Non-biological foldamers are a promising class of macromolecules
that share similarities to classical biopolymers such as proteins
and nucleic acids. Currently, designing novel foldamers is a non-trivial
process, often involving many iterations of trial synthesis and characterization
until folded structures are observed. In this work, we aim to tackle
these foldamer design challenges using computational modeling techniques.
We developed CG PyRosetta, an extension to the popular protein folding
python package, PyRosetta, which introduces coarse-grained (CG) residues
into PyRosetta, enabling the folding of toy CG foldamer models. Although
these models are simplified, they can help explore overarching physical
hypotheses about how oligomers can form. Through systematic variation
of CG parameters in these models, we can investigate various folding
hypotheses at the CG scale to inform the design process of new foldamer
chemistries. In this study, we demonstrate CG PyRosetta’s ability
to identify minimum energy structures with a diverse structural search
over a range of simple models, as well as two hypothesis-driven parameter
scans investigating the effects of side-chain size and internal backbone
angle on secondary structures. We are able to identify several types
of secondary structures from single- and double-helices to sheet-like
and knot-like structures. We show how side-chain size and backbone
bond angle both play an important role in the structure and energetics
of these toy models. Optimal side-chain sizes promote favorable packing
of side chains, while specific backbone bond angles influence the
specific helix type found in folded structures.