Bottom-Up Coarse-Grained Modeling of Sequence-Specific Polymers

Molecular modeling offers direct insight into conformational landscapes, enhancing our understanding of sequence-structure relationships. In this work, we use sequence-specific peptoids as a platform for developing design rules for relating chemical sequence to polymer conformation. Polypeptoids are particularly useful in this context due to their lack of backbone hydrogen bonding, isolating the effect of sidechain chemical sequence on polymer chain shape. Moreover, they are routinely synthesized at gram scale, sequence-specifically, with hundreds of different side chain functionalities, allowing for detailed experimental investigation and validation. However, polypeptoid simulations encounter major sampling challenges due to the long-time scales associated with conformational transitions, which has greatly limited fundamental studies on broader peptoid chain shape effects and self-assembly behaviors. Recently, our studies of small polypeptoid systems with all-atom advanced sampling molecular dynamics revealed the local and global structure of short chains in response to their sequence patterning to be in excellent agreement with experiments. In this work, we develop a bottom-up coarse-grained peptoid model using the relative entropy approach to create a library of peptoid monomers suitable for studying CG models of a wide range of sequences in both long chain and self-assembly simulations. We validate the CG models with experimental end-to-end distance measurements measured from double electron-electron resonance (DEER) spectroscopy. Importantly, this CG approach allows for higher throughput simulations of peptoid chains and enables long and multi-chain studies not accessible with atomistic models. Moreover, this CG workflow enables the development of models that can be readily transformed into field-theoretic representations, facilitating exploration of larger length scales and phase behavior.