Using multi-objective computational design to extend protein promiscuity.

Area of Science:

• Biochemistry
• Protein Engineering
• Computational Biology

Background:

• Enzymes often exhibit promiscuous activities alongside their native functions, offering potential for biotechnological applications.
• Natural protein promiscuity is limited as enhancing new functions can decrease native activity.
• Computational protein design offers a method to overcome these limitations.

Purpose of the Study:

• To explore computational protein design for creating multipurpose catalysts with enhanced promiscuous activities.
• To develop a minimal-perturbation approach to introduce new active sites without compromising protein folding or native function.
• To validate the approach by engineering esterase activity into E. coli thioredoxin.

Main Methods:

• Utilizing high-plasticity positions near the native active site for mutations.
• Employing combinatorial optimization to balance de novo catalytic activity and folding free-energy.
• Constructing a Pareto Set of optimal stability/promiscuous-function solutions.
• Introducing promiscuous esterase activity in E. coli thioredoxin via mutations near the disulfide bridge.

Main Results:

• Successfully introduced a promiscuous esterase activity into E. coli thioredoxin.
• Native oxidoreductase activity was not compromised and showed a 1.5-fold enhancement.
• Demonstrated the feasibility of creating multipurpose catalysts using computational design.

Conclusions:

• Computational protein design can effectively expand the scope and applications of protein promiscuity.
• The minimal-perturbation, multi-objective optimization approach is a viable strategy for engineering novel enzyme functions.
• This work provides a framework for designing proteins with multiple, distinct catalytic activities.