Synthetic membrane signaling systems built from de novo protein components

University Of California-San Francisco, San Francisco CA

Investigators

Abstract

Nature has evolved complex signaling pathways that allow cells to sense inputs from their environment and respond through various outputs like changes in cell behavior and state. Reprogramming these signaling pathways and building new synthetic signaling systems have many significant applications in biotechnology; for example, cells could be engineered as sensitive diagnostics, or signaling pathways could be reprogrammed to address challenges in sustainability and climate change. However, broadly repurposing natural protein signaling components for new applications has proven to be difficult. This research team will overcome these challenges by building synthetic signaling systems from the ground up using de novo proteins (proteins designed computationally and not found in nature). These de novo proteins will act as stable, tunable, controllable, and modular building blocks that can be assembled to form synthetic signaling systems capable of generating diverse cellular behaviors. In addition, due to this project’s bottom-up approach, this work will shed insights into fundamental processes of life, which have remained elusive due to the complexity of natural signaling systems. Finally, this project will provide synthetic biology training at the intersection of artificial-intelligence (AI)-based protein design, engineering, and cellular biology; will broaden participation of groups underrepresented in STEM through partnerships with programs at the graduate, postbac, undergraduate, and high school levels; and will consider ethical and biosafety/security aspects of AI-based de novo protein engineering. Engineering synthetic signaling systems that can precisely control biological processes have many significant applications, both to quantify the functional requirements of the processes of life, and to engineer synthetically modified cells for biotechnology. The overall goals of this project are to design, build, and test synthetic signaling systems built from protein components that are designed entirely de novo (not derived from natural proteins) using computational methods including deep learning. These de novo protein parts will be assembled into membrane localized signaling systems in mammalian cells and will be capable of (i) mediating synthetic receptor clustering behaviors with programmable properties, (ii) tunable signal amplification, and (iii) being controlled with modular user-defined signals that allow bi-directional transmembrane signaling. The synthetic signaling systems built in this project will lead to more predictive cellular engineering applications in biotechnology, allowing for the engineering of cells as sentinels or building synthetic cell-cell communication. In addition, the generated signaling systems should be useful to learn and test principles of signaling beyond those that nature has sampled. 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.