Computational Discovery of Novel Secondary Structures from Non-Canonical Amino Acids

Abstract

Protein secondary structures are a fundamental component of biological macromolecules, which are responsible for the myriad molecular processes of life. However, these biological protein macromolecules are not evolved to be amenable for rational molecular engineering due to the conformational polymorphism of alpha amino acid polymers. Therefore, developing new protein secondary structures that exhibit exceptional stability and consequently programmability could accelerate the efforts to engineer and, more importantly, utilize designed macromolecules. In this thesis, I describe a computational method to identify new secondary structures which are composed of non-canonical amino acids. Further I describe the subsequent experimental validation of these secondary structures to expand the known secondary structures for future molecular engineering efforts. Here I considered a pool of 135 non-canonical amino acid building blocks to create combinations of di-peptide repeat units, because this theoretical space has only been sparsely investigated previously. In total, the combinations of these building blocks yielded over 15,000 unique sequences which were computationally evaluated for their propensity to form a stable helical structure. Due to the lack of experimental data on the conformational properties of these residues, I developed an exhaustive and adaptive resolution computational search method to efficiently sample the enormous space of potential conformations. This method enabled the computational evaluation of the entire molecular conformational ensemble. Using this method, I identified 10 novel secondary structures which were expected to occupy a single low energy state. Experimental evidence suggests that these molecules are well-folded, engineerable helical polypeptides. Moreover, a select secondary structure was characterized as a polymer to explore the potential for these molecules as new classes of helical polymers for future materials applications.