Role of Position K+4 in the Phosphorylation and Dephosphorylation Reaction Kinetics of the CheY Response Regulator

Abstract

Two-component signaling is a primary method by which microorganisms interact with their environments. A kinase detects stimuli and modulates autophosphorylation activity. The signal propagates by phosphotransfer from the kinase to a response regulator, eliciting a response. Response regulators operate over a range of time scales, corresponding to their related biological processes. Response regulator active site chemistry is highly conserved, but certain variable residues can influence phosphorylation kinetics. An Ala-to-Pro substitution (K+4, residue 113) in the Escherichia coli response regulator CheY triggers a constitutively active phenotype; however, the A113P substitution is too far from the active site to directly affect phosphochemistry. To better understand the activating mechanism(s) of the substitution, we analyzed receiver domain sequences to characterize the evolutionary role of the K+4 position. Although most featured Pro, Leu, Ile, and Val residues, chemotaxis-related proteins exhibited atypical Ala, Gly, Asp, and Glu residues at K+4. Structural and in silico analyses revealed that CheY A113P adopted a partially active configuration. Biochemical data showed that A113P shifted CheY toward a more activated state, enhancing autophosphorylation. By characterizing CheY variants, we determined that this functionality was transmitted through a hydrophobic network bounded by the β5α5 loop and the α1 helix of CheY. This region also interacts with the phosphodonor CheA P1 , suggesting that binding generates an activating perturbation similar to the A113P substitution. Atypical residues like Ala at the K+4 position likely serve two purposes. First, restricting autophosphorylation may minimize background noise generated by intracellular phosphodonors such as acetyl phosphate. Second, optimizing interactions with upstream partners may help prime the receiver domain for phosphorylation.

The Solvation of the E. coli CheY Phosphorylation Site Mapped by XFMS

“…X-ray crystallography has shown that the Mg 2+ ion, essential as cofactor for phosphorylation, is coordinated to 13D, 57D, and 59N, with water-mediated contact to 13D [ 22 , 23 , 27 ]; it has also documented hydrogen-bond rearrangements between 12D, 57D, 87T, and 109K triggered by activation. Studies have also explored the effects of charge substitutions on chemotaxis [ 28 ] and phosphorylation kinetics [ 29 ]. However, a structural or functional role for 18R and the β 2 -α 2 loop residues (34E, 35E, 37E, 38D) has not previously been recognized to our knowledge.…”

Predicted Functional and Structural Diversity of Receiver Domains in Fungal Two-Component Regulatory Systems

“…D+2 and T+2 surround the path to the phosphorylation site and exert influence via hydrophobic/hydrophilic interactions with the reactants and products ( 18 , 21 , 22 ). K+1, K+2, and K+4 affect the equilibria between catalytically active and inactive conformations ( 19 , 23 ). Different types of bacterial response regulators exhibit different combinations of amino acids at these variable positions, consistent with multivariate evolutionary tuning of reaction kinetics to suit their biological processes.…”

“…Bacterial receiver domains exhibit bidirectional allostery between the active site and at least two distinct functional surfaces ( 16 ). Communication between the active site and the α5α1 surface likely involves changes in the positioning of the conserved K ( 23 ). In many response regulators, movement of the T position to bind a phosphoryl group oxygen is coupled with burying an adjacent aromatic residue at K–3, thus altering the α4β5α5 surface.…”

“…Another prominent feature of Skn7 is the consistent presence of an Arg/Lys residue at K+4 ( Data Set S1 ), a position known to affect equilibria between active and inactive conformations ( 23 ). However, a lack of data makes it challenging to predict the functional consequences of basic residues at this position.…”

Dynamic correlations: exact and approximate methods for mutual information

“…Several computational studies have investigated mutual information in proteins using both exact ( McClendon et al 2014 , Cortina and Kasson 2016 , Long and Tian 2016 , Singh and Bowman 2017 , Sogunmez and Akten 2020 , Shao et al 2021 , Schneider and Antes 2023 ), mostly considering the magnitudes of fluctuations, and approximate calculations ( Lange and Grubmüller 2006 , Jana et al 2011 , Rennebaum and Caflisch 2012 , Invernizzi et al 2014 , Scarabelli and Grant 2014 , Köhler et al 2015 , Tiberti et al 2015 , Ahmed and Barakat 2017 , Milenkovic and Bondar 2018 , Zhang et al 2018 , Foster et al 2021 , Payne et al 2022 , Tekpinar and Yildirim 2022 ). By relying on the three approximations, calculations requiring shorter simulations or only static crystal structures may become possible.…”