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Protein (Enzyme) Design and Engineering:

We design and optimize enzymes using computational and experimental tools. We are interested in understanding the biophysical bases of enzyme function, uncovering the evolutionary implications of molecular recognition by enzymes, and engineering enzymes for applications aimed at addressing 21st-century challenges. These applications include biodegradation of pollutants and toxins, design of enzymatic therapeutics, and making cancer chemotherapy more specific.

Modeling and Designing Protease Substrate Specificity:

Site-selective proteolytic cleavage is a ubiquitous post-translational modification involved in the transfer of biological information (e.g., via cascades) in many cellular processes and their dysfunction. Proteases with “dialed in” substrate selectivities would be ideal catalytic drugs designed to irreversibly neutralize their target substrates (e.g., viral coat proteins) if their substrate selectivity can be precisely controlled. No robust and general method is available for protease substrate specificity design, in spite of ~25 years of efforts by protein chemists and chemical engineers. Our approach is to develop a mechanism-guided biophysical framework that allows design for both positive and negative substrate specificity, and tightly couple it with high-throughput experimental testing. We have developed a new computational design approach (Rubenstein et al. PLOS Comp. Biol, 2017) combining Rosetta and Amber calculations (Pethe et al. J. Mol. Biol. 2017), and developing high-throughput characterization approaches that combine computational predictions with experimental data obtained using yeast-based screening and deep sequencing (Pethe, Rubenstein et al. submitted). Our approach allows simultaneously querying and identifying millions of peptide sequences for cleavability.

Designing light- and proteolysis-controlled enzymes for advancing chemotherapy:

We are developing the ability to design enzymes such that they can be activated by environmental stimuli or external triggers such as light. The application we are pursuing is Directed Enzyme Prodrug Therapy, which is a promising approach to attenuate side-effects and thereby increase the therapeutic efficacy of conventional chemotherapy. In this approach an exogenous enzyme, targeted to the tumor by, for example, an antibody, activates a prodrug to generate toxicity locally. Animal studies and clinical trials have shown that slow clearance leading to activity of enzyme in non-tumor tissue is a major limitation. Computationally designed “smart” enzymes, that are constitutively inactive but are activatable by a tumor-specific stimulus (MMP-2 protease) or light (via use of attached azobenzene dyes) are expected to overcome these limitations. We have obtained good starting leads straight from computational design (~10X switches) (Blacklock et al. submitted), developed high-throughput screening approaches (Yachnin & Khare, Prot. Eng. Des. Sel. 2017). Biological characterization of designed enzymes is ongoing (Justin Drake, Rutgers).

Stimulus-responsive self-assembly of enzymes into novel supramolecular structures:

Enzymatic processes in nature are spatially organized. To develop the ability to similarly organize synthetic enzymatic pathways and develop efficient biosensors using enzymes (requiring high surface:volume), we have developed a modular design approach that allows construction of supramolecular assemblies in response to chemical and/or optical stimuli (Yang et al. ChemBioChem 2017 ). More recently, we have built fractal supramolecular topologies with extremely high surface area:volume ratios that organize component enzymes into hyperbranched dendritic supramolecular topologies (Hernandez, Hansen et al. submitted). Although fractals are ubiquitous in nature, our studies represent the first instance of designing such topologies using protein self-assembly. These studies are in collaboration with structural biologists Wei Dai, Sanghyuk Lee (Rutgers) and enzyme biochemist Lawrence Wackett (Minnesota), and aimed at enhancing the efficiency of pollutant biosensing and biodegradation.