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

Area of Science:

• Polymer Science
• Materials Science
• Computational Chemistry

Background:

• Sequence-controlled polymers offer tunable properties but face challenges in high-throughput screening.
• The vast chemical design space of polypeptoids is limited by a lack of large structural and property databases.
• Predictive models for polypeptoid behavior are needed to guide material design.

Purpose of the Study:

• To develop a systematic, physics-based computational method for predicting how sequence influences polypeptoid structure and material properties.
• To create a multiscale simulation workflow for bottom-up coarse-grained (CG) peptoid modeling.
• To enable in silico screening of sequence-defined polymers.

Main Methods:

• Developed a multiscale simulation workflow using the relative entropy approach for bottom-up coarse-grained (CG) peptoid model development.
• Created a library of peptoid monomers for simulating a wide range of sequences in long-chain and multi-chain systems.
• Validated CG models against all-atom simulations and experimental measurements (double electron-electron resonance spectroscopy).

Main Results:

• Successfully created validated bottom-up coarse-grained peptoid models.
• Demonstrated the workflow's ability to navigate the vast sequence and chemistry space of sequence-defined polymers.
• Provided molecular-level insights into sequence-structure-property relationships.

Conclusions:

• The developed physics-based simulation approach offers a framework for understanding and predicting the behavior of sequence-defined polymers.
• This method facilitates in silico screening, addressing limitations in experimental high-throughput synthesis and data availability.
• Enables efficient exploration of the chemical design space for novel peptoid-based materials.