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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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