Design of self-organizing peptide chassis materials for synthetic cells by machine learning, molecular modeling, and cell-free protein synthesis
Abstract
DESIGN OF SELF-ORGANIZING PEPTIDE CHASSIS MATERIALS FOR SYNTHETIC CELLS BY MACHINE LEARNING, MOLECULAR MODELING, AND CELL-FREE PROTEIN SYNTHESIS Natural cells employ lipid bilayers as the boundary material, but synthetic cell-sized lipid vesicles are fragile and are susceptible to osmotic and mechanical stress. Peptidic encapsulants can survive in harsher environments than biological lipid membranes and have improved biocompatibility and biofunctionalization than synthetic polymerosome materials, making them ideal for biotechnology and biomanufacturing applications. It is the central hypothesis of this proposed work that amphiphilic peptides can serve as alternative membrane materials for synthetic cells, and that these novel encapsulants can open up tremendous opportunities for engineering peptidic vesicles with tailored structure and function. We will employ a convergent computational/experimental approach to efficiently screen the vast peptide sequence space for peptides capable of self-assembling stable compartments, experimentally assemble these molecules into synthetic peptide vesicles, and demonstrate the capability of these compartments to support on-demand biomanufacture of nanobodies withing harsh physical and chemical environments. We will accomplish this work in the following three specific objectives: Aim 1. Establish a high throughput assay for screening ELPs for self-assembly into peptidic compartments using CFE. We will first establish CFE of self-assembled peptidic compartments based on an initially proven ELP and a small set of derivative ELPs. Aim 2. Implement data-driven computational/experimental closed loop design of vesicle-forming peptides. We will perform high-throughput virtual screening of peptide vesicle formation using coarse-grained molecular simulation, and fuse experimental / computational screening within a multi-fidelity Bayesian optimization closed loop for data-driven discovery of vesicle-forming peptides. Aim 3. Demonstrate CFE-based biomanufacturing of nanobodies in peptidic vesicles. We will host CFE reactions within the peptidic vesicles to synthesize nanobodies within harsh chemical and physical environments that could not be tolerated by lipid-based vesicles or cells. Successful completion of the proposed work will establish a new class of chassis materials for synthetic cells capable of withstanding harsher environmental conditions and amenable to multiplexed functionalization for future biotechnology applications, demonstrate an efficient computational/experimental screening approach for the rapid discovery and engineering of non-natural peptides with emergent functionality, and demonstrate peptide vesicle-encapsulated cell-free expression as a robust platform for the synthesis and release of biologics within harsh environments. These outcomes are aligned with ARO programmatic goals to wield control of biomolecular assembly and spatial organization, support complex biomolecular function in artificial cells, and elucidate and exploit fundamental sequence-structure-property relations to enable rational design of biological/abiological assemblies with tailored function. This work will equip the Army with a powerful new synthetic biology capability for the on-demand biomanufacturing of nanobodies for prophylactic or therapeutic treatments within harsh operating environments unsuitable for conventional biological systems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 09, 2023
- Source ID
- W911NF2310084
Entities
People
- Allen P. Liu
Organizations
- Army Contracting Command
- United States Army
- University of Michigan