Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov’SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2ⁿ combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (2⁹), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.

Saturation mutagenesis constitutes a powerful method in the directed evolution of enzymes. Traditional protocols of whole plasmid amplification such as Stratagene’s QuikChange™ sometimes fail when the templates are difficult to amplify. In order to overcome such restrictions, we have devised a simple two-primer, two-stage polymerase chain reaction (PCR) method which constitutes an improvement over existing protocols. In the first stage of the PCR, both the mutagenic primer and the antiprimer that are not complementary anneal to the template. In the second stage, the amplified sequence is used as a megaprimer. Sites composed of one or more residues can be randomized in a single PCR reaction, irrespective of their location in the gene sequence.The method has been applied to several enzymes successfully, including P450-BM3 from Bacillus megaterium, the lipases from Pseudomonas aeruginosa and Candida antarctica and the epoxide hydrolase from Aspergillus niger. Here, we show that megaprimer size as well as the direction and design of the antiprimer are determining factors in the amplification of the plasmid. Comparison of the results with the performances of previous protocols reveals the efficiency of the improved method.

Multidimensional fitness landscapes provide insights into the molecular basis of laboratory and natural evolution. To date, such efforts usually focus on limited protein families and a single enzyme trait, with little concern about the relationship between protein epistasis and conformational dynamics. Here, we report a multiparametric fitness landscape for a cytochrome P450 monooxygenase that was engineered for the regio- and stereoselective hydroxylation of a steroid. We develop a computational program to automatically quantify non-additive effects among all possible mutational pathways, finding pervasive cooperative signs and magnitude epistasis on multiple catalytic traits. By using quantum mechanics and molecular dynamics simulations, we show that these effects are modulated by long-range interactions in loops, helices and β-strands that gate the substrate access channel allowing for optimal catalysis. Our work highlights the importance of conformational dynamics on epistasis in an enzyme involved in secondary metabolism and offers insights for engineering P450s.