Department of Chemistry & Chemical Biology
Center for Integrative Proteomics Research
Piscataway, NJ 08854
Design principles of molecular recognition
The Khare lab will seek to understand the structural determinants of enzymatic specificity and reactivity using a combination of computational protein design and experimental characterization. Our goal is to develop a quantitative and predictive understanding of specificity at protein-ligand and protein-peptide interfaces; this will inform various therapeutic and synthetic applications.
Arising out of natural selection, the structures of proteins (and their complexes with small molecules, nucleic acids, and other proteins) display exquisitely fine-tuned molecular recognition, which is critical for life to operate. Under selection conditions, accurate molecular recognition must be robust to random perturbations such as mutations. Yet, natural proteins are also evolvable -- variation in a few amino acids can lead to profound changes in function, e.g. a new enzymatic activity can arise in an “old” enzyme. In other words, these molecular interactions have the fascinating property of being simultaneously functionally robust and plastic.
We would like to understand the underlying evolutionary and biophysical “design” principles of this property. We are taking a protein design approach for discovering and dissecting routes by which new functions (activities, specificities) can evolve. These mutational routes will shed light on the underlying landscape of functional robustness and plasticity. Our approach is inspired by natural evolutionary processes: in Nature, new enzymes and new substrate specificities arise by repurposing the catalytic machinery, i.e. protein functional groups and/or exogenous cofactors, of related existing enzymes by evolutionary sequence optimization. Sampling of new functions is achieved by accruing both point mutations and large-scale insertions/deletions (indels). Thus, we will emulate these natural evolutionary mechanisms on the computer and in the test tube. On the computational side, we will develop methods to perform this sampling -- especially large-scale indels -- to generate predictive models of both existing, and new (including non-natural) activities and substrate specificities. On the experimental side, we will use (and develop) high-throughput assays to characterize libraries of designed proteins for one or more functions.
The following are some of the underlying questions we hope to address:
Which structural features encode functional robustness and which features encode plasticity?
What structural relationships govern the balance of robustness and plasticity?
What, if any, are the limits on plasticity? Can the existing enzyme machinery be reused to obtain non-natural reactivities/specificity?
Answering these questions should, in principle, provide us with a framework to engineer new molecular interactions that go beyond naturally occurring ones. The ability to design novel catalysts and specificities will significantly aid in developing from bottom up, for example, tailor-made therapeutics and bioremediation agents/pathways that we will design using our approach.