The Impact of Experimental, Protein Structure on our Ability to Model Protein Function

Gaining an accurate understanding of 3D protein:ligand complex structure, including proper protonation and explicit solvent effects, is crucial for obtaining meaningful results from docking, molecular dynamics, thermodynamic calculations, active site exploration, and ultimately lead optimization in structure based drug discovery (SBDD). In this work, we measure the impact of improved experimental structure - available with the advent of modern, quantum mechanics-based X-ray crystallographic refinement methods - on our understanding of protein function. We will discuss results from the study of docking/scoring (i.e. binding affinity prediction) as a function of structure quality as obtained through the characterization and re-refinement of several, “standard” highly-curated data sets including the Astex Diverse Set, CSAR, Iridium, and others. For each considered set, we have refined the associated structures using advanced, chemically rigorous, QM/MM crystallographic methods, explored key protonation states, and chosen the conformer which most closely matches the experimental density. The calculated binding affinities both before and after re-refinement have been determined using “name brand” score functions, and these calculated affinities have been compared to experimental binding affinity data in order to measure the impact improved structure on in silico characterization. In addition to the impact of structure as it pertains to affinity prediction, improvements in experimental density agreement (e.g. RSR, RCC, and ZDD) and in ligand strain will also be discussed.