Research Abstracts - 2006

Erythropoietin: A Case Study of Cytokine Signaling and Potency

Mala L. Radhakrishnan & Bruce Tidor

Motivation

Erythropoietin (Epo) is a crucial hormone involved in the maturation of red blood cell precursors. People with renal failure must take injections of Epo to avoid serious anemia [1]. Unfortunately, these external sources of Epo are rapidly cleared from the body, and therefore, a current priority is to increase the potency of these drugs by increasing their in vivo half-life and by altering their signaling properties. The erythropoietin system is particularly interesting, as it provides many avenues of investigation and potential mechanisms for modulation of potency. Using molecular and kinetic models, we are exploring the roles of binding kinetics, endocytic trafficking, and signal production/duration. By doing structural computations at the molecular level, we are probing how location-specific binding can affect cytokine trafficking to increase Epo's half-life, and we are suggesting specific mutations to achieve this effect. On the cellular level, we have also developed a kinetic model applicable to general cytokine systems that allows us to understand what kinetic regimes will generate a strong signal, a long half-life, or both. Using both models in conjunction will allow us to propose novel, potent cytokine molecules and systems.

Figure 1. Cartoon representation of the Erythropoietin hormone bound to the extracellular portion of its receptor (PDB ID 1EER [2]).

Our Approach

We wish to understand how various structural and systems-level characteristics of the Epo system contribute to its overall potency. Therefore, our work combines both molecular-level modeling and systems-level modeling. At the systems level, we want to determine the binding kinetics that would optimize cytokine potency; At the molecular level, we wish to introduce specific mutations or changes to the cytokine (or its receptor) to realize the appropriate binding properties. Moreover, studying these mutants allows us to build a better systems-level model as well. The ongoing cycle of systems- and molecular-level models, coupled with experimentation, is the key to this study.

Using Cellular-Level Models As Tools for Improving Cytokine Potency

Based on our current knowledge of Epo and other typical cytokine systems, we have constructed a "generic" cytokine model based on ordinary differential equations that can be parameterized for a particular cytokine. We have done a comprehensive analysis of this model to understand how overall cytokine potency varies throughout the model's parameter space. We found that in this model, the need to signal strongly in the short term favors tight-binding ligands, while the need to signal well over the long term favors weak binders. This tension leads to an optimal set of binding kinetics, an idea also noted in [3] and [4]. We have analyzed in detail how the optimal binding kinetics vary as we vary other parameters in the model, and we also analyzed the difference between modulating a cytokine's affinity via its on-rate versus its off-rate. Applying this model to the Epo system, using experimentally determined parameters [5], we predict that increasing the off-rate of the Epo-EpoR interaction may increase the long-term potency of Epo.

Pictorial representation of the ordinary differential equation model used for analyzing the systems-level determinants of cytokine potency

Engineering Mutant Forms of Epo and EpoR With Altered Cytokine Trafficking Properties

At the molecular level, we have computationally designed mutant forms of both Epo and its receptor, EpoR. Some of these mutants were computationally predicted to bind worse than wild type, while others were predicted to bind equally well or better. Still others were predicted to bind well at the cell surface but then to lose binding affinity in the lower-pH environment of the endosome. The binding predictions were based on energetic calculations using a continuum solvent electrostatics model [6] and molecular mechanics for the nonelectrostatic contributions[7]. Several EpoR mutants were experimentally constructed, and, in general, their binding properties qualitatively matched the computational predictions. These mutants are currently being experimentally tested for their trafficking and signaling properties. We will use the results of these tests either to validate or to improve the systems-level model from above. If our cellular-level model is currently an accurate representation of the system, we would predict that some of these "first-generation" mutants will show increased long-term potency over wild type, thus being the first step toward better therapeutics.

Left: A snapshot of interaction residues at the Epo/EpoR interface in the wild-type system. Note that there is a histidine at the interface that is computationally predicted to be responsible for pH-dependent binding. Removal of this histidine may therefore alter trafficking properties. The image on the right shows the binding interface of two mutants (Histidine mutated to Phenylalanine or Glutamate) with potentially altered trafficking.

Research Support:

This work is partially being funded by the National Institutes of Health and by a Department of Energy Computational Science Graduate Fellowship (DE-FG02-97ER25308).

References:

[1] S. Elliott et al. Enhancement of therapeutic protein in vivo activities through glycoengineering. Nature Biotech., 10:1-8, 2003.

[2] R. S. Syed et al. Efficiency of signalling through cytokine receptors depends critically on receptor orientation. Nature, 395:511-516, 1998.

[3] E.M. Fallon and D.A. Lauffenburger. Computational model for effects of ligand/receptor binding properties on interleukin-2 trafficking dynamics and T-cell proliferation. Biotechnol. Prog., 16:905-916, 2000.

[4] D.A. Lauffenburger, E.M. Fallon, and J.M. Haugh. Scratching the (cell) surface: cytokine engineering for improved ligand/receptor trafficking dynamics. Chem and Biol., 5:R257-R263, 1998.

[5] A.W. Gross and H.F. Lodish. Cellular trafficking and degradation of erythropoietin and novel erythropoiesis stimulationg protein (NESP). J Biol. Chem., 281:2024-2032, 2006.

[6] M.K. Gilson, K.A. Sharp, and B.H. Honig. Calculating the electrostatic potential of molecules in solution: Method and error assessment. J Comp. Chem., 9:327-335, 1987.

[7] B.R. Brooks et al. CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J Comp. Chem., 4:187-217, 1983.