Insights into E.†coli Cyclopropane Fatty Acid Synthase (CFAS) Towards Enantioselective Carbene Free Biocatalytic Cyclopropanation.

Cyclopropane fatty acid synthases (CFAS) are a class of S-adenosylmethionine (SAM) dependent methyltransferase enzymes able to catalyse the cyclopropanation of unsaturated phospholipids. Since CFAS enzymes employ SAM as a methylene source to cyclopropanate alkene substrates, they have the potential to be mild and more sustainable biocatalysts for cyclopropanation transformations than current carbene-based approaches. This work describes the characterisation of E. coli CFAS (ecCFAS) and its exploitation in the stereoselective biocatalytic synthesis of cyclopropyl lipids. ecCFAS was found to convert phosphatidylglycerol (PG) to methyl dihydrosterculate 1 with up to 58 % conversion and 73 % ee and the absolute configuration (9S,10R) was established. Substrate tolerance of ecCFAS was found to be correlated with the electronic properties of phospholipid headgroups and for the first time ecCFAS was found to catalyse cyclopropanation of both phospholipid chains to form dicyclopropanated products. In addition, mutagenesis and in silico experiments were carried out to identify the enzyme residues with key roles in catalysis and to provide structural insights into the lipid substrate preference of ecCFAS. Finally, the biocatalytic synthesis of methyl dihydrosterculate 1 and its deuterated analogue was also accomplished combining recombinant ecCFAS with the SAM regenerating AtHMT enzyme in the presence of CH3I and CD3I respectively.

A Xylenol Orange-Based Screening Assay for the Substrate Specificity of Flavin-Dependent para-Phenol Oxidases.

Vanillyl alcohol oxidase (VAO) and eugenol oxidase (EUGO) are flavin-dependent enzymes that catalyse the oxidation of para-substituted phenols. This makes them potentially interesting biocatalysts for the conversion of lignin-derived aromatic monomers to value-added compounds. To facilitate their biocatalytic exploitation, it is important to develop methods by which variants of the enzymes can be rapidly screened for increased activity towards substrates of interest. Here, we present the development of a screening assay for the substrate specificity of para-phenol oxidases based on the detection of hydrogen peroxide using the ferric-xylenol orange complex method. The assay was used to screen the activity of VAO and EUGO towards a set of twenty-four potential substrates. This led to the identification of 4-cyclopentylphenol as a new substrate of VAO and EUGO and 4-cyclohexylphenol as a new substrate of VAO. Screening of a small library of VAO and EUGO active-site variants for alterations in their substrate specificity led to the identification of a VAO variant (T457Q) with increased activity towards vanillyl alcohol (4-hydroxy-3-methoxybenzyl alcohol) and a EUGO variant (V436I) with increased activity towards chavicol (4-allylphenol) and 4-cyclopentylphenol. This assay provides a quick and efficient method to screen the substrate specificity of para-phenol oxidases, facilitating the enzyme engineering of known para-phenol oxidases and the evaluation of the substrate specificity of novel para-phenol oxidases.

Learning Strategies in Protein Directed Evolution.

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.