A roadmap for metagenomic enzyme discovery.

Area of Science:

• Microbiology
• Biotechnology
• Bioinformatics

Background:

• Metagenomics provides vast sequencing data on microbial communities.
• Predicting biocatalytic functions from sequencing data, especially for secondary metabolism enzymes, remains difficult.
• Environmental DNA (eDNA) studies often rely on PCR or activity-based screening, which can introduce biases.

Purpose of the Study:

• To provide a roadmap for identifying new candidate biosynthetic enzymes from shotgun metagenomic data.
• To explore computational and experimental strategies for mining eDNA.
• To focus on natural product biosynthesis discovery.

Main Methods:

• Comparison of *in silico* enzyme discovery methods: phylogenetics, sequence similarity networks, genomic context, 3D structure, and machine learning.
• Discussion of experimental validation strategies: heterologous expression and screening.
• Review of advancements in meta-omics, single-cell genomics, cell-free expression, and sequence-independent methods.

Main Results:

• Shotgun metagenomics offers an alternative to biased PCR/activity-based screening for enzyme discovery.
• A comprehensive comparison of various computational approaches for inferring enzyme function from sequence data.
• Integration of computational predictions with experimental validation is crucial for confirming enzyme function.

Conclusions:

• Shotgun metagenomics holds significant potential for discovering novel enzymes, especially for natural product biosynthesis.
• A combination of advanced computational tools and experimental validation is key to unlocking the biosynthetic potential of eDNA.
• Future directions emphasize integrated 'omics' approaches and novel expression systems for enhanced enzyme discovery.