Quantifying the taxonomic bias in enzymology

Dan Tawfik's Lessons for Protein Engineers about Enzymes Adapting to New Substrates

Natural evolution has been creating new complex systems for billions of years. The process is spontaneous and requires neither intelligence nor moral purpose but is nevertheless difficult to understand. The late Dan Tawfik spent years studying enzymes as they adapted to recognize new substrates. Much of his work focused on gaining fundamental insights, so the practical utility of his experiments may not be obvious even to accomplished protein engineers. Here we focus on two questions fundamental to any directed evolution experiment. Which proteins are the best starting points for such experiments? Which trait(s) of the chosen parental protein should be evolved to achieve the desired outcome? We summarize Tawfik's contributions to our understanding of these problems, to honor his memory and encourage those unfamiliar with his ideas to read his publications.

Functional metagenomic screening identifies an unexpected β-glucuronidase

The abundance of recorded protein sequence data stands in contrast to the small number of experimentally verified functional annotation. Here we screened a million-membered metagenomic library at ultrahigh throughput in microfluidic droplets for β-glucuronidase activity. We identified SN243, a genuine β-glucuronidase with little homology to previously studied enzymes of this type, as a glycoside hydrolase 3 family member. This glycoside hydrolase family contains only one recently added β-glucuronidase, showing that a functional metagenomic approach can shed light on assignments that are currently 'unpredictable' by bioinformatics. Kinetic analyses of SN243 characterized it as a promiscuous catalyst and structural analysis suggests regions of divergence from homologous glycoside hydrolase 3 members creating a wide-open active site. With a screening throughput of >10⁷ library members per day, picolitre-volume microfluidic droplets enable functional assignments that complement current enzyme database dictionaries and provide bridgeheads for the annotation of unexplored sequence space.

Structures and kinetics of Thermotoga maritima MetY reveal new insights into the predominant sulfurylation enzyme of bacterial methionine biosynthesis

Bacterial methionine biosynthesis can take place by either the trans-sulfurylation route or direct sulfurylation. The enzymes responsible for trans-sulfurylation have been characterized extensively because they occur in model organisms such as Escherichia coli. However, direct sulfurylation is actually the predominant route for methionine biosynthesis across the phylogenetic tree. In this pathway, most bacteria use an O-acetylhomoserine aminocarboxypropyltransferase (MetY) to catalyze the formation of homocysteine from O-acetylhomoserine and bisulfide. Despite the widespread distribution of MetY, this pyridoxal 5'-phosphate-dependent enzyme remains comparatively understudied. To address this knowledge gap, we have characterized the MetY from Thermotoga maritima (TmMetY). At its optimal temperature of 70 °C, TmMetY has a turnover number (apparent k_cat = 900 s^-1) that is 10- to 700-fold higher than the three other MetY enzymes for which data are available. We also present crystal structures of TmMetY in the internal aldimine form and, fortuitously, with a β,γ-unsaturated ketimine reaction intermediate. This intermediate is identical to that found in the catalytic cycle of cystathionine γ-synthase (MetB), which is a homologous enzyme from the trans-sulfurylation pathway. By comparing the TmMetY and MetB structures, we have identified Arg270 as a critical determinant of specificity. It helps to wall off the active site of TmMetY, disfavoring the binding of the first MetB substrate, O-succinylhomoserine. It also ensures a strict specificity for bisulfide as the second substrate of MetY by occluding the larger MetB substrate, cysteine. Overall, this work illuminates the subtle structural mechanisms by which homologous pyridoxal 5'-phosphate-dependent enzymes can effect different catalytic, and therefore metabolic, outcomes.

Quantifying the taxonomic bias in enzymology

The ongoing biotechnological revolution is rooted in our knowledge of enzymes. However, metagenomics is showing how little we know about Earth's enzyme repertoire. Deep sequencing has revolutionized our view of the tree of life. The genomes of newly‐discovered organisms are replete with novel sequences, emphasizing the trove of enzyme structures and functions waiting to be explored by biochemists. Here, we sought to draw attention to the vastness of the “enzymatic dark matter” within the tree of life by placing enzymological knowledge in the context of phylogeny. We used kinetic parameters from the BRaunschweig ENzyme DAtabase (BRENDA) as our proxy for enzymological knowledge. Mapping 12,677 BRENDA entries onto the phylogenetic tree revealed that 55% of these data were from eukaryotes, even though they are the least diverse part of the tree. At the next taxonomic level, only four of 18 archaeal phyla and 24 of 111 bacterial phyla are represented in the BRENDA dataset. One phylum, the Proteobacteria, accounts for over half of all bacterial entries. Similarly, the supergroup Amorphea, which includes animals and fungi, contains over half the data on eukaryotes. Many major taxonomic groups are notable for their complete absence from BRENDA, including the ultra‐diverse bacterial Candidate Phyla Radiation. At the species level, five mammals (including human) contribute 15% of BRENDA entries. The taxonomic bias in enzymology is strong, but in the era of gene synthesis we now have the tools to address it. Doing so promises to enrich our biochemical understanding of life and uncover powerful new biocatalysts.