Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for ispetase structure prediction

Accurate protein structure prediction is critical for rational enzyme engineering, which requires high-fidelity models. This study benchmarks three distinct structure prediction paradigms against the experimental crystal structure of IsPETase, serving as a diagnostic case study. The evaluated approaches include classical homology modeling (SWISS-MODEL), MSA-conditioned diffusion (AlphaFold 3), and generative language modeling (ESM-3). Predicted models were evaluated using stereochemical validation, molecular docking with a PET dimer analogue, and molecular dynamics simulations. While all approaches reproduced the overall fold and preserved the catalytic triad geometry, notable differences were observed in atomic clashes and hydrogen bonding patterns. ESM-3 showed elevated steric clashes and reduced hydrogen bond counts. Molecular dynamics indicated that the experimental structure maintained the highest stability, with SWISS-MODEL closely following, while ESM-3 displayed greater fluctuations, particularly in loop regions. Crucially, blind docking simulations revealed that the ESM-3 active site was sterically occluded, rendering it inaccessible to the PET dimer. This inaccessibility persisted even after targeted energy minimization. These findings suggest that while generative language models represent a powerful capability for rapid scaffold exploration, they do not yet achieve the thermodynamic precision of established homology and evolutionary approaches required for functional active site engineering.