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 characteriz e 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 i terative learning algorithm, to analyze possible coiled coil domains i n histidine kinase receptors. The potential coiled coils have not yet been structurally characterized in any histidine kinase, and they appe ar outside previously noted kinase homology regions. The learning algo rithm uses a combination of established sequence patterns in known coi led coil proteins and histidine kinase sequence data to learn to recog nize efficiently this coiled coil-like motif in the histidine kinases. The common appearance of the structural motif in a functionally impor tant part of the receptors suggests hypotheses for kinase regulation a nd signal transduction.