A systems-level understanding of Gram-positive bacteria is important from both an environmental and health perspective and is most easily obtained when high-quality, validated genomic resources are available. To this end, we constructed two ordered, barcoded, erythromycin-resistance- and kanamycin-resistance-marked single-gene deletion libraries of the Gram-positive model organism, Bacillus subtilis. The libraries comprise 3,968 and 3,970 genes, respectively, and overlap in all but four genes. Using these libraries, we update the set of essential genes known for this organism, provide a comprehensive compendium of B. subtilis auxotrophic genes, and identify genes required for utilizing specific carbon and nitrogen sources, as well as those required for growth at low temperature. We report the identification of enzymes catalyzing several missing steps in amino acid biosynthesis. Finally, we describe a suite of high-throughput phenotyping methodologies and apply them to provide a genome-wide analysis of competence and sporulation. Altogether, we provide versatile resources for studying gene function and pathway and network architecture in Gram-positive bacteria.

Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis

De novo protein design for catalysis of any desired chemical reaction is a long-standing goal in protein engineering because of the broad spectrum of technological, scientific and medical applications. However, mapping protein sequence to protein function is currently neither computationally nor experimentally tangible. Here, we develop ProteinGAN, a self-attention-based variant of the generative adversarial network that is able to ‘learn’ natural protein sequence diversity and enables the generation of functional protein sequences. ProteinGAN learns the evolutionary relationships of protein sequences directly from the complex multidimensional amino-acid sequence space and creates new, highly diverse sequence variants with natural-like physical properties. Using malate dehydrogenase (MDH) as a template enzyme, we show that 24% (13 out of 55 tested) of the ProteinGAN-generated and experimentally tested sequences are soluble and display MDH catalytic activity in the tested conditions in vitro, including a highly mutated variant of 106 amino-acid substitutions. ProteinGAN therefore demonstrates the potential of artificial intelligence to rapidly generate highly diverse functional proteins within the allowed biological constraints of the sequence space. A protein’s three-dimensional structure and properties are defined by its amino-acid sequence, but mapping protein sequence to protein function is a computationally highly intensive task. A new generative adversarial network approach learns from natural protein sequences and generates new, diverse protein sequence variations, which are experimentally tested.

Expanding functional protein sequence spaces using generative adversarial networks

Unculturable bacterial communities provide a rich source of biocatalysts, but their experimental discovery by functional metagenomics is difficult, because the odds are stacked against the experimentor. Here we demonstrate functional screening of a million-membered metagenomic library in microfluidic picolitre droplet compartments. Using bait substrates, new hydrolases for sulfate monoesters and phosphotriesters were identified, mostly based on promiscuous activities presumed not to be under selection pressure. Spanning three protein superfamilies, these break new ground in sequence space: promiscuity now connects enzymes with only distantly related sequences. Most hits could not have been predicted by sequence analysis, because the desired activities have never been ascribed to similar sequences, showing how this approach complements bioinformatic harvesting of metagenomic sequencing data. Functional screening of a library of unprecedented size with excellent assay sensitivity has been instrumental in identifying rare genes constituting catalytically versatile hubs in sequence space as potential starting points for the acquisition of new functions.

/pdf/ultrahigh-throughput-discovery-of-promiscuous-enzymes-by-kxd4s9xzal.pdf

Ultrahigh-throughput discovery of promiscuous enzymes by picodroplet functional metagenomics.

The exponentially increasing number of protein and nucleic acid sequences provides opportunities to discover novel enzymes, metabolic pathways, and metabolites/natural products, thereby adding to our knowledge of biochemistry and biology. The challenge has evolved from generating sequence information to mining the databases to integrating and leveraging the available information, i.e., the availability of “genomic enzymology” web tools. Web tools that allow identification of biosynthetic gene clusters are widely used by the natural products/synthetic biology community, thereby facilitating the discovery of novel natural products and the enzymes responsible for their biosynthesis. However, many novel enzymes with interesting mechanisms participate in uncharacterized small-molecule metabolic pathways; their discovery and functional characterization also can be accomplished by leveraging information in protein and nucleic acid databases. This Perspective focuses on two genomic enzymology web tools that assis...

http://pubs.acs.org/doi/pdf/10.1021/acs.biochem.7b00614

Genomic Enzymology: Web Tools for Leveraging Protein Family Sequence-Function Space and Genome Context to Discover Novel Functions.

Shining a light on enzyme promiscuity.

Large-scale activity profiling of enzyme superfamilies provides information about cellular functions as well as the intrinsic binding capabilities of conserved folds. Herein, the functional space of the ubiquitous haloalkanoate dehalogenase superfamily (HADSF) was revealed by screening a customized substrate library against >200 enzymes from representative prokaryotic species, enabling inferred annotation of ∼35% of the HADSF. An extremely high level of substrate ambiguity was revealed, with the majority of HADSF enzymes using more than five substrates. Substrate profiling allowed assignment of function to previously unannotated enzymes with known structure, uncovered potential new pathways, and identified iso-functional orthologs from evolutionarily distant taxonomic groups. Intriguingly, the HADSF subfamily having the least structural elaboration of the Rossmann fold catalytic domain was the most specific, consistent with the concept that domain insertions drive the evolution of new functions and that the broad specificity observed in HADSF may be a relic of this process.

https://www.pnas.org/content/pnas/112/16/E1974.full.pdf

Panoramic view of a superfamily of phosphatases through substrate profiling

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Panoramic view of a superfamily of phosphatases through substrate profiling