A method for the systematic selection of enzyme panel candidates by solving the maximum diversity problem

Area of Science:

• Biochemistry
• Bioinformatics
• Enzymology

Background:

• Enzymes are vital industrial biocatalysts, but selecting diverse candidates from large protein families is challenging.
• Systematic methods are needed for efficient enzyme selection and functional annotation.
• Current enzyme selection approaches lack scalability and systematicity.

Purpose of the Study:

• To develop a novel algorithm for the automatic selection of diverse enzyme subsets.
• To improve the efficiency and scalability of enzyme panel selection for experimental characterization.
• To aid in the functional annotation of uncharacterized proteins within enzyme families.

Main Methods:

• Implementation of a tabu search algorithm to solve the maximum diversity problem based on sequence identity.
• Application of the algorithm to three diverse enzyme families.
• Comparison of the algorithm's performance against existing methods like k-medoids.

Main Results:

• The algorithm automatically selects enzyme panels with high richness and relative abundance of known catalytic functions.
• The tabu search approach demonstrates superior performance compared to k-medoids.
• The selected panels facilitate effective experimental characterization and functional annotation.

Conclusions:

• The developed algorithm offers a systematic and efficient solution for selecting diverse enzyme panels.
• This method enhances the discovery of novel industrial biocatalysts and aids in understanding enzyme function.
• The approach is scalable and applicable to large, diverse enzyme families.