Dextran has demonstrated great potential in biomedical applications due to its chemical characteristics as well as its capacity to conjugate with other polymers. Targeted drug delivery systems have been made using star-like copolymer architectures of dextran and poly(N-isopropylacrylamide)(PNIPAM) - a thermosensitive polymer with a lower critical solution temperature (LCST) of 305 K. However, a molecular-level understanding of dextran-polymer conjugates and architectures, is still missing, given the lack of polymer compatible carbohydrate models used for computational modeling and simulations. Bottlebrush polymers (BBPs) are one such architecture, which can be molded into a variety of shapes by modifying structural factors including the grafting density, the length and arrangement of the side chains as well as functionalization of side chains. Furthermore, it has been demonstrated via temperature-sensitive BBPs (TS-BBPs) of PNIPAM, that the shapes of these architectures considerably impact their overall conformations, giving rise to a variety of features and properties. In the current study, we conduct coarse-grained (CG) molecular dynamics (MD) simulations of BBPs of dextran-PNIPAM copolymers, having one of three shapes: cake-like, dumbbell-like, or plus-like. We investigate the influence of BBP shape and temperature (below and above LCST) on the conformations of overall BBPs, their backbones and side chains, as well as the surrounding solvent structure in comparison to non-dextran conjugated BBPs. By grafting dextran 10-mers and 30-mers onto the TS-BBPs in various configurations, we also examine the impact of dextran chain length and conjugation density on these designs. We demonstrate that the lengths of conjugated dextran chains around the backbone and their immediate environments influence the compactness of overall structures while also affecting PNIPAM conformations in the core of these architectures through analyses such as free volume, contact maps, asphericity, etc. Finally, we quantify the similarities and differences between conjugated and non-conjugated TS-BBPs using a recently developed convolutional neural network (CNN)-based analytic procedure that captures molecular-level properties that may be missed using traditional analysis. In order to further inform the development of new BBPs for dextran-PNIPAM conjugates, we analyze the link between the degree of dextran conjugation and its impact on the PNIPAM side chains in the TS-BBP.

Deep learning enabled investigation of dextran-conjugated thermosensitive bottlebrush polymer conformations through coarse grained molecular dynamics simulations

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