The core advantage of our model lies not in replacing AlphaFold or Rosetta, but in offering a complementary system-level layer that treats protein evolution as a complex adaptive process---not just as a sequence-to-structure problem, but as a dynamic, multi-objective, population-level exploration.
While AlphaFold excels at snapshots, our CAS framework captures the movie of enzyme evolution.
5. Proposed Hybrid Strategy
We envision an integrated loop where:
CAS-based simulations generate mutation trajectories and structural hypotheses
AlphaFold2 or ESMFold refines tertiary structure predictions of high-scoring variants
Rosetta validates thermodynamic stability
Wet-lab experiments test catalytic efficiency
This layered system could guide directed evolution far more efficiently, by focusing experimental effort on CAS-predicted emergent hotspots, rather than random mutagenesis or brute-force scanning.
5.C. Analysis of Emergent Structural Motifs Across Evolutionary Cycles
A critical capability of a Complex Adaptive System (CAS)-based evolution engine is the identification of emergent motifs---recurring, functionally relevant structural configurations that arise not from deterministic design, but from iterative cycles of mutation, selection, and structural adaptation. In the context of synthetic PETase evolution, these motifs often correspond to adaptive fold topologies, residue clustering patterns, and novel catalytic microenvironments that do not exist in the wild-type enzyme.
