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

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 c

SUBMITTER: Singh M 

PROVIDER: S-EPMC19638 | biostudies-literature | 1998 Mar

REPOSITORIES: biostudies-literature