UCLA

Presented here are a trio of computational projects that, on the surface, have seemingly little in common: (1) proposing a new bioactive conformation of epothilone; (2) creating an amyloid binding model that rationalizes the affinities of a series of DDNP analogs that act as neuroimaging probes; and (3) designing a program for the rapid filtering and ranking of enzyme designs. However, the solution for all of these projects involved focusing on structural distortion energies or penalties. In the case of epothilone, a crystallographic structure, a transferred cross- correlated relaxation NMR structure, and a slew of computational structures all claiming to correctly identify the bioactive conformation of epothilone have already been published. We initially set out to provide corroborating computational evidence for one of these previously proposed conformations, but by correlating distortion energies incurred upon binding and experimental IC50 values, we have discovered a new bioactive conformation of epothilone that fits computational models and experimental data. In a similar fashion, we have shown that the degree of steric strain introduced upon the binding of DDNP to the tau amyloid correlates well with the experimentally determined binding affinities. We have created a model that qualitatively explains binding affinity data and structure-activity relationships for a new set of DDNP analogs. Finally, we have shown that analyzing the distortion from the ideal quantum mechanically computed geometry for an enzyme-catalyzed transition state can be an efficient way of filtering and ranking large numbers of computationally designed enzymes. This led to the creation of two novel computer programs: EDGE (Enzyme Design Geometry Evaluation), that performs these computations in an embarrassingly parallel fashion; and an enzyme design scientific workflow in Kepler that harnesses the power of the UC Grid to perform enzyme design in a massively parallel fashion.