NSF AI Disclosure Required
NSF requires disclosure of AI tool usage in proposal preparation. Ensure you disclose the use of FindGrants' AI drafting in your application.
Fluidic Protein Vesicles for High-Density Signal Presentation in Immunomodulatory Biomaterials
NSF
About This Grant
PART 1: NON-TECHNICAL SUMMARY Living cells constantly communicate and respond to their surroundings using complex molecular interactions on their surfaces. Many life-saving immunotherapies, such as engineered T cells used to fight cancer, rely on these surface interactions to activate the immune system. However, building artificial systems that can reliably mimic these natural signals has been very difficult. Current synthetic materials cannot easily control how proteins cluster, move, and organize on a surface, even though these features are essential for proper immune activation. This project supports fundamental research that creates new ways to design protein-based building blocks that self-assemble into soft, cell membrane-like vesicles. These vesicles behave like simplified versions of natural cell membranes, allowing researchers to program how surface proteins move and interact. By learning how to control the spacing, mobility, and density of these proteins, the research lays a foundation for next-generation artificial antigen-presenting cells that may one day improve cancer immunotherapy and other biomedical technologies. The broader impacts of this work include developing hands-on research experiences for undergraduate and high-school students. The project will integrate laboratory modules into courses, provide mentoring opportunities, and create outreach activities that introduce young learners to biomolecular engineering and synthetic biology. Together, these efforts support the national interest by advancing scientific understanding, improving human health, and strengthening the future STEM workforce. PART 2: TECHNICAL SUMMARY This project seeks to establish fundamental design principles governing the self-assembly, membrane organization, and immunological function of recombinant protein vesicles that mimic essential features of natural antigen-presenting cells (APCs). The research uses modular fusion proteins composed of three domains: a folded globular protein for functional display, a heterodimerizing ZE/ZR coiled-coil pair that specifies molecular stoichiometry, and an elastin-like polypeptide (ELP) segment that provides amphiphilicity, membrane formation, and tunable mechanical properties. These components self-assemble into immunomodulatory protein vesicles (iPVs) whose deformability, lateral mobility, and protein valency can be precisely modulated through sequence design and controlled mixing ratios. The central objective is to determine how protein architecture, stoichiometric loading, membrane mechanics, and ligand mobility collectively regulate mesoscale spatial patterning and receptor engagement at synthetic cell-mimetic interfaces. Quantitative biophysical methods will define how these parameters influence membrane fluidity, protein clustering, and dynamic reorganization during contact with T cells. A major focus is understanding how iPVs present peptide–MHC complexes, costimulatory antibodies, and cytokines to cytotoxic CD8+ T cells. The platform enables systematic interrogation of early signaling events such as receptor activation thresholds, microcluster formation, immunological synapse stabilization, and downstream proliferative responses. Overall, this research will define foundational principles for engineering synthetic biomolecular membranes capable of coordinating immune recognition. The resulting framework will guide the construction of next-generation artificial antigen-presenting cells built entirely from recombinant proteins, enabling scalable, tunable, and safe immunomodulatory materials. The principles discovered will also support broader efforts in synthetic cell design, biomaterials development, and bottom-up synthetic biology. 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.
Focus Areas
Eligibility
How to Apply
Up to $507K
2028-12-31
One-time $749 fee · Includes AI drafting + templates + PDF export
AI Requirement Analysis
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.