Peptoids, or n-substitued glycines, are complex and diverse oligomeric structures which have been explored for a number of biomimetic applications including drug delivery, surfactants, and catalysts. In contrast to their peptide counterparts, on peptoids the sidechain is bonded to the backbone nitrogen resulting in a flexible omega backbone dihedral that is able to isomerize into both stable cis- and trans- backbone conformations. This unique feature of peptoids allows for these structures to potentially span a significantly larger configurational space of chemical and structural functionality through the careful tuning of their side chains. This vast chemical and structural space has created significant challenges for rational design of new structures and functions as the underlying molecular scale driving forces that give rise to sequence/structure/function relationships have proven difficult to uncover.
This talk will highlight recent developments from our group in the use of statistical mechanical tools to accelerate molecular simulations of rare events like peptoid folding, aggregation and adsorption on inorganic surfaces. The first part of the talk focuses on studies of peptoid folding. Peptoids can freely explore a 12-dimensional helical configurational space with stabilization dictated largely by interactions between sidechains. I will discuss the thermodynamic basis of helix stabilization by chiral sidechains as well as fundamental aspects of the simulation science and the role of solvent in folding. Implications for rational design of higher order (tertiary structures) will be discussed. The remainder of the talk focuses on the use of peptoids in biomineralization. We discuss the rational design of peptoid mimics of the well-known R5/silaffin system, compare their nanoscale properties with peptides, and highlight experimental findings showing the efficacy of peptoids in biomineralization applications as well as similarities and differences between the peptoid and peptide systems. If available time remains, I will briefly discuss our group’s use of machine learning models to assist in high throughput screening towards inverse design of new sequences that precisely tune surface adsorption energies for biomolecules.