A computational pipeline for predicting distal hotspots in an artificial enzyme

Area of Science:

• Biochemistry
• Computational Biology
• Protein Engineering

Background:

• Distal mutations offer a promising avenue for enzyme engineering by influencing protein function through allosteric effects.
• The Lactococcal multidrug resistance regulator (LmrR) scaffold is a versatile platform for developing artificial enzymes.

Purpose of the Study:

• To present an open-source computational workflow for identifying and evaluating the impact of distal mutations on enzyme function.
• To demonstrate the workflow's efficacy using an artificial enzyme based on the LmrR scaffold.

Main Methods:

• Integration of residue network analysis, allosteric pathway mapping, and machine learning for functional site modeling.
• Prioritization of distal mutation sites based on predicted influence on protein dynamics.
• Systematic exploration of single and double point mutations.

Main Results:

• Identification of a single point mutation (Y27H) that increased enzyme activity by 20% and thermostability by 12.5 °C.
• Double mutants achieved further enhancements, with up to a 50% activity increase and a 22.7 °C gain in thermostability.
• The computational workflow successfully guided the identification of beneficial distal mutations.

Conclusions:

• Distal mutations represent a powerful strategy for enhancing enzyme activity and stability.
• The developed computational workflow provides an effective method for guiding enzyme design efforts.
• The study offers a comprehensive protocol to facilitate the application of this strategy by other researchers.