1. Methodology of Motif Emergence Tracking
Each evolutionary cycle in the simulation comprises a set of:
Mutational proposals based on reinforcement learning agents (RL-agents) that sample sequence space
Folding predictions via graph-based residue interaction modeling
Scoring using composite metrics: substrate affinity, systemic stability, and energy efficiency
Over multiple cycles, the simulation engine records not only high-scoring variants but also topological convergence patterns. Structural motifs are extracted via:
Clustering algorithms (e.g., DBSCAN, HDBSCAN) on RMSD-reduced latent structural space
Residue co-evolution analysis across mutational lineages
Allosteric pathway tracing to detect novel communication routes within the protein fold
This data-driven approach identifies motifs not present in the initial sequence population but emerging repeatedly under selective pressure, signifying functional or stability advantages.
2. Types of Emergent Motifs Observed
