Predictive Modeling of Sequence-Programmable IDP Brushes for Soft Interfaces

Many modern technologies rely on surface coatings to control how the surface interacts with liquids, biological molecules, and other surfaces. One important type of coating is a polymer brush. A polymer brush is a layer of polymer molecules attached to a surface. Polymer brushes are used in many everyday technologies. Examples include medical devices, anti-corrosion coatings, sensors, and water-treatment systems. This project will study a new class of polymer brush made from intrinsically disordered proteins (IDPs). Like all proteins, IDPs are made from amino acids. The molecular structure of IDPs and, therefore, their physical properties, are determined by their amino acid sequences. This allows the possibility choosing amino acid sequences to produce IDPs with desired properties, which cannot be done with conventional polymers. The project will use computer simulations and artificial intelligence to discover the rules that relate the behavior of the brush to the molecular structure of the IDP. The results will guide the design of surface treatment technologies for a variety of applications. The project will provide interdisciplinary training for undergraduate and graduate students and increase public awareness of soft materials through education and outreach. Brushes composed of neutral or charged homopolymers or copolymers are well-studied. Sequence-defined IDPs contain twenty distinct amino acids whose charge, hydropathy, and interaction patterns generate a vastly expanded design space for interfacial materials. The project will establish quantitative relationships between amino acid sequence and the structure and dynamics of the brush. This will be accomplished by combining large-scale coarse-grained molecular simulations with physics-guided machine learning. All three regimes will be examined: isolated chains, dilute brushes corresponding to low surface graft density, and dense brushes corresponding to high surface graft density. The analysis will identify a compact set of physically interpretable sequence descriptors that govern transitions between distinct interfacial brush states. It will reveal how sequence heterogeneity modifies classical polymer-brush scaling, and establish generalized design principles for sequence-programmable polymer brushes. The resulting predictive framework will extend polymer brush theory to biomolecular interfaces. It will generate experimentally testable predictions for the design of next-generation biomaterials, biosensing platforms, and responsive soft interfaces. The simulation methods, analysis tools, and machine-learning workflows developed in this project will be released as open-source resources to facilitate their adoption by the broader scientific community. The results will provide a foundation for the development of advanced biomaterials and interfacial technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.