From Atoms to Cells: AI-Based Structure Prediction Fueling Molecular Dynamics Simulations in Computational Structural...

The simulation of biological systems has undergone a revolutionary transformation, progressing from modeling single proteins to entire cellular environments. This leap forward is driven by the convergence of molecular dynamics (MD) simulations and artificial intelligence (AI)-powered structure prediction. Traditionally, MD simulations provided atomic-level insights into protein function and interactions, yet their accuracy relied on experimentally determined structures. AI-based models, such as AlphaFold, now enable the rapid and accurate prediction of protein structures, expanding the scope of simulations beyond isolated biomolecules to complex assemblies. However, a structure alone is not sufficient to capture biological function. Molecular motion underlies almost all cellular processes, from enzyme catalysis to signal transduction. MD simulations breathe life into static models, revealing dynamic conformational changes and mechanistic pathways. With computational power and AI capabilities, we are now approaching the long-sought goal of simulating entire cellular processes with unprecedented resolution. This chapter explores how AI and MD are bridging the gap between static snapshots and dynamic cellular models, paving the way for whole-cell simulations. The ability to computationally reconstruct cellular behavior at the molecular scale is poised to transform biological research, drug discovery, and synthetic biology, marking an era in which digital cells become a fundamental tool in scientific exploration.