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The majority of protein sequence data published today is of metagenomic origin. However, our ability to assign functions to these sequences is often hampered by our general inability to cultivate the larger part of microbial species and the sheer amount of sequence data generated in these projects. Here we present a combination of bioinformatics, synthetic biology, and  Escherichia coli  genetics to discover biocatalysts in metagenomic datasets. We created a subset of the Global Ocean Sampling dataset, the largest metagenomic project published to date, by removing all proteins that matched Hidden Markov Models of known protein families from PFAM and TIGRFAM with high confidence ( E -value > 10 -5 ). This essentially left us with proteins with low or no homology to known protein families, still encompassing ~1.7 million different sequences. In this subset, we then identified protein families  de novo  with a Markov clustering algorithm. For each protein family, we defined a single representative based on its phylogenetic relationship to all other members in that family. This reduced the dataset to ~17,000 representatives of protein families with more than 10 members. Based on conserved regions typical for lipases and esterases, we selected a representative gene from a family of 27 members for synthesis. This protein, when expressed in  E. coli , showed lipolytic activity toward para-nitrophenyl (pNP) esters. The  K  m -value of the enzyme was 66.68 μM for pNP-butyrate and 68.08 μM for pNP-palmitate with  k  cat / K  m  values at 3.4 × 10 6  and 6.6 × 10 5  M -1 s -1 , respectively. Hydrolysis of model substrates showed enantiopreference for the R-form. Reactions yielded 43 and 61% enantiomeric excess of products with ibuprofen methyl ester and 2-phenylpropanoic acid ethyl ester, respectively. The enzyme retains 50% of its maximum activity at temperatures as low as 10°C, its activity is enhanced in artificial seawater and buffers with higher salt concentrations with an optimum osmolarity of 3,890 mosmol/l.

A combined bioinformatics and functional metagenomics approach to discovering lipolytic biocatalysts