By unifying folding pathways and mutation networks into a shared graph-theoretic language, we arrive at a multilayer adaptive system:
The intra-layer dynamics (folding) govern how a given sequence maps to a structure,
The inter-layer dynamics (mutation) describe how sequences evolve across generations.
These layers influence one another recursively:
Structural constraints shape which mutations are viable,
Mutations alter topological properties that feedback into folding pathways.
Such recursive interactions are characteristic of CAS, where structure and function co-emerge from local rules and global feedback. We model these relationships using coupled graph layers and reinforcement dynamics, enabling:
Simulation of evolutionary trajectories toward optimized function,
Prediction of nonlinear responses to environmental perturbation (e.g., temperature, pH, or xenobiotic presence),
Exploration of functional exaptation, where structural innovations repurpose previous modules.
4. Implications for Synthetic Enzyme Design
