Molecular system identification for enzyme directed evolution and design.

Area of Science:

• Computational Chemistry and Molecular Modeling
• Catalysis and Reaction Engineering
• Biocatalysis and Enzyme Engineering

Background:

• Rational catalyst design necessitates accurate free energy difference measurements within catalytic mechanisms.
• Current experimental methods often yield parameters not directly correlating to free energies or provide insufficient data for structure-activity relationships.
• High-throughput screening demands statistically efficient methods for estimating complete sets of free energy differences.

Purpose of the Study:

• To present a theoretical and algorithmic system identification framework for optimal estimation of free energy differences in solution-phase catalysts.
• To enable the precise determination of all free energy differences relevant to catalytic activity for improved catalyst design.
• To enhance the efficiency and reduce uncertainty in computational catalyst design workflows.

Main Methods:

• Developed a system identification framework for estimating free energy differences in catalytic mechanisms.
• Incorporated programmable logic for automation, prescribing experimental measurements and input variables.
• Utilized decision-theoretic logic for model reduction to optimize high-throughput catalyst design.

Main Results:

• The framework optimally estimates complete sets of free energy differences relevant to catalytic activity.
• It minimizes uncertainty in free energy estimates for successive catalyst Hamiltonian designs.
• Demonstrated potential for automation via fluidic control systems for enhanced catalyst design.

Conclusions:

• The proposed framework provides a robust method for accurately characterizing catalyst mechanisms.
• It significantly advances the rational design of chemical catalysts, particularly enzymes.
• Automation and decision-theoretic model reduction promise to accelerate catalyst discovery and optimization.