and Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139; ^‡Howard Hughes Medical Institute and Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142; ^§Whitehead Institute, Nine Cambridge Center, Cambridge, MA 02142; and ^¶Department of Protein Engineering, Genentech, Inc., 460 Point San Bruno Boulevard, South San Francisco, CA 94080

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.

Footnotes

↵ ‖ To whom reprint requests should be addressed. e-mail: andrea{at}gene.com.
↵ ** Coiled coil motifs previously have been noted in unique amino-terminal domains of the histidine kinases Nik-1 (20) and TodS (21). Predicted coiled coil domains in the nonreceptor serine/threonine kinase TOUSLED (22) and tyrosine kinase Fes (23) are important for oligomerization and autophosphorylation.
↵ ‡‡ Information on the learncoil algorithm may be obtained by email to learncoil{at}theory.lcs.mit.edu. The histidine kinase probability table and the paircoil program are available at http://theory.lcs.mit.edu, in the learncoil and paircoil directories, respectively.
↵ †† www.ncbi.nlm.nih.gov/Entrez/ A list of histidine kinase sequences used in this study may be obtained from A.G.C.
↵ §§ The exception is halobacterial phototaxis transducers (Htr family) (49–52). newcoils and paircoil predict numerous high likelihood coiled coils throughout Htr cytoplasmic domains, whereas learncoil-derived histidine kinase tables assign the linker regions much higher likelihoods than other regions (data not shown).
ABBREVIATION:

CC block,

coiled coil block