(604h) Predicting Sequence-Dependent Solubility in Polypeptoids: A Multiscale Approach

Polymer synthesis has become increasingly sophisticated, enabling the creation of precise sequence-controlled polymers with tailored properties. However, the vast number of potential sequences demands robust and efficient modeling to predict how sequence impacts macromolecular interactions. Polypeptoids (a biomimetic of polypeptides) serve as an ideal platform for establishing design rules as they are routinely synthesized at the gram scale with hundreds of different side chain functionalities. However, polypeptoid simulations encounter major sampling challenges due to their long-time scales associated with conformational transitions, limiting studies of longer and multiple peptoid chain systems. To address this challenge, we present a multiscale modeling framework that enables high-throughput screening of sequence-dependent solubility. We use atomistic molecular dynamics simulations to parameterize coarse-grained, sequence-specific field-theoretic models. By leveraging this approach, we efficiently evaluate the impact of monomer composition and patterning on solubility across large sequence spaces—enabling access to mesoscopic behavior and sequence-level insights that were previously inaccessible with traditional peptoid modeling. Our results reveal that sequence plays a critical role in determining solubility—capturing effects missed by traditional models that consider only average composition. These findings expand our understanding of sequence-dependent behavior in polymers across longer length scales and offer new strategies for using sequence to design materials with tailored solubility and performance.