Summary
Recent advances in materials synthesis, characterization, and simulation have allowed the creation of responsive, soft materials with controllable physicochemical properties. Materials that self-assemble into desired morphologies such as nanoparticles, fibrils, and supramolecular networks are of particular interest, where their ability to self-assemble can be tuned by applying external stimuli such as heat, light, pH, and salt for a range of applications including in drug delivery and tissue engineering. While experimental synthesis and characterization of soft materials are often time consuming and limited in terms of resolution, simulations allow for efficient screening of broad design spaces, while also giving insights into the molecular mechanisms and polymer physics governing the complex phase behavior and assembly of responsive materials. Therefore, there is a need to develop molecular models that can capture the structure, dynamics, and self-assembly of polymers over large enough length scales and time scales to provide experimentally relevant insights. The Taylor lab addresses this gap by combining computational tools such as molecular simulations and machine learning to study the structure and dynamics of polymers and biomacromolecules as a means for molecular engineering of multifunctional, soft materials.
Dynamic Covalent and Double-Network Hydrogels
Physical hydrogels are formed via non-covalent crosslinks: e.g., hydrogen bonding, ionic interactions, π-π stacking, but have weak mechanical properties. Traditional covalently crosslinked hydrogels, conversely, offer higher mechanical strength but are difficult to be remolded once formed. An emerging solution to the tradeoff between physical and covalent polymer networks is Dynamic Covalent Networks. Such systems contain reversible covalent bonds which allow for good mechanical strength, injectability, and self-healing properties.
While the majority of simulation studies have examined solvent-free dynamic covalent networks, there has been significant interest in designing hydrogels with dynamic covalent crosslinks for tissue engineering and other biomedical applications. In the Taylor lab, we use reactive atomistic and coarse-grained models to ask fundamental polymer physics questions involving the effect of polymer design (e.g., chain length, entanglements, bond strength) on the nonlinear rheology and self-healing properties of dynamic covalent hydrogels.
We are also interested in single- and double-network hydrogels. The latter contains multiple interpenetrating networks such that they show enhanced toughness and are more realistic models of the Extracellular Matrix (ECM). It is well-known that the ECM comprises a complex mixture of physically and covalently crosslinked biopolymers (e.g., Glycosaminoglycans and proteins such as collagen). In the Taylor lab, we also use atomistic and CG models to study the structure and rheology of hybrid physical-covalent biopolymer hydrogels and examine nanoparticle transport through polymer networks.
Peptide-based Polyelectrolyte Complexation and Transport
Membrane-less organelles comprising polyelectrolyte complexes (PECs) are naturally present in vivo. Inspired by nature, researchers have utilized PECs for biomedical applications such as carriers for drug delivery. Therefore, a fundamental understanding of the link between PEC macromolecular structure and self-assembled (carrier) structure is paramount to design novel soft materials.
Many studies have examined PEC phase separation at equilibrium, but less is known about their nonlinear rheology and assembly under flow (i.e., when nanocarriers are injected in vivo). Specifically, in the Taylor lab, we ask fundamental polymer physics questions pertaining to the coacervation of charged polypeptides, such as elastin-like polypeptides, and α-helix and β-sheet forming peptides. Such systems contain biomacromolecules with complex chain structures (e.g., secondary and tertiary structures) versus conventional non-biological polymers.
How does the presence of folded domains impact the mechanism of coacervation, both at equilibrium and under flow? How do chirality (i.e. L- vs. D- amino acids) and peptide sequence impact the stability of complexes under different types of flow fields (e.g., shear versus extensional flow)?
Machine-learned Coarse-Grained (CG) Models of Polymers
The ability of polymers to dynamically adjust their structure in response to changes in environment such as temperature, pH, and solution conditions, is essential to their multifunctional properties in medicine, both in the laboratory, in vitro, and in the body, in vivo. Therefore, a crucial step in simulating soft materials is the choice of an appropriate molecular model to capture desired structural and/or dynamic properties. Coarse-grained models for biomacromolecules such as peptides frequently retain a lot of chemical details, which still limit them to small length (nm) and time (ns-μs) scales and therefore cannot efficiently probe multiscale assembly problems. Simple coarse-grained polymer models, frequently with isotropic attractions, capture polymer dynamics, entanglements, scaling laws, and moduli but miss chemical details, structure, and directional interactions that are essential to multiscale problems involving biomacromolecules. Other coarse-grained models which capture directional interactions target structure and thermodynamics, but frequently neglect dynamics and rheological properties which are essential to designing biomimetic materials with viscoelastic properties. The Taylor lab develops coarse-grained models using deep learning methods which bridge the gap between simple, generic polymer models and chemically accurate CG models to capture biopolymer/macromolecular structure, dynamics, and rheology.
