Computation-guided engineering of distal mutations in an artificial enzyme

Area of Science:

• Biocatalysis
• Protein Engineering
• Enzyme Dynamics

Background:

• Artificial enzymes offer precise, selective biocatalysis for novel reactions.
• Their catalytic rates often lag behind natural enzymes, limiting industrial use.
• Current designs focus on active sites, neglecting protein dynamics.

Purpose of the Study:

• To enhance artificial enzyme performance by targeting protein dynamics.
• To investigate the impact of distal mutations on enzyme catalysis.
• To improve catalytic rates and stability of engineered biocatalysts.

Main Methods:

• Utilized an innovative algorithm to screen for mutations affecting protein dynamics.
• Engineered an artificial enzyme based on lactococcal multidrug resistance regulator (LmrR).
• Employed microsecond molecular dynamics simulations to analyze conformational changes.

Main Results:

• Identified two distal variants with mutations >11 Å from the active site.
• Achieved a 66% higher turnover number and 14 °C increased thermostability in a recombined variant.
• Observed a shift in productive enzyme conformations due to distal mutations.

Conclusions:

• Distal mutations can significantly enhance artificial enzyme catalytic performance.
• Protein dynamics play a crucial role in optimizing enzyme function.
• This approach offers a new strategy for developing efficient industrial biocatalysts.