UCSF

Computational drug discovery is an active area of biophysics research due to the difficulties in estimating the free energy changes of small molecules binding to proteins. Considering the large search space of drug-like molecules, any estimates of binding must be fast as well as accurate. In this dissertation, I present new tools and models to improve computational drug discovery. The first project (Chapter 2) involves converting an expert-curated hierarchical database into a statistical potential for rapidly evaluating torsion strain in docked ligand poses. The second project concerns better understanding a new mode of ligand binding. New cryogenic electron microscopy (cryo-EM) structures of positron emission tomography (PET) radiotracers bound to protein fibrils show long, symmetric stacks of ligands within the fibrils. We present SymDOCK (Chapter 3) for accurately docking molecules to protein fibrils in symmetric, interacting stacks at a fast enough rate for large-scale docking. To better understand the effects on experimental observables from ligand-ligand interactions and entropy from the number of sites, we derive a new model for symmetric ligand binding to protein fibrils (Chapter 4). We lastly attempt to use our new tools and models to prospectively dock molecules against Alzheimer’s Disease tau fibrils (Chapter 5), where preliminary experimental results show some of our predicted hits do bind to the protein.