Title: Sequence Effects in Polypeptoid Materials: A Multiscale Simulation Study from Atomistic to Field-Theoretic Models

 

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 polypeptoid structure and material properties.

 

In this work, we develop a multiscale modeling workflow to investigate sequence-structure-property relationships in polypeptoid systems. By integrating atomistic simulations, coarse-grained modeling, and field-theoretic approaches, we demonstrate how sequence-specific effects propagate across length scales to influence both local conformations and bulk thermodynamic properties. Atomistic simulations reveal how features such as hydrophobic patterning and backbone chirality influence local solvation and chain rigidity. These atomistic insights guide the development of a CG model using relative entropy optimization, enabling the study of longer chains and multi-chain assemblies. These CG representations are mapped onto field-theoretic models, allowing efficient screening of sequence-dependent solubility. This approach is encouraging for polymer platforms that lack large databases as it provides a physics-based framework that does not rely on extensive experimental input to navigate the vast sequence and chemistry space of sequence-defined polymers.