Targeted enzyme discovery using metal-coordination mining.

Area of Science:

• Biochemistry
• Structural Biology
• Enzymology

Background:

• Advances in genome sequencing and protein structure prediction offer new avenues for enzyme function analysis.
• Discovering and annotating novel enzymes is critical for understanding genotype-phenotype links and developing industrial biocatalysts.
• Predicting enzyme function accurately remains difficult, often relying on serendipitous discovery.

Purpose of the Study:

• To present a metal-coordination-guided strategy for targeted metalloenzyme discovery using atomic-level mechanistic principles.
• To apply this framework to the AlphaFold2 database for identifying new Fe(II)/α-ketoglutarate-dependent halogenase family members.
• To address the challenge of finding low-abundance radical halogenases within the diverse cupin superfamily due to low sequence conservation.

Main Methods:

• A metal-coordination-guided strategy was developed, leveraging atomic-level mechanistic principles.
• The strategy was applied to mine the AlphaFold2 Protein Structure Database.
• Computational mining was performed at minimal cost to identify potential metalloenzymes.

Main Results:

• The methodology successfully identified previously unrecognized radical halogenase families across diverse phylogenetic groups.
• Two new radical halogenases, AspX and BtnX, were discovered and experimentally validated.
• BtnX exhibited unprecedented substrate promiscuity among radical halogenases, indicating broad biocatalytic potential.

Conclusions:

• The metal-coordination mining strategy enables efficient and targeted discovery of novel metalloenzymes, including challenging-to-find radical halogenases.
• The identified enzymes, particularly BtnX, offer significant potential for diverse biocatalytic applications in pharmaceuticals and industry.
• This approach overcomes limitations of sequence conservation-based methods for enzyme discovery.