Statistical analyses of protein sequence alignments identify structures and mechanisms in signal activation of sensor histidine kinases

Abstract

Summary Statistical analyses of genome sequence-derived protein sequence data can identify amino acid residues that interact between proteins or between domains of a protein. These statistical methods are based on evolution-directed amino acid variation responding to structural and functional constraints in proteins. The identified residues form a basis for determining structure and folding of proteins as well as inferring mechanisms of protein function. When applied to two-component systems, several research groups have shown they can be used to identify the amino acid interactions between response regulators and histidine kinases and the specificity therein. Recently, statistical studies between the HisKA and HATPase-ATP binding domains of histidine kinases identified amino acid interactions for both the inactive and active catalytic states of such kinases. The identified interactions generated a model structure for the domain conformation of the active state. This conformation requires an unwinding of a portion of the C-terminal helix of the HisKA domain that destroys the inactive state residue contacts and suggests how signal-binding determines the equilibrium between the inactive and active states of histidine kinases. The rapidly accumulating protein sequence databases from genome, metagenome and microbiome studies are an important resource for functional and structural understanding of proteins and protein complexes in microbes.

Toward rationally redesigning bacterial two-component signaling systems using coevolutionary information

“…TCS signaling partners are often adjacent to one another on the genome, e.g., cognate pairs from the same operon, a number of studies (11,(13)(14)(15)(16)(17)(18)(19)(20) have applied statistical methods to collections of cognate pairs to identify the evolutionarily conserved interactions between HK and RR signaling partners from their multiplesequence alignments (MSA). These studies extend upon early work using statistical methods to infer protein-protein interactions (21,22) from coevolutionary data.…”

Charting the Signal Trajectory in a Light-Oxygen-Voltage Photoreceptor by Random Mutagenesis and Covariance Analysis

“…Second, it can be challenging to untangle direct interactions between residue pairs from indirect interactions mediated by a third intermediary residue that directly interacts with the first two residues. The latter aspect is efficiently addressed by advanced covariance analysis methods (30) such as the direct-coupling analysis (31,32) and the protein-sparse-inverse-covariance (PSICOV) (24,33) strategy.…”

Evidence that Autophosphorylation of the Major Sporulation Kinase in Bacillus subtilis Is Able To Occur in trans

“…For example, one such study suggests that length and handedness of the ␣-helix bundle loops in the DHp domain are important for determining whether histidine kinases autophosphorylate in cis or in trans (45). Another such study suggests that a certain signal binding to the sensor domain causes helical unwinding in the C-terminal DHp and CA domains, resulting in the transition from the inactive to the active conformation (46). However, actual experimental data are required to directly support these assumptions.…”