Computational studies of protein-ligand recognition

Molecular recognition between biomolecules and ligands is very specific in living cells. The functions of all biochemical processes and cell mechanisms are dependent upon complex but specific non-covalent intermolecular interactions. As essential building blocks in protein and nucleic acid, phosphate groups are commonly found in nucleic acids, proteins, and lipids. Nearly half of known proteins have been shown to interact with ligands containing a phosphate group. Binding of a phosphoryl group is fundamental to a range of biological processes including metabolism, biosynthesis, gene regulation, signal transduction, muscle contraction, and antibiotic resistance. Phosphorylation is one of the most common forms of reversible posttranslational modification of protein and, nearly 30% of all proteins are phosphorylated on at least one residue in cells. However, phosphate binding sites are less well defined and fundamental principles of why and how proteins recognize phosphate groups are not yet fully understood. Molecular modeling is a common tool for studying biomolecular structure, dynamics, interaction and function. Due to the complex electrostatics, high concentration of ions and intricate interactions with environment, however, the modeling and designing of highly charged drug-like molecules and nucleic acid derivatives are extremely difficult. This thesis will focus on the highly charged phosphate, including its different protonation states, and energetic and thermodynamic driving forces behind protein-phosphate recognition. This thesis work will also discuss the development of more sophisticated computational models, AMOEBA+, that are necessary for a better understanding and prediction of the physical properties of small organic molecules. Four projects will be discussed in this dissertation: two projects on force field development, and two on applying molecular dynamic simulations to understand biological processes. These projects have led to new insights into understanding of physical and chemical principles and mechanisms underlying highly protein-phosphate binding and nucleic acid stability. In addition, this thesis work will enhance the capability to develop and apply computational and theoretical frameworks to model, predict and design proteins, therapeutics, and diagnostic strategies targeting phosphates, phosphate-containing biomolecules