Improving enzymes by directed evolution requires the navigation of very large search spaces; we survey how to do this intelligently.

The microbial production of fine chemicals provides a promising biosustainable manufacturing solution that has led to the successful production of a growing catalog of natural products and high-value chemicals. However, development at industrial levels has been hindered by the large resource investments required. Here we present an integrated Design–Build-Test–Learn (DBTL) pipeline for the discovery and optimization of biosynthetic pathways, which is designed to be compound agnostic and automated throughout. We initially applied the pipeline for the production of the flavonoid (2S)-pinocembrin in Escherichia coli, to demonstrate rapid iterative DBTL cycling with automation at every stage. In this case, application of two DBTL cycles successfully established a production pathway improved by 500-fold, with competitive titers up to 88 mg L−1. The further application of the pipeline to optimize an alkaloids pathway demonstrates how it could facilitate the rapid optimization of microbial strains for production of any chemical compound of interest.

The field of synthetic biology aims to make the design of biological systems predictable, shrinking the huge design space to practical numbers for testing. When designing microbial cell factories, most optimization efforts have focused on enzyme and strain selection/ engineering, pathway regulation, and process development. In silico tools for the predictive design of bacterial ribosome binding sites (RBSs) and RBS libraries now allow translational tuning of biochemical pathways; however, methods for predicting optimal RBS combinations in multigene pathways are desirable. Here we present the implementation of machine learning algorithms to model the RBS sequence−phenotype relationship from representative subsets of large combinatorial RBS libraries allowing the accurate prediction of optimal high-producers. Applied to a recombinant monoterpenoid production pathway in Escherichia coli, our approach was able to boost production titers by over 60% when screening under 3% of a library. To facilitate library screening, a multiwell plate fermentation procedure was developed, allowing increased screening throughput with sufficient resolution to discriminate between high and low producers. High producers from one library did not translate during scale-up, but the reduced screening requirements allowed rapid rescreening at the larger scale. This methodology is potentially compatible with any biochemical pathway and provides a powerful tool toward predictive design of bacterial production chassis.

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Monoterpenes (C10 isoprenoids) are a structurally diverse group of natural compounds that are attractive to industry as flavours and fragrances. Monoterpenes are produced from a single linear substrate, geranyl diphosphate, by a group of enzymes called the monoterpene cyclases/synthases (mTC/Ss) that catalyse high-energy cyclisation reactions involving unstable carbocation intermediates. Efforts towards producing monoterpenes via biocatalysis or metabolic engineering often result in the formation of multiple products due to the nature of the highly branched reaction mechanism of mTC/Ss. Rational engineering of mTC/Ss is hampered by the lack of correlation between the active site sequence and cyclisation type. We used available mutagenesis data to show that amino acids involved in product outcome are clustered and spatially conserved within the mTC/S family. Consensus sequences for three such plasticity regions were introduced in different mTC/S with increasingly complex cyclisation cascades, including the model enzyme limonene synthase (LimS). In all three mTC/S studied, mutations in the first two regions mostly give rise to products that result from premature quenching of the linalyl or α-terpinyl cations, suggesting that both plasticity regions are involved in the formation and stabilisation of cations early in the reaction cascade. A LimS variant with mutations in the second region (S454G, C457V, M458I), produced mainly more complex bicyclic products. QM/MM MD simulations reveal that the second cyclisation is not due to compression of the C2-C7 distance in the α-terpinyl cation, but is the result of an increased distance between C8 of the α-terpinyl cation and two putative bases (W324, H579) located on the other side of the active site, preventing early termination by deprotonation. Such insights into the impact of mutations can only be obtained using integrated experimental and computational approaches, and will aid the design of altered mTC/S activities towards clean monoterpenoid products.