Conformational sub-states in enzyme catalysis: Applications to ribonuclease

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DESCRIPTION (provided by applicant): Enzymes perform the designated function of catalyzing chemical reactions by serving more than a scaffold for bringing together the reactants. The role of structure in enzyme function has been known for more than a century now; however, more recent evidence suggests that a functioning enzyme exists in an ensemble of conformations under ambient physiological conditions. The ensemble view of enzyme structure suggests that it can sample conformational sub-states that exhibit function promoting structural and dynamical features. Further, evidence from experiments and computational modeling suggest that transitions between these conformational sub-states enable substrate recognition and catalysis. Quantitative insights into these functionally relevant sub-states remains challenging, particularly due to the wide range of time-scales involved, limited window of resolution for individual techniques and the fact that some of the sub-states can be potentially short-lived. We address these issues by developing a joint computational-experimental framework to identify and characterize such functionally relevant sub-states in the context of enzyme function. In addition to identifying structural intermediates, our framework will quantify the relative population of the conformations in various sub-states as well as enable their linkage to kinetics of enzyme function through the catalytic cycle. This integrated approach will be used to investigate the bio-medically relevant ribonuclease (RNase) family of proteins and enzymes. In particular, we will: (1) Develop a theoretical framework to identify and characterize the multi-scale hierarchy in the conformational landscape of proteins; (2) Utilize the developed framework to investigate the RNase fold members and their ability to access distinct conformational sub-states, including functionally relevant sub-states along the catalytic cycle; (3) Validate the developed model and predicted sub- states by integrating nuclear magnetic resonance (NMR) relaxation dispersion experiments. The developed methodology and models will be improved by iterative interaction between the 3 PIs with different expertise spanning theoretical biophysics, computational simulations and experimental techniques. Overall, our studies will have implications in the design of novel inhibitors of RNase function in the context of neurotoxicity, angiogenesis and anti-pathogenicity.