Computational Enzyme Redesign Enhances Tolerance to Denaturants for Peptide C-Terminal Amidation

The escalating demand for biocatalysts in pharmaceutical and biochemical applications underscores the critical imperative to enhance enzyme activity and durability under high denaturant concentrations. Nevertheless, the development of a practical computational redesign protocol for improving enzyme tolerance to denaturants is challenging due to the limitations of relying solely on model-driven approaches to adequately capture denaturant–enzyme interactions. In this study, we introduce an enzyme redesign strategy termed GRAPE_DA, which integrates multiple data-driven and model-driven computational methods to mitigate the sampling biases inherent in a single approach and comprehensively predict beneficial mutations on both the protein surface and backbone. To illustrate the methodology's effectiveness, we applied it to engineer a peptidylamidoglycolate lyase, resulting in a variant exhibiting up to a 24-fold increase in peptide C-terminal amidation activity under 2.5 M guanidine hydrochloride. We anticipate that this integrated engineering strategy will facilitate the development of enzymatic peptide synthesis and functionalization under denaturing conditions and highlight the role of engineering surface residues in governing protein stability.