PNNL

Abstract

Collagen-mimetic peptides (CMPs) are engineered molecules designed to replicate the triple-helical structure of natural collagen. A repeating x-y-Gly sequence is the dening motif of CMPs and is critical to its triple-helical structure and stability. Substitutions to the residues occupying the x and y positions present a means to modulate the CMP structure and properties. Peptoid residues { N-substituted glycine derivatives { present an attractive potential substitution due to their thermal stability, proteolytic resistance, biocompatibility, and diverse palette of non-natural side chains, but also tend to introduce a high degree of backbone exibility that can diminish the stability of the triple helix. In this work, we report a computational active learning cycle comprising molecular dynamics simulation, Gaussian process regression, and Bayesian optimization to computationally identify a number of promising CMPs stabilized by peptoid side chain chemistry predicted to assemble into highly stable collagen-like triple helices. The top candi- date identied by the computational screen is experimentally synthesized and imaged using scanning electron microscopy to resolve bril-like bundles consistent with collagen-like triple helices. This work identies a number of polypeptoid sequences capable of forming stable, triple-helical CMP-like structures, opening new avenues for the design of biomimetic materials and advancing our understanding of structure-function relationships in synthetic peptoid-based polymers.