(545j) Developing a Bottom-up Coarse-Grained Model for Sequence-Specific Polypeptoids | AIChE

(545j) Developing a Bottom-up Coarse-Grained Model for Sequence-Specific Polypeptoids


Rivera Mirabal, D. - Presenter, University of Puerto Rico at Mayagüez
Jiao, S., University of California, Santa Barbara
DeStefano, A., University of Wyoming
Mengel, S., Drexel University
Segalman, R., UC Santa Barbara
Shell, M. S., UC Santa Barbara
Polymer synthesis has become increasingly sophisticated and current developments in precise sequence-controlled polymers allow for new design opportunities. However, the enormous number of possible sequences requires robust and efficient modeling to understand and predict how sequence impacts material properties. Here, we use sequence-specific polypeptoids (a biomimetic of polypeptides) 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. Our earlier 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. However, to study broader chain shape effects and self-assembly behaviors, access to longer and multiple peptoid systems requires coarse-grained simulations that can exceed the limitations of atomistic simulations.

Here, 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 our CG models with experimental end-to-end distance measurements measured from double electron-electron resonance (DEER) spectroscopy. We also leverage inverse design methods to suggest sequences with unique folding and self-assembly properties into various structures as a way to investigate fundamental limits of sequence design. These new computational methods provide molecular insight into the driving forces for polymer conformation and are exciting as new in silico screening tools to guide the development of sequence-specific polymeric materials with tunable properties.