Title: “Computational Modeling of Protein Conformational Change and Molecular Interactions”
Sandor Vajda, PhD – BME (Advisor)
Maxim D. Frank-Kamenetskii, PhD – BME (Chair)
Dmitri Beglov, PhD – BME
Karen Allen, PhD – Chemistry
Adrian Whitty, PhD – Chemistry
Protein conformational change and interactions with other molecules serve as the basis for many biological processes such as metabolic control and cell signaling that are important for drug design and development of diagnostics. Structural details of these changes and interactions can be experimentally determined by X-ray crystallography or nuclear magnetic resonance (NMR) methods, whereas binding affinity can be measured by a variety of tools such as surface plasmon resonance (SPR). Under many circumstances, direct experimental analysis is either not possible or too time-consuming and expensive. Therefore, it is important to develop computational methods capable of characterizing protein conformational change and modeling interacting complexes. This thesis details four projects which involve computational modeling of binding pocket dynamics, protein-small molecule interactions and the formation of protein-protein complexes.
The first project provides structure-based analysis of cryptic site opening. Cryptic sites are pockets formed in ligand-bound proteins but not observed in unbound protein structures. Through analysis of crystal structures supplemented by molecular dynamics (MD) with enhanced sampling techniques, it was shown that cryptic sites can be grouped into three types: 1) “genuine” cryptic sites, which do not form without ligand binding, 2) spontaneously forming cryptic sites, and 3) cryptic sites impacted by mutations or off-site ligand binding. The second project details the effort to improve the accuracy of the solvent mapping server FTMap, which finds small molecule binding hot spots on proteins. More specifically, a statistical pairwise potential, which adopts the Decoy As Reference State (DARS) framework, was developed and added to the scoring function of FTMap. The third and fourth projects both involve using docking to predict the quality of protein-protein interactions in the form of binding affinity. The structural impact of a frequent mutation in the human pancreatic secretory trypsin inhibitor (PSTI) was explored using a hybrid approach of MD simulations and molecular docking. The fourth project aims at establishing a relationship between docked near-native hits and experimentally measured binding free energies of antibody-antigen interactions.