Computational learning reveals coiled coil-like motifs in histidine kinase linker domains.
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.
Singh
M
M
Center for Discrete Mathematics and Theoretical Computer Science (DIMACS) and Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.
Berger
B
B
Kim
P S
PS
Berger
J M
JM
Cochran
A G
AG
D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D005069:Evaluation Studies as Topic; D000071677:Histidine Kinase; D016013:Likelihood Functions; D008565:Membrane Proteins / Q000737:chemistry; D011494:Protein Kinases / Q000737:chemistry*; D017434:Protein Structure, Tertiary; D015398:Signal Transduction
D008565:Membrane Proteins; D011494:Protein Kinases; D000071677:Histidine Kinase