UC Riverside

Interactions between biomolecules are essential to biological function, and fundamentally understanding the forces that drives these interactions is of great medical importance. Molecular modeling approaches represent a powerful set of tools for gaining a mechanistic perspective of biomolecular interactions at the atomic level. Due to the complexities of biological environments, a diverse set of molecular modeling methods are often needed to capture various aspects of biomolecular function. Here we present a computational framework that utilizes established molecular modeling methods, such as molecular dynamics, Poisson-Boltzmann electrostatics, and small-molecule docking, as well as includes novel tools for elucidating the role of electrostatics in protein association. Our novel computational tool, Analysis of Electrostatics Similarity Of Proteins (AESOP), utilizes theoretical mutagenesis, electrostatic clustering, and electrostatic free energies of association to evaluate the role of each charged residue in protein association. The AESOP framework has been applied to various biomolecular systems, including barnase-bartstar, the gold-standard system for protein electrostatics, as well as for the successful design of novel SUMO-4 substrate analogs. One biological system that is of key interest is the complement immune system, which is an ancient component of innate immunity. The complement system is involved in the opsonization and clearance of foreign pathogens, and achieves its function through a cascade of protein-protein interactions largely driven by electrostatics. The key role of electrostatics in complement function is further evidenced by the existence of electrostatic "hot-spots" on the surface of complement proteins, and by the fact that pathogens utilize electrostatics in complement targeted evasion mechanisms. Over-activation of the complement system is implicated in numerous autoimmune and inflammatory diseases, and as a result anaphylatoxin receptor C5aR has become an important drug target. We have developed a conformationally-sampled pharmacophore model for known C5aR antagonists, which has utility in the design/evaluation of novel C5aR antagonists. We have also performed virtual screening based on a newly developed model of the interaction between C5aR and known potent antagonist PMX-53, and have identified a structurally diverse set of potential C5aR ligands. These studies define a computational framework that has utility in the analysis of many other protein targets.