Every generation of simulated mutations is followed by:
1. Structural recalibration, using local energy minimization or threading;
2. Thermodynamic scoring, rejecting or retaining variants probabilistically;
3. Emergent output evaluation (O), to assess fitness in functional terms.
This creates a closed-loop adaptive algorithm, where thermodynamic laws act as dynamic constraints that evolve in tandem with systemic configurations---a principle core to CAS theory.
5. Toward a Multi-Level Thermo-Probabilistic Evolution Engine
The integration of thermodynamics and probabilistic mutation logic does more than improve prediction fidelity. It enables:
Emergence of non-obvious mutational paths that traditional energy minimization overlooks,
Evolution of novel folds or hybrid domains with no natural analogs,
Embedding of synthetic constraints such as biodegradability, reaction to environmental triggers, or cooperative catalysis.
Ultimately, this hybrid modeling philosophy positions protein evolution as a probabilistic exploration of a thermodynamic manifold, governed not solely by selection or entropy, but by adaptive interaction between system-level variables.
