Abstract
Single-chain polymer nanoparticles (SCNPs) combine the chemical diversity of synthetic polymers with the intricate structure of biopolymers, generating versatile biomimetic materials. The mobility of polymer chain segments at length scales similar to secondary structural elements in proteins are critical to SCNP structure and thus function. However, the influence of non-covalent interactions used to form SCNPs (e.g., hydrogen-bonding and biomimetic secondary-like structure) on these conformational dynamics is challenging to quantitatively assess. To isolate the effects of non-covalent interactions on SCNP structure and conformational dynamics, we synthesized a series of amphiphilic copolymers containing dimethylacrylamide and monomers capable of forming these different interactions: 1) di(phenylalanine) acrylamide that forms intramolecular β-sheet-like crosslinks, 2) phenylalanine acrylamide that forms hydrogen-bonds, but lacks a defined local structure, and 3) benzyl acrylamide that has lowest propensity for hydrogen-bonding. Each SCNP formed folded structures comparable to those of intrinsically disordered proteins, as observed by size exclusion chromatography and Small Angle Neutron Scattering. The dynamics of these polymers, as characterized by a combination of dynamic light scattering and Neutron Spin Echo spectroscopy, was well described using the Zimm with internal friction (ZIF) model, high-lighting the role of each non-covalent interaction to additively restrict the internal relaxations of SCNPs. These results demonstrate the utility of local scale interactions to control SCNP polymer dynamics, guiding the design of functional biomimetic materials with refined binding sites and tunable kinetics.
Supplementary materials
Title
Dynamic Implications of Non-Covalent Interactions in Amphiphilic Sin-gle-Chain Polymer Nanoparticles - Supplemental Information
Description
Supporting information containing synthetic procedures, experimental details, supplemental tables, and figures including DLS, CD, SEC, SANS, and NSE characterization is available.
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