Protein dynamics are often invoked in explanations of enzyme catalysis, but their design has proven elusive. Here we track the role of dynamics in evolution, starting from the evolvable and thermostable ancestral protein AncHLD-RLuc which catalyses both dehalogenase and luciferase reactions. Insertion-deletion (InDel) backbone mutagenesis of AncHLD-RLuc challenged the scaffold dynamics. Screening for both activities reveals InDel mutations localized in three distinct regions that lead to altered protein dynamics (based on crystallographic B-factors, hydrogen exchange, and molecular dynamics simulations). An anisotropic network model highlights the importance of the conformational flexibility of a loop-helix fragment of Renilla luciferases for ligand binding. Transplantation of this dynamic fragment leads to lower product inhibition and highly stable glow-type bioluminescence. The success of our approach suggests that a strategy comprising (i) constructing a stable and evolvable template, (ii) mapping functional regions by backbone mutagenesis, and (iii) transplantation of dynamic features, can lead to functionally innovative proteins.

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Chiral amines are important building blocks, especially for the pharmaceutical industry. Although amine transaminases (ATAs) are versatile enzymes to synthesize chiral amines, the wildtype enzymes do not accept ketones with two large substituents next to the carbonyl functionality. Using bioinformatic tools to design a seven‐site mutant library followed by high‐throughput screening, we were able to identify variants of the enzyme from Vibrio fluvialis (VF‐ATA) with a widened binding pocket, as exemplified for a range of ketones. Three variants allowed the asymmetric synthesis of 2,2‐dimethyl‐1‐phenylpropan‐1‐amine—not accessible by any wildtype ATA described so far. The best variant containing four mutations (L56V, W57C, F85V, V153A) gave 100 % conversion of the ketone to yield the amine with an enantiomeric excess value >99 %, notably with preference for the (R)‐enantiomer. In silico modeling enabled the reconstruction of the substrate binding mode to the newly evolved pocket and, hence, allowed explanation of the experimental results.

Here, we report a widely and generally applicable strategy to obtain reliable information in high-throughput protein screenings of enzyme mutant libraries. The method is based on the usage of the split-GFP technology for the normalization of the expression level of each individual protein variant combined with activity measurements, thus resolving the important problems associated with the different solubility of each mutant and allowing the detection of previously invisible variants. The small size of the employed protein tag (16 amino acids) required for the reconstitution of the GFP fluorescence reduces possible interferences such as enzyme activity variations or solubility disturbances to a minimum. Specific enzyme activity measurements without purification, in situ soluble protein expression monitoring, and data normalization are the powerful outputs of this methodology, thus enabling the accurate identification of improved protein variants during high-throughput screening by substantially reducing the occurrence of false negatives and false positives.

A fully automatized robotic platform has been established to facilitate high-throughput screening for protein engineering purposes. This platform enables proper monitoring and control of growth conditions in the microtiter plate format to ensure precise enzyme production for the interrogation of enzyme mutant libraries, protein stability tests and multiple assay screenings. The performance of this system has been exemplified for four enzyme classes important for biocatalysis such as Baeyer-Villiger monooxygenase, transaminase, dehalogenase and acylase in the high-throughput screening of various mutant libraries. This allowed the identification of novel enzyme variants in a sophisticated and highly reliable manner. Furthermore, the detailed optimization protocols should enable other researchers to adapt and improve their methods. Biotechnol. Bioeng. 2016;113: 1421-1432. © 2016 Wiley Periodicals, Inc.