Deng, Q.; Qiao, J.; Wang, C.; Ni, X.; Chang, Y.; Zhao, N.; Zhai, R.; Cui, H.; Li, X.; Jin, M.

Protein language models (PLMs) provide a novel computational paradigm for deeply mining evolutionary information. Nevertheless, the discrepancy between the natural evolutionary fitness captured by their zero-shot predictions and actual industrial demands significantly constrains wet-lab success rates. To address this bottleneck, we developed CASPE, a light-weighted protein engineering platform consisting of the CAS and APCNet. CAS leverages gradient activation mapping and multi-layer attention matrices to transform the implicit representations of PLMs into explicit site-importance metrics. Working in tandem with APCNet, CASPE establishes a workflow encompassing the entire trajectory from site localization to residue prediction, which successfully overcomes the fitness misalignment issue, enabling the precise directed evolution of target protein properties. CASPE efficiently identifies thermostable (31-60%) and pH-stable (40-80%) mutants. Specialized models further boost its success in phytase evolution, significantly outperforming FoldX and ESM2-t33 in hit rates. By shifting from global saturated mutagenesis to targeted optimization of feature-relevant sites, CASPE streamlines enzyme evolution, yielding a higher discovery rate of beneficial mutants.