Non-technical Abstract: The cell is the fundamental building block of all living things. The materials within the cell are separated and protected from the environment by the cell membrane that is composed of molecules derived from fatty acids. These molecules function well under relatively benign natural conditions, such as in water at room temperature and pressure. Under harsh environments, such as extreme temperatures and pressures prevalent in industrial processes, these molecules are unstable therefore making it difficult to deploy cells within such environments. Furthermore, it is difficult to engineer natural membrane molecules with new functions such as the ability to sense their environment or bind particular surfaces or target molecules. It is the primary goal of this work to discover and synthesize a new class of molecules based on proteins as an alternative 'chassis' material for synthetic cell membranes that have improved mechanical and chemical stability and can be engineered to endow the membrane with new functions. Profs. Ferguson and Liu combine fast experimental synthesis and testing with computer simulations and artificial intelligence tools to search for new synthetic membranes with the ability to survive in harsh environments and the capacity to assemble synthetic cells together into synthetic tissues. By combining experiment and computation within a virtuous cycle, wherein computation guides experiment and experiment informs computational modeling, massive savings in labor, time, and resources are realized compared to traditional trial-and-improvement experimentation. In the course of this work, Profs. Ferguson and Liu provide research opportunities for post-doctoral, graduate, undergraduate, and high-school trainees, incorporate the outcomes of the research into classes that they teach, and engage in outreach activities through a Girls in Science and Engineering summer camp, Detroit Area Pre-College Engineering Program, and University of Chicago After School Matters summer internship program.

Technical Abstract

The aim of this work is to discover novel peptidic biomaterials as an alternative "chassis" material for synthetic cells. While biology has settled on using lipid bilayer membrane as the material for compartmentalizing cytoplasm and for membrane-bound organelles, polypeptides offer an alternative biomaterial that can establish peptidic microcompartments with improved mechanical and chemical stability and the capacity for additional engineered biological function. Peptidic chassis materials offer unique advantages compared to lipid and polymersome membrane materials in terms of biocompatibility, chemical and mechanical stability, and capacity for additional functionalization that make them extremely desirable for applications in biomedicine, drug delivery, biosensing, and deployment in non-natural environments. The discovery of peptide sequences capable of spontaneous self-assembly into solute-filled microcompartments with desired materials properties is frustrated by the vast size of the protein sequence search space that makes exhaustive exploration intractable and Edisonian trial-and-improvement inefficient. In this work, we establish an integrated data-driven modeling and high-throughput cell-free synthesis platform to rapidly traverse sequence space. In a tightly integrated feedback loop between theory and modeling, we employ an 'active learning' paradigm to extract information from experimental data, guide rational traversal of the vast peptide sequence space, and optimally deploy experimental resources. Completion of this work will lead to the discovery of highly sought-after peptide-based synthetic cell 'chassis materials' that are more robust to harsh environments and which have complementary binding functionality to enable self-organization of the synthetic cells into synthetic tissues. In the course of this work, Profs. Ferguson and Liu offer research opportunities for high-school and undergraduate students to provide exposure to scientific research and improve representation in the STEM pipeline, train graduate and post-doctoral trainees in integrated computational and experimental research, incorporate the scientific outcomes in course materials, and engage in outreach activities through a Girls in Science and Engineering summer camp, Detroit Area Pre-College Engineering Program, and University of Chicago After School Matters summer internship program.

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.

Agency
National Science Foundation (NSF)
Institute
Division of Materials Research (DMR)
Type
Standard Grant (Standard)
Application #
1939534
Program Officer
Germano Iannacchione
Project Start
Project End
Budget Start
2019-08-15
Budget End
2021-07-31
Support Year
Fiscal Year
2019
Total Cost
$150,000
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109