Probing the Evolutionary Dynamics Of WW Domains Using the Gaussian Network Model and Nuclear Magnetic Resonance

Proteins are known to be dynamic molecules, whose motions are closely related to their biological activity. Computer simulations have proven to be an indispensable tool in characterizing these motions. However, molecular dynamics simulations are limited by the computational costs of adequately sampling the longer timescales (Ì_å_s-s) necessary for characterizing the slower motions (Ì_å_s-ms) of biomolecules. A current method for combating this issue is the application of coarse grain network models, such as the Gaussian Network Model (GNM), to describe portions of the dynamics of proteins. GNM has proven to be, thus far, a fruitful analytical method for characterizing the amplitude of fluctuations about the native states of proteins. In an effort to understand the evolutionary pressures governing the specific functional dynamics conserved in proteins, perturbations are introduced in the form of single to multiple residue mutations. The mutants are then characterized experimentally in order to realize the relationship between specific residues and their functional role in the protein. Proposed herein is a novel application and validation of GNM as a technique to assess the differences in flexibility of a functional loop when single and multiple residue mutations are introduced. This application constitutes new method for predicting the effects of mutations on protein dynamics. Research Covered: ÌøåÀå_ Experimentally characterize the flexibility of the WW Loop 1 in: 1. wild type WW domain (RSSG ) 2. WWS19 mutant: single residue deletion (RSG ) 3. KSKK WW domain mutant: entire loop 1 mutation to alter binding specificity ( KSKK ) ÌøåÀå_ Apply GNM to characterize Loop 1 through slowest mode K-shapes in order to view the effects of mutations mentioned previously on loop dynamics