This composite metric is a proxy for functional optimization, balancing both substrate affinity and reaction velocity. In CAS terms, it reflects the fitness value assigned to specific mutational topologies under selective pressure.
Modeling Approaches:
Quantum mechanics/molecular mechanics (QM/MM) hybrid simulations can estimate transition state stabilization and active site reactivity.
Kinetic Monte Carlo simulations allow exploration of multiple reaction pathways, incorporating stochastic fluctuations and structural perturbations.
Topological Embedding:
Catalytic efficiency is assigned as a fitness scalar to graph nodes in the mutation network.
The emergence of high-efficiency variants in otherwise distant regions of sequence space may indicate evolutionary attractors, where multiple weakly functional routes converge on optimal performance.
The rate of change of k_cat/K_m in response to single or multiple mutations provides a measure of functional sensitivity, which can be analyzed using centrality metrics (e.g., eigenvector centrality) or curvature-based topological indicators.
3. Emergent Function as Multi-Scalar Output
Both binding energy and catalytic efficiency are subject to nonlinear interactions and trade-offs. For instance, a mutation that increases binding affinity may simultaneously destabilize the transition state, reducing catalytic performance---a classic case of epistatic interference in evolutionary networks.
In a CAS framework:
