Highly active enzymes by automated combinatorial backbone assembly and sequence design.

Area of Science:

• Biochemistry and Molecular Biology
• Protein Engineering
• Computational Biology

Background:

• Automated design of enzymes with wild-type-like catalytic activity remains a significant challenge in protein engineering.
• Existing methods often struggle to achieve high catalytic efficiency and structural diversity in designed enzymes.

Purpose of the Study:

• To present a general, automated method for enzyme design via combinatorial backbone assembly.
• To demonstrate the method's efficacy in generating active enzymes with wild-type-like properties.

Main Methods:

• Utilized a set of homologous, structurally diverse enzyme structures as starting points.
• Employed combinatorial backbone assembly to create novel backbone combinations.
• Optimized amino acid sequences using Rosetta, while preserving essential catalytic residues.

Main Results:

• Designed 43 glycoside hydrolase 10 (GH10) xylanases and 34 phosphotriesterase-like lactonases (PLLs).
• Achieved functional activity in 21 GH10 and 7 PLL designs, including those from templates with less than 25% sequence identity.
• Identified four designs exhibiting activity comparable to natural enzymes, with one GH10 design's atomic accuracy confirmed by crystallography.

Conclusions:

• Combinatorial backbone assembly is a viable strategy for generating stable and active enzymes.
• The method enables the creation of structurally diverse enzymes with potentially altered selectivity or enhanced activity.
• This approach represents a significant advancement in the automated design of functional proteins.