Dissertation Defense: Diego Caballero Orduna, Yale University, “Computational Studies of Protein Structure”

Despite the abundance of crystallographic and structural data and many recent advances in computational methods for protein design, we still lack a quantitative and predictive understanding of the driving forces that control protein folding and stability. This inadequacy of current computational approaches to the analysis and design of protein structures is arguably one of the biggest open challenges in biophysics and biochemistry. My work has built on the pioneering work of Ponder and Richards in the 1980’s, who while at Yale demonstrated that steric and packing constraints in protein interiors were the most stringent criteria in determining protein conformations. I will present my work developing and using a hard-sphere plus stereo-chemical constraint molecular dynamics force field to study amino acid conformations. I use this force field to model hydrophobic amino acids and study the fundamental driving forces that determine amino acid side-chain conformations. I have complemented this approach with other numerical and computational techniques such as approximate Markov models to predict amino acid conformations in different environments. This dissertation presents three separate but related computational studies. In the first one, I present an analysis of the equilibrium backbone conformations that the amino acid Alanine can take as well as inter-conversion mechanisms between them. I then study side-chain dihedral angle equilibrium conformation states in the amino acids Leucine and Isoleucine. I predict and show a novel transition method between them. Finally, I employ my model to comprehensively study and predict the side-chain dihedral angle distributions of eight different hydrophobic residues in the context of high resolution protein crystal structures. Future progress built on this work will enable the reliable design of specific protein-ligand motifs, the development of efficient computational methods to rationally re-design protein cores and interfaces, and numerous other applications in biomedicine.