5.B. Comparison with AlphaFold and Other Predictive Models
To evaluate the novelty and potential advantages of our CAS-based synthetic evolution framework, it is essential to benchmark it against state-of-the-art structural prediction and protein engineering models, particularly AlphaFold2, Rosetta, and ProteinMPNN. These models have transformed protein design by leveraging deep learning and physics-based simulations. However, they differ significantly in objective, dynamical modeling capability, and handling of emergent evolutionary pathways, which becomes evident when placed in the context of CAS-driven synthetic evolution.
1. AlphaFold2: Strengths and Limitations
AlphaFold2, developed by DeepMind, has demonstrated unprecedented accuracy in predicting protein tertiary structures from primary sequences. However, its operational strengths lie primarily in:
Static structure prediction rather than evolutionary trajectory simulation
Single-sequence focus, not population or mutational lineage modeling
An implicit assumption of thermodynamic minimum, not necessarily aligned with catalytic optimality or functional innovation under selective pressure
In contrast, our CAS-based model:
Feature
AlphaFold2
