Exploring Non-Biological Foldamer Secondary Structure Using Tuneable Coarse-Grained Models

Non-biological foldamers are a promising new class of macromolecules which share similarities to classical biopolymers, such as proteins and nucleic acids. With designed monomers, foldamers enable researchers to incorporate novel chemistries into folding macromolecules. Designability of chemical moieties in foldamer monomers allows for the development of brand new highly specific macromolecules which can be designed with the final application in mind. Currently, designing novel foldamers is a non-trivial process, often involving many iterations of trial synthesis and characterization until a folded structure is observed. In this work, we aim to address these foldamer design challenges using computational modeling techniques. We develop CG PyRosetta, a coarse-grained folding software with tuneable foldamer models to prototype various foldamer monomers. Using CG PyRosetta, we can define a coarse-grain foldamer and search its configuration space to find its minimum energy structures. Through systematic variation of coarse-grained parameters we can investigate various folding hypotheses at the coarse-grained scale to help guide experimental design of foldamers. To demonstrate CG PyRosetta’s ability to identify minimum energy structures we propose two preliminary folding hypotheses to test. Firstly, we examine how sidechain size of a 1 backbone and 1 sidechain model affects foldamer secondary structure. We find for simple models, foldamers with larger sidechains tend to adopt tighter helical structures, often governed by packing effects of the sidechains. Secondly, we explore the effects of monomer geometry on foldamer secondary structure through variation of the 1 backbone and 1 sidechain model’s backbone angle. Using CG PyRosetta’s framework we plan to explore more complex folding hypotheses, such as effects of backbone rigidity, solvation effects and sequence heterogeneity on foldamer secondary structure.