MARKOV STATE MODELS AND THEIR APPLICATIONS IN PROTEIN FOLDING SIMULATION, SMALL MOLECULE DESIGN, AND MEMBRANE PROTEIN MODELING

Abstract

This dissertation is focused on the application of Markov State Models on protein folding and designing of small drug-like molecules, as well as application of computational tools on the study of biological processes. The central focus of protein folding is to understand how proteins obtain their unique three-dimensional structure from their aminoacid sequences. The function of protein critically depends on its three- dimensional structure; hence, any internal (such as mutations) or external (such as high temperature) perturbation that obstructs three-dimensional structure of a protein will also interfere with its function. Many diseases are associated with inability of protein to form its unique structure. For example, sickle cell anemia is caused by a single mutation that changes glutamic acid to valine. Molecular dynamics (MD) simulations could be utilized to study protein folding and effects of perturbations on protein energy landscape; however, due to its inherent atomic resolution, MD simulations usually provide enormous amount of data even for small proteins. A thorough analysis and extraction of desired information from MD provided data could be extremely challenging and is well beyond human comprehension. Markov state models (MSMs) are proved to be apt for the analysis of large scale random processes and equilibrium conditions, hence it could be applied for protein folding studies. MSMs can be used to obtain long timescale information from short timescale simulations. In other words, the combination of many short simulations and MSMs is a powerful technique to study the folding mechanism of many proteins, even the ones with folding times over millisecond. This dissertation is centered on the use of MSMs and MD simulation in understanding protein folding and biological processes and is constructed as the following. The first chapter provides a brief introduction into MD simulation and the different techniques that could be used to facilitate simulations. Protein folding and its challenges are also discussed in chapter one. Finally, chapter one ends with describing MSMs and technical aspects of building them for protein folding studies. Chapter two is focused on using MD simulations and MSMs to design small protein like molecules to prevent biofilm propagation by disrupting its lifecycle. The biofilm lifecycle and strategy for its interruption is described first. Then, the designed molecules and their conformational sampling by MD simulations are explained. Next, the application of MSMs in obtaining and comparing equilibrium population of all designs are discussed. At the end of chapter two, the molecular descriptions of best designs are explained. Chapter three is focused on the effects of mutations on the energy landscape of a sixteen residue protein from c-terminal hairpin of protein G, GB1. Three mutations, tz4, tz5, and tz6 are discussed, and their folding rates and folding mechanisms are compared with wild-type GB1 using MSMs built from a significantly large MD simulation data set (aggregating over 9 millisecond). Finally, chapter four is focused on the application of MD simulations on understanding the selectivity of Na,K-ATPase, a biologically critical protein that transports sodium ions outside and potassium ions inside against their concentration gradient in almost all eukaryotic cells. Multiple MD approaches, including metadynamics and free energy perturbation methods are used to describe the origins of selectivity for Na,K-ATPase.