Dennis Bromley

The structural dynamics of proteins are integral to protein function; if these structural dynamics are altered by mutation, the function of the protein can be altered as well, potentially resulting in disease. Experimental structure-determination with x-ray crystallography and Nuclear Magnetic Resonance (NMR) can be useful in determining mutant protein structures, but detailed, high-resolution dynamics data can be difficult to ascertain. Molecular Dynamics (MD) simulation is a high temporal- and spatial-resolution in silico method for dynamic protein structure determination. Unfortunately, the data generated by MD simulations can be too large for standard analysis tools. Here I describe a novel visual-analytics tool called DIVE that was specifically created to handle large, structured datasets like those generated by MD simulations. Using DIVE, I analyzed MD simulation-data of disease-associated mutations to the α-Tocopherol Transfer Protein (α-TTP) and to the p53 tumor suppressor protein. In addition to mutant structural-analysis and characterization, I also used DIVE to develop an algorithm for identifying regions of mutant proteins that are amenable to ‘rescue’, or ligand-mediated stabilization that can suppress the destabilizing effect of mutations. The results of these investigations highlight the utility of big-data, visual-analytics approaches to exploring MD simulation data.