Tuning the entropic spring: A predictive modeling approach to dictating order in polymer conjugated peptide assemblies

Living cells possess extraordinary capabilities in maintaining chemical balance in extreme environments, which are facilitated by exceptional chemical and biological sensing and selective transport capabilities that far surpass what can currently be achieved with engineered systems. Advances in polymer chemistry and physics, protein engineering, and synthetic biology, have created new pathways to transfer biological capabilities to man-made systems, specifically by enabling the possibility to recreate nature’s complexity by generating polymeric materials that incorporate nature’s building blocks (i.e., proteins) in their native functional state. By combining the hierarchical structure and chemical functionality of proteins with the stability and processability of polymers, peptide-polymer conjugates may potentially lead to materials of both high level of ordering and complexity simultaneously. However, the materials science of peptide-polymer conjugates is still at infancy because a predictive model that links molecular design parameters to hierarchical organization and functionality remains elusive for these hybrid systems. The most distinguishing aspect of this work is based on our original hypothesis that mechanical forces arising from the entropic penalty of conjugated chains can be used as a means to generate theoretically predictable ordered structures consisting of functional peptides, specifically helix bundles. Recognizing this capability as a new frontier in bioinspired material design, control of the spatial organization of functional peptides conjugated with polymers is the foundational concept upon which bioinspired synthetic strategies will be built. In pursuit of this capability, this research aims to establish a rational, predictive modeling approach to the design of entropic forces in alpha-helix/polyethylene glycol (helix-PEG) conjugates to achieve hierarchical self-assembling systems with predictable order. The two aims that will be pursued in light of this overarching objective are:  Aim 1 - Investigate conformational dynamics of helix-PEG conjugates  Aim 2- Establish a predictive model to dictate ordering of helix-PEG bundles Through a combined molecular theory/simulation approach validated by experimental characterizations, this work will contribute significantly to the current understanding of how entropic spring forces, generated by polymer conjugation, control conformational dynamics and hierarchical stability of helix-PEG bundles. The proposed efforts are foundational in the context of the long-term career objective of the PI, which is to enable progress towards rapid discovery and design of bioinspired suprabiomolecular materials by establishing a knowledge base of their physical behavior relevant to applications beyond the biological milieu. Blending stability analysis concepts from structural mechanics with directed self-assembly principles from polymer physics, we will aim to generate predictable functionalities that are inspired from nature. New understanding gained from these efforts will delineate a science-based approach to discovering new forms of biomolecular materials with tailored functionalities that will potentially overcome bottlenecks in future technologies of relevance to the Navy. Fundamental insight gained through these studies will accelerate the discovery of functional polymer-peptide conjugate materials that could potentially mimic biological capabilities of biosensing, molecular recognition, environmental remediation, selective transport, mech