Thermodynamic parameters are central in defining the energy landscape within which protein structures navigate during folding and functional adaptation. In the CAS framework, these parameters are treated not as fixed minima, but as adaptive basins that co-evolve with mutational inputs and interaction dynamics.
Key Thermodynamic Parameters Integrated:
G_folding: The change in Gibbs free energy upon folding, providing a global measure of structural stability. Only mutations that preserve or reduce G_folding are retained with high probability.
G_binding: For enzyme-substrate interaction modeling, this defines the catalytic potential or affinity, becoming a primary fitness proxy.
G_mutation: Change in Gibbs energy upon mutation, which guides whether a particular mutation is stabilizing, destabilizing, or neutral.
These values can be obtained through hybrid methods:
Empirically from databases (e.g., ProTherm, FireProt),
Computationally via Rosetta, FoldX, or MD simulations,
Learned statistically from large protein design datasets.
Within CAS, thermodynamic constraints are not merely used for post hoc filtering, but proactively embedded in the probabilistic mutation generator and trajectory evaluator.
2. Probabilistic Mutation Logic
