Evolution of Synthetic Nucleocapsids Encapsulating their own RNA genome

Abstract

Viruses are the simplest example of a fundamental feature of biology— they maintain genotype phenotype linkage in complex biochemical environments by encapsulating and protecting a nucleic acid genome. This allows evolution to improve the functional properties required to complete their life cycle and deliver their genome into the host cells matching their tropism. While these naturally occurring systems have been modified to change their tropism1 and to display proteins or peptides2-4, billions of years of evolution have favored efficiency at the expense of modularity, making viral capsids difficult to engineer. Synthetic systems composed of non-viral proteins could provide a blank slate to evolve desired properties for drug delivery and other biomedical applications, while avoiding the safety risks and engineering challenges associated with viruses. Here we create synthetic nucleocapsids—computationally designed icosahedral protein assemblies5,6 with positively charged inner surfaces capable of packaging their own full-length mRNA genomes—and explore their ability to evolve virus-like properties by generating diversified populations using Escherichia coli as an expression host. Several generations of evolution resulted in drastically improved genome packaging (>133-fold), stability in whole murine blood (from less than 3.7% to 71% of packaged RNA protected after 6 hours of treatment), and in vivo circulation time (from less than 5 minutes to 4.5 hours). The resulting synthetic nucleocapsids package one full-length RNA genome for every 11 icosahedral assemblies, similar to the best recombinant adeno-associated virus (AAV) vectors7,8. Our results show that there are simple evolutionary paths through which protein assemblies can acquire virus-like genome packaging and protection. Considerable effort has been directed at “top-down” modification of viruses to be safe and effective for drug delivery and vaccine applications1,9,10; the ability to computationally design synthetic nanomaterials and to optimize them through evolution now enables a complementary “bottom-up” approach with considerable advantages in programmability and control.