Moreover, our framework enables simulation of non-equilibrium pathways, encoding thermodynamic fluxes and adaptive resonance---elements typically out of scope in Rosetta-based workflows.
3. ProteinMPNN and Sequence Design Models
ProteinMPNN and related transformer-based models focus on inverse folding: given a 3D backbone, they infer likely amino acid sequences. While powerful in designing sequence-structure compatibility, they:
Lack evolutionary reasoning (no temporal dynamics or mutation history)
Are highly dependent on backbone input, which may itself not reflect realistic in vivo folding
Do not model substrate interaction, catalytic function, or allosteric effects
In contrast, our CAS-based engine integrates not just backbone stability, but also:
Substrate affinity dynamics
Mutation-driven path dependence
Emergent interaction networks, simulated over evolutionary time steps
4. Systemic Integration Advantage
