A systematic methodology to develop bottom-up coarse-grained models for sequence-specific polypeptoids

Abstract

Current developments in the precise synthesis of sequence-controlled polymers allow for new opportunities in designing materials with finely tunable properties. In particular, polypeptoids offer a robust platform for sequence-specific polymers that can be produced at gram scale and offer a range of sidechain chemistries that far exceed those of polypeptides and natural protein-based biopolymers. However, the vast chemical design space of polypeptoids demands high-throughput screening, which is not yet synthetically feasible. Moreover, the lack of large structural and property databases limits the development of AI-based predictive models. These challenges highlight the need for systematic, physics-based computational methods to understand and predict how sequence impacts the polypeptoid structure and material properties. Here, we create a multiscale simulation workflow to develop bottom-up coarse-grained (CG) peptoid models using the relative entropy approach, to create a library of peptoid monomers suitable for studying the CG models of a wide range of sequences in both long-chain and multi-chain simulations. Using a representative subset of peptoid chemistries, we validate the resulting CG models by comparison with all-atom simulations and experimental end-to-end distance measurements measured through double electron-electron resonance spectroscopy. This approach is encouraging for polymer platforms that lack large databases as it offers a bottom-up framework to navigate the vast sequence and chemistry space of sequence-defined polymers, enabling molecular-level insight and in silico screening of peptoid-based materials.