The propagation of these changes across the graph enables modeling of epistasis, compensatory dynamics, and function-altering cascades. Crucially, by simulating how local perturbations generate global graph changes, we can identify tipping points and evolutionary attractors---mirroring bifurcation theory in network form.
4. Applications in Predictive Folding and Functional Divergence
This graph-based modeling framework offers several advantages in predictive design of anti-plastic enzymes or other synthetic biomolecules:
Folding prediction: Use spectral properties and graph Laplacians to simulate folding trajectories under thermodynamic constraints.
Function mapping: Infer active site emergence or substrate specificity by detecting graph motifs (e.g., catalytic triads, hydrophobic pockets).
Evolutionary branching: Model divergent paths using graph distance metrics or random walk simulations across the mutational graph landscape.
In combination with reinforcement learning or probabilistic sampling, the system can evolve graph states toward functionally viable and structurally stable configurations, guided not just by deterministic energy minimization but by emergent complexity and adaptive behavior.
5. Toward a Generative Graph Engine for Protein Evolution
The ultimate goal is to develop a generative protein graph engine, where:
Input: a target function or desired catalytic profile (e.g., plastic degradation),
Constraints: environmental parameters, thermodynamic feasibility,
