Grants and Contracts Details
To address the challenge of improving enzymatic conversion of biomass, we will use high--].performance computing to construct large--].scale molecular models and perform robust, cutting--].edge free energy calculations closely coupled with experiment to test our hypothesis that degree of processivity is directly related to ligand binding free energy as a result of the work required of the enzyme to decrystallize the substrate. Furthermore, we anticipate ligand binding free energy is a function of active site topology, dynamics, and chemical composition and can be used as a parameter to predict change in processivity upon mutation of the enzyme active site. This hypothesis has been mathematically formalized (Eqn. 1) in our publication in the Journal of the American Chemical Society. Briefly, we use a probabilistic definition of processivity, intrinsic processivity, PIntr, to represent theoretical maximum processive ability. Assuming equilibrium conditions, the Eyring equation provides the basis for relating binding free energy, ƒ¢Gb‹, to intrinsic processivity. Combined, we obtain the following mathematical relationship as the basis of a computational protein engineering protocol We will test this hypothesis in a model GH system (Serratia marcescens family 18 chitinases). The distinct catalytic mechanism of this family makes experimental characterization of processivity more straightforward than for any other accessible GH system, and experimental collaborators will soon have characterized the worldfs most comprehensive publicly available GH variant dataset related to enzyme processivity. All of the variables in Eqn. 1 are experimentally obtainable in the chitinase model system, while most are out of reach of common analytical techniques for observing cellulase behavior due to biphasic kinetics. The following objectives will test our hypothesis: Validate the relationship in wild--].type processive chitinases. The S. marcescens chitinolytic suite consists of two processive chitinases, ChiA and ChiB, and one non--].processive chitinase, ChiC. Experimentally, we will determine kcat and Km, leading to kon, from Michaelis--].Menten kinetic analysis of activity on chitosan. The binding dissociation constant, and ultimately koff, as well as ƒ¢Gb‹ will be determined using isothermal titration calorimetry. PIntr is calculated from kcat and koff. With these experimental values, we validate the general relationship. We will use Free Energy Perturbation with Hamiltonian--].Replica Exchange Molecular Dynamics (FEP/H--]. REMD) to calculate ƒ¢Gb‹ and confirm the accuracy of our calculations against experimental free energies. We have been working closely with scientists and software engineers at the Argonne Leadership Computing Facility in implementing this method, which has shown remarkable accuracy and improved performance over less rigorous methods. Currently, we have demonstrated ability to predict experimental binding free energies within error in 40 kDa proteins with ligands five monomers long. Predict processivity in mutant chitinases. From molecular models, we will suggest point mutations in the chitinase active sites likely to have the largest impact on processivity. We will calculate the binding free energies, again through FEP/H--].REMD, and make predictions as to the effect on chitinase processive ability. These predictions will be in the form of a general logarithmic relationship, as explicitly calculating kon and kcat is not yet readily and rapidly accessible. Experimental point mutations and subsequent kinetic analysis will confirm our rank--].ordered predictions. This dataset of binding free energies and experimentally measured processivity rates will identify the range of ligand binding free energies for which processive action is maintained yet ligand binding free energy is minimized, which will enhance biomass conversion. Long--].term, we will expand our predictions to cellulases for direct confirmation via processivity measurements or qualitative confirmation by observing enhanced hydrolytic ability of predicted active site mutations. With sufficient access to computing resources, funds will be used to support personnel focused on achieving these research goals including a graduate student and postdoctoral researcher depending on the award. Funds will also be used for dissemination of research (e.g., publication fees and travel). Personal computers for individuals associated with this research will also be purchased.
|Effective start/end date||6/1/14 → 5/31/15|
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