An aspartate to lysine mutation at position 13 of the chemotaxis regulatory protein CheY causes a constitutive tumbly phenotype when expressed at high copy number in vivo even though the mutant protein is not phosphorylatable. These properties suggest that the D13K mutant adopts the active, signaling conformation of CheY independent of phosphorylation, so knowledge of its structure could explain the activation mechanism of CheY. The x-ray crystallographic structure of the CheY D13K mutant has been solved and refined at 2.3 Å resolution to an R-factor of 14.3%. The mutant molecule shows no significant differences in backbone conformation when compared with the wild-type, Mg 2؉ -free structure, but there are localized changes within the active site. The side chain of lysine 13 blocks access to the active site, whereas its ⑀-amino group has no bonding interactions with other groups in the region. Also in the active site, the bond between lysine 109 and aspartate 57 is weakened, and the solvent structure is perturbed. Although the D13K mutant has the inactive conformation in the crystalline form, rearrangements in the active site appear to weaken the overall structure of that region, potentially creating a metastable state of the molecule. If a conformational change is required for signaling by CheY D13K, then it most likely proceeds dynamically, in solution.Cells continuously monitor various chemical and physical parameters in their surrounding environment and use this information to implement appropriate adaptive responses to changing conditions. This vital task is accomplished by a cascade of transient protein phosphorylation and dephosphorylation events arranged so that the flow of phosphoryl groups reflects environmental conditions. One ubiquitous class of signal transduction networks is the "two-component" regulatory systems that control diverse processes such as behavior, development, physiology, and virulence in bacteria, as well as adaptive processes in eukaryotes (for reviews, see Refs.
In this work, a closed-loop identification method based on a reinforcement learning algorithm is proposed for multiple-input multiple-output (MIMO) systems. This method could be an attractive alternative solution to the problem that the current frequency-domain identification algorithms are usually dependent on the attenuation factor. With this method, after continuously interacting with the environment, the optimal attenuation factor can be identified by the continuous action reinforcement learning automata (CARLA), and then the corresponding parameters could be estimated in the end. Moreover, the proposed method could be applied to time-varying systems online due to its online learning ability. The simulation results suggest that the presented approach can meet the requirement of identification accuracy in both square and non-square systems.
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