In this thesis, density functional theory (DFT) is used to investigate the mechanisms and reactivities of electrophilic and nucleophilic aromatic substitution reactions (SEAr and SNAr respectively). For SEAr, the σ-complex intermediate is preceded by one (halogenation) or two (nitration) π-complex intermediates. Whereas the rate-determining transition state (TS) for nitration resembles the second π-complex, the corresponding chlorination TS is much closer to the σ-complex. The last step, the expulsion of the proton, is modeled with an explicit solvent molecule in combination with PCM and confirmed to be a nearly barrierless process for nitration/chlorination and involves a substantial energy barrier for iodination. It is also shown for nitration that the gas phase structures and energetics are very different from those in polar solvent. The potential energy surface for SNAr reactions differs greatly depending on leaving group; the σ-complex intermediate exist for F-/HF, but for Cl-/HCl or Br-/HBr the calculations indicate a concerted mechanism. These mechanistic results form a basis for the investigations of predictive reactivity models for aromatic substitution reactions. For SEAr reactions, the free energy of the rate-determining TS reproduces both local (regioselectivity) and global reactivity (substrate selectivity) with good to excellent accuracy. For SNAr reactions good accuracies are obtained for Cl-/HCl or Br-/HBr as leaving group, using TS structures representing a one-step concerted mechanism. The σ-complex intermediate can be used as a reactivity indicator for the TS energy, and for SEAr the accuracy of this method varies in a way that can be rationalized with the Hammond postulate. It is more accurate the later the rate-determining TS, that is the more deactivated the reaction. For SNAr reactions with F-/HF as leaving group, the same method gives excellent accuracy for both local and global reactivity irrespective of the degree of activation. / <p>QC 20170510</p>

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Proteins are dynamic structural entities that are involved in many biophysical processes through molecular interactions with their ligands. Protein-ligand interactions are of fundamental importance for computer-aided drug discovery. Due to the fast development in computer technologies and theoretical methods, computational studies are by now able to provide atomistic-level description of structures, thermodynamic and dynamic properties of protein-ligand systems, and are becoming indispensable in understanding complicated biomolecular systems. In this dissertation, I have applied molecular dynamic (MD) simulations combined with several state of the art free-energy calculation methodologies, to understand structures and binding properties of several protein-ligand systems. The dissertation consists of six chapters. In the first chapter, I present a brief introduction to classical MD simulations, to recently developed methods for binding free energy calculations, and to enhanced sampling of configuration space of biological systems. The basic features, including the Hamiltonian equations, force fields, integrators, thermostats, and barostats, that contribute to a complete MD simulation are described in chapter 2. In chapter 3, two classes of commonly used algorithms for estimating binding free energies are presented. I highlight enhanced sampling approaches in chapter 4, with a special focus on replica exchange MD simulations and metadynamics, as both of them have been utilized in my work presented in the chapter thereafter. In chapter 5, I outlined the work in the 5 papers included in the thesis. In paper I and II, I applied, respectively, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and alchemical free energy calculation methods to identify the molecular determinant of the affibody protein ZAb3 bound to an amyloid b peptide, and to investigate the binding profile of the positive allosteric modulator NS-1738 with the α7 acetylcholine-binding protein (α7-AChBP protein); in paper III and VI, unbiased MD simulations were integrated with the well-tempered metadynamics approach, with the aim to reveal the mechanism behind the higher selectivity of an antagonist towards corticotropin-releasing factor receptor-1 (CRF1R) than towards CRF2R, and to understand how the allosteric modulation induced by a sodium ion is propagated to the intracellular side of the d-opioid receptor; in the last paper, I proved the structural heterogeneity of the intrinsically disordered AICD peptide, and then employed the bias-exchange metadynamics and kinetic Monte Carlo techniques to understand the coupled folding and binding of AICD to its receptor Fe65-PTB2. I finally proposed that the interactions between AICD and Fe65-PTB2 take place through an induced-fit mechanism. In chapter 6, I made a short conclusion of the work, with an outlook of computational simulations of biomolecular systems. / <p>QC 20170516</p>

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