Bottom-Up Nonempirical Approach To Reducing Search Space in Enzyme Design Guided by Catalytic Fields

Area of Science:

• Biocatalysis and Enzyme Engineering
• Computational Chemistry
• Protein Design

Background:

• Current enzyme design methods yield biocatalysts with suboptimal activity compared to natural enzymes.
• In vitro laboratory-directed evolution (LDE) is the primary method for enzyme improvement, often requiring extensive computational resources and assumptions.
• Existing computational approaches struggle to match the efficiency and activity of natural enzymes.

Purpose of the Study:

• To propose a novel computational strategy for enzyme design based on a
• bottom-up
• approach.
• To enable direct, one-step determination of optimal catalytic site characteristics.
• To provide a detailed insight into enzyme dynamics and epistatic effects.

Main Methods:

• Utilizing catalytic fields derived from transition state and reactant complex wave functions.
• Implementing a
• bottom-up
• mutation prediction strategy.
• Incorporating a library of atomic multipoles for amino acid side-chain rotamers to model preorganized catalytic environments.

Main Results:

• The proposed method directly determines quantitative angular characteristics of optimal catalytic sites.
• It accounts for both transition-state stabilization (TSS) and ground-state destabilization (GSD) effects.
• Qualitative agreement was achieved with experimental LDE data for Kemp eliminase KE07 mutants.

Conclusions:

• The novel computational approach offers a more efficient and accurate method for enzyme design.
• This strategy facilitates the creation of biocatalysts with improved activity by optimizing catalytic environments.
• The findings provide a deeper understanding of enzyme dynamics and the impact of mutations.