Predicting and Simulating the Self-Assembly of Sequence-Specific Peptoids

Polymer synthesis has grown increasingly sophisticated, allowing for precise sequence-controlled polymers with tailored properties and routes to new materials with highly tuned structure and functionality. However, the enormous number of possible sequences requires robust and efficient modeling to understand and predict how sequence impacts macromolecular self-assembly. Here, we use sequence-specific polypeptoids (a biomimetic of polypeptides) as a platform for developing design rules for relating chemical sequence to polymer conformation. Our earlier atomistic simulation studies of small polypeptoid systems with advanced sampling molecular dynamics methods examined changes in the local and global structure of short chains in response to the number and location of the hydrophobic and chiral monomers, with excellent agreement with experiments. In this work, we developed a bottom-up coarse-grained peptoid model, which allows access to longer and multiple peptoid systems. With this simulation workflow we study the effect of sequence on broader chain shape effects and self-assembly behavior. Moreover, we leverage inverse design methods based on genetic optimization to suggest sequences with unique folding and self-assembly properties into varied structures and phases. These new computational methods provide a molecular-level understanding of the factors governing polymer conformation and offer new in silico screening tools to guide the development of sequence-specific polymeric materials with tunable properties.