Simulation Framework for Capturing Simultaneous Folding and Complexation in Autonomous Ribogenesis

Autonomous self-assembly of ribosomes (ribogenesis) remains a major outstanding challenge in synthetic biology. Understanding how physical conditions in vivo affect ribogenesis is key to replicating the process in synthetic cells. With this goal in mind, we are developing an AI-enhanced mesoscale simulation pipeline for the ultra-fast prediction of self-assembly of bacterial ribosomes under different physical conditions. In this work, we investigate the binding of the primary ribosomal protein S4 with the 5-way junction (5WJ) of the 16S rRNA of E. coli’s smaller 30S subunit. This choice is motivated by the fact that S4 is one of the two ribosomal proteins that nucleate the assembly of the 30S subunit. The interactions between S4 and 5WJ are modeled at near-atomistic resolution using molecular dynamics (MD) simulations. Our simulations serve as a generalizable framework for capturing the simultaneous folding and complexation of ribosomal proteins and rRNA segments during ribosomal self-assembly. The simulation data will be used in our ongoing efforts to train an AI model for on-the-fly prediction of interaction forces between the various ribosomal components, and its subsequent integration with our physics-based whole-cell model of bacterial cells.