Computational modeling of transition metals in medicinal chemistry: Realistic models to probe metal-biomolecule binding energetics

My research mainly focused on exploring the role of transition metal (TM) complexes in a biochemical context using atomistic molecular modeling. The application of one of the computationally least expensive high-level quantum chemical methods, Density Functional Theory, and classical mechanical methods allowed the modeling of relatively large TM complexes in order to understand their chemical behavior.Chapter one of this thesis describes four studies centered around one of the most widely used anti-cancer drugs, cisplatin. This simple Pt(II) complex exerts its anti-cancer activity by binding to adjacent purine bases of DNA in a bifunctional manner. The first two studies involve the thermodynamics and kinetics of formation of the most cytotoxic bifunctional lesions, the 1,2-intrastrand GpG and ApG adducts with DNA. The third study deals with the selective activity of the R,R enantiomer of a cisplatin analog, oxaliplatin, while the fourth study explores why guanine forms intrinsically stronger bonds compared to adenine.Chapter two describes the potential role of Cu(II) in the neurotoxicity of Alzheimer's disease (AD). AD is characterized by insoluble, fibrillous plaques in brain tissues consisting of Amyloid-beta peptides (Abeta). A proposed mechanism of pathogenecity in AD involves the formation of an Abeta-Cu complex which catalytically activates dioxygen to generate H 2O2. One of the first steps in studying such a catalytic reaction is an atomistic description of the Abeta-Cu complex. To this end, we used the relative copper-binding free energies of a series of Abeta-Cu complexes with different ligands, in order to predict the most plausible ligands around the copper center.