An allosteric model for heterogeneous receptor complexes: understanding bacterial chemotaxis responses to multiple stimuli

Abstract

The classical Monod-Wyman-Changeux model for homogeneous allosteric protein complex is generalized in this article to model the responses of heterogeneous receptor complexes to multiple types of ligand stimulus. We show that the recent in vivo experimental data of Escherichia coli chemotaxis responses for mutant strains with different expression levels of the chemo-receptors to different types of stimulus [Sourjik, V. & Berg, H. C. (2004) Nature 428, 437-441] all can be explained consistently within this generalized Monod-Wyman-Changeux model. Based on the model and the existing data, responses of all of the strains (studied in this article) to the presence of any combinations of ligand (Ser and MeAsp) concentrations are predicted quantitatively for future experimental verification. Through modeling the in vivo response data, our study reveals important information about the properties of different types of individual receptors, as well as the composition of the cluster. The energetic contribution of the nonligand binding, cytoplasmic parts of the cluster, such as CheA and CheW, is also discussed. The generalized allosteric model provides a consistent framework in understanding signal integration and differentiation in bacterial chemotaxis. It should also be useful for studying the functions of other heterogeneous receptor complexes.

Fig. 1.

Illustration of a functional MCP complex, consisting of different types of membrane-bound receptors, Tar and Tsr, and other important cytoplasmic components, CheW (W) and CheA (A), in response to two stimuli, Ser and MeAsp, which bind to Tsr and Tar, respectively. The whole polar receptor cluster may contain many such functional complexes.

Fig. 2.

Fitting of our model (lines) to the 14 experimental response data sets (symbols) for the 10 CheRB^– mutant strains reported in ref. . (a) Response to MeAsp for strains 1–4. (b) Response to Ser for strains 1–4. (c) Response to MeAsp for strains 5–7. (d) Response to Ser for strains 8–10. The parameters of our model are given in Table 1.

Fig. 3.

The kinase activity (vertical axis) predicted by our model for strain 2 in the presence of both MeAsp and Ser concentrations. The contour lines for different activities in the ligand concentration space are also plotted.

Fig. 4.

The predicted kinase activity and response characteristics for strain 2 in the presence of two stimuli, Ser and MeAsp. (a and b) The responses of strain 2 to MeAsp (a) and Ser (b) in the presence of different background levels of Ser or MeAsp. (c and d) Each normalized response curve is fitted by a Hill function (S_max – S_min)(1 + ([L]/K_eff)^h)^–1 + S_min, and the resulting parameters, S_max, S_min, K_eff, and h for MeAsp (c) and Ser (d) responses are plotted against the concentrations of the Ser and MeAsp background concentrations, respectively.

Fig. 5.

The strain-dependent parameters from our model (see Table 1). (a) The number of receptors in the cluster in the 10 different strains. (b) The scaling factor A⁽⁰⁾ against the total receptor expression level T_t (in units of WT Tar expression level) for different strains; most of the values of A⁽⁰⁾ from the 10 strains collapse onto a curve that is fitted by function A⁽⁰⁾ = 0.0947/[1 + (1.43/T_t)^2.61] shown as a dotted line. The outlier at T_t = 4 corresponds to strain 3 where the Tar and Tsr are equally expressed as f_3,1 = f_3,2 = 2.