Abstract
Cold-adapted bidomain enzymes are vital for transforming modern industries by decreasing energy consumption, delivering economic benefits, and fostering sustainability through reduced greenhouse gas emissions. Yet, the design strategies guiding their acquisition of cold adaptation remain unknown. Here, we developed an integrated computational-experimental strategy to engineer bidomain enzymes for enhanced cold-adaptation. Using five model amylase variants exhibiting different degrees of cold adaptation, we identified a descriptor from molecular dynamics simulations, namely domain separation index (DSI), which positively correlates with bidomain amylases’ relative activity at 0°C. The bidomain amylase variants with a longer distance between its catalytic domain and carbohydrate-binding module (i.e., a high DSI) were observed to demonstrate cold adaptation. Guided by DSI, we developed a high-throughput molecular modeling protocol to convert the thermophilic Pseudomonas saccharophila amylase (psA) into a cold-adapted bidomain enzyme, virtually screening 120 psA variants with different linkers. Two psA variants with a greater DSI value were selected and experimentally confirmed to be cold-adapted, with the psA121 variant achieving a 12-fold increase in relative activity at 0°C from 2.4% (specific activity: 14 U/mg) to 30.5% (specific activity: 219 U/mg). Conformational analyses reveal that compared to non-cold-adapted counterparts, cold-adapted variants leverage its linker to induce domain separation and enhance flexibility of active-site and binding loop via dynamic allostery, thereby promoting substrate recruiting, binding, and catalysis at lower temperatures. Statistical analyses of 120 variants demonstrate that helical motifs within linkers drive interdomain separation. Overall, our study offers strategies for engineering cold-adapted bidomain enzymes and suggests the molecular basis of cold adaptation in bidomain amylases.
Supplementary materials
Title
Supplementary Information
Description
The Supplementary Information primarily includes DSI and RMSF data derived from MD simulations, structural conformations, experimental assay data, sequence information for amylase variants, and code for MD simulations.
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