Quantifying the taxonomic bias in enzymology

Area of Science:

• Biochemistry
• Genomics
• Phylogenetics

Background:

• The biotechnological revolution relies on enzyme knowledge, yet metagenomics reveals a vast unexplored enzyme repertoire.
• Deep sequencing has expanded our understanding of life's diversity, uncovering novel sequences with potential enzyme functions.
• Significant gaps exist in our understanding of the full scope of enzyme structures and functions across all domains of life.

Purpose of the Study:

• To highlight the extent of
• enzymatic dark matter
• by analyzing the phylogenetic distribution of known enzyme data.
• To identify taxonomic biases in current enzymological knowledge.

Main Methods:

• Utilized kinetic parameters from the BRaunschweig ENzyme DAtabase (BRENDA) as a measure of enzymological knowledge.
• Mapped 12,677 BRENDA entries onto the phylogenetic tree of life.
• Analyzed data distribution across different taxonomic levels, from phyla to species.

Main Results:

• Enzymological data is heavily skewed, with 55% originating from eukaryotes, despite their lower diversity.
• Only a small fraction of archaeal and bacterial phyla are represented in BRENDA.
• Proteobacteria and the Amorphea supergroup (animals, fungi) dominate the dataset, while major groups like the Candidate Phyla Radiation are absent.
• Five mammal species, including humans, contribute 15% of all entries.

Conclusions:

• A strong taxonomic bias exists in current enzymological knowledge.
• The era of gene synthesis provides tools to overcome these biases and explore uncharted enzymatic diversity.
• Addressing this bias will enrich biochemical understanding and uncover novel biocatalysts.