Protein engineering involves the design of known protein structures to introduce an improved or completely novel function. This is accomplished by either modifying the existing structure (i.e., redesign) to achieve an altered functionality or by introducing a completely new binding site.

(i) Protein Redesign for Altered Specificity

In this project computational modeling was first tested by altering the effector binding specificity of the bacterial transcriptional regulatory protein AraC to molecules other than L-arabinose. A systematic computational formulation was developed and employed to accurately reflect the relative strengths with which wild-type AraC binds various compounds, and were being used to predict mutagenesis strategies resulting in altered binding selectivity. Our initial goal was to design proteins capable of distinguishing between D- and L-arabinose. Our model identified mutations near the binding pocket for improving the selectivity towards the desired effector. In another study, we used the modified version of IPRO equipped with different implicit solvation modules to redesign Candida boidinii xylose reductase (CbXR) to use NADH as its cofactor by finding the optimal set of mutations in the CbXR binding pocket. Calculated cofactor binding energy was verified as a good surrogate to computationally drive cofactor alteration. We performed site-directed mutagenesis to redesign CbXR according to the designs predicted by IPRO. The mutant with lowest binding energy for NADH was found to have activity for NADH while having abolished activity for NADPH.

(ii) Grafting a New Binding Pocket into an Existing Fold

In this project we introduced a new computational procedure OptGraft for placing a novel binding pocket onto a protein structure so as its geometry is minimally perturbed. This was accomplished by introducing a two-level procedure where we first identify where are the most appropriate locations to graft the new binding pocket into the protein fold by minimizing the departure from a set of geometric restraints using mixed-integer linear optimization. Upon identifying the suitable locations that can accommodate the new binding pocket CHARMM energy calculations were employed to identify what mutations in the neighboring residues, if any, are needed to ensure that the minimum energy conformation of the binding pocket conserves the desired geometry. OptGraft was successfully used to guide our experimental studies for transferring a calcium binding pocket from thermitase protein (PDB: 1thm) into the first domain of CD2 protein (PDB:1hng)

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