UCSF

Predicting new ligands and their binding poses for a protein target relies on an understanding of the physical forces that exist between the water-submerged protein and ligand. The relative favorability of these molecular and atomic interactions between the protein and ligand compared with their interactions with water determine the binding affinity, which in turn can be converted into a binding free energy. Protein-ligand binding energetics are, with varying levels of success, encoded into scoring functions, which at their best, can only partially emulate the true binding affinity of a protein-ligand binding event. In the context of virtually screening millions or hundreds of millions of drug-like ligands, molecular docking algorithms take advantage of scoring functions to rank the binding energies of these molecules relative to one another to help prioritize the most promising ligands.

The focus of this dissertation is the balance between scoring function energy terms with an emphasis on water energetics, specifically the desolvation of the protein upon ligand binding. It is thought that our limited understanding of water is largely responsible for our limitations in discovering and designing drugs. This is due to the large number of roles that water can play, as well as its significant, and even dominant, contribution to protein-ligand binding energetics, which in the realm of molecular docking, is typically under-modeled or completely neglected.

First, I focus on the incorporation of receptor desolvation into the standard DOCK3.7 scoring function to more accurately model protein-ligand binding interactions by including further contributions of water. This is the original implementation of Grid Inhomogeneous Solvation Theory applied to the model cavity, cytochrome c peroxidate, and spearheaded by Trent Balius and Marcus Fischer. Second, I discuss an extension of GIST in DOCK3.7, a new implementation that relies on pre-computed Gaussian-weighted GIST receptor desolvation enthalpies. This results in negligible slowdown of the standard DOCK3.7 scoring function, similar performance to the original implementation of GIST, and the identification of new ligands for the drug-like model system, AmpC β-lactamase. The work on receptor desolvation contained within these two chapters inspires the name of this thesis, and were started in my rotation and have continued until the end. Third, I focus on the use of property-matched and property-unmatched decoys for use in retrospective enrichment calculations prior to running a large-scale molecular docking virtual screen. Decoy molecules share the same physical properties as ligands that bind a protein but are topologically dissimilar to ensure that they do not actually bind the protein. What we found was that charge mismatching between ligands and decoys could bias one’s docking setup towards artifactually strong performance. Chapter 3 focuses on how we both decreased and increased the property space of decoys relative to ligands to safeguard against these docking setup biases. Fourth, I employ this knowledge of protein-ligand binding affinities to identify novel selective melatonin receptor ligands that are active in in vivo circadian rhythm assays. Finally, I discuss my current project on the CB1 cannabinoid receptor in the context of analgesia, followed by future directions.