Computation-Aided Engineering of Cytochrome P450 for the Production of Pravastatin

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Advancing microbial production through artificial intelligence-aided biology

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.

Enhanced catalytic activity and stability of lactate dehydrogenase for cascade catalysis of D-PLA by rational design

Serving as a vital medical intermediate and an environmentally-friendly preservative, D-PLA exhibits substantial potential across various industries. In this report, the urgent need for efficient production motivated us to achieve the rational design of lactate dehydrogenase and enhance catalytic efficiency. Surprisingly, the enzymatic properties revealed that a mutant enzyme, LrLDHT247I/D249A/F306W/A214Y (LrLDH-M1), had a viable catalytic advantage. It demonstrated a 3.3-fold increase in specific enzyme activity and approximately a 2.08-fold improvement of Kcat. Correspondingly, molecular docking analysis provided a supporting explanation for the lower Km and higher Kcat/Km of the mutant enzyme. Thermostability analysis exhibited increased half-lives and the deactivation rate constants decreased at different temperatures (1.47-2.26-fold). In addition, the mutant showed excellent resistance abilities in harsh environments, particularly under acidic conditions. Then, a two-bacterium (E. coli/pET28a-lrldh-M1 and E. coli/pET28a-ladd) coupled catalytic system was developed and realized a significant conversion rate (77.7%) of D-phenyllactic acid, using 10 g/L L-phenylalanine as the substrate in a two-step cascade reaction.

Regio- and stereoselective steroid hydroxylation by CYP109A2 from Bacillus megaterium explored by X-ray crystallography and computational modeling

The P450 monooxygenase CYP109A2 from Bacillus megaterium DSM319 was previously found to convert vitamin D3 (VD3) to 25-hydroxyvitamin D3. Here, we show that this enzyme is also able to convert testosterone in a highly regio- and stereoselective manner to 16β-hydroxytestosterone. To reveal the structural determinants governing the regio- and stereoselective steroid hydroxylation reactions catalyzed by CYP109A2, two crystal structures of CYP109A2 were solved in similar closed conformations, one revealing a bound testosterone in the active site pocket, albeit at a nonproductive site away from the heme-iron. To examine whether the closed crystal structures nevertheless correspond to a reactive conformation of CYP109A2, docking and molecular dynamics (MD) simulations were performed with testosterone and vitamin D3 (VD3) present in the active site. These MD simulations were analyzed for catalytically productive conformations, the relative occurrences of which were in agreement with the experimentally determined stereoselectivities if the predicted stability of each carbon-hydrogen bond was taken into account. Overall, the first-time determination and analysis of the catalytically relevant 3D conformation of CYP109A2 will allow for future small molecule ligand screening in silico, as well as enabling site-directed mutagenesis toward improved enzymatic properties of this enzyme.

Computational redesign of cytochrome P450 CYP102A1 for highly stereoselective omeprazole hydroxylation by UniDesign

Cytochrome P450 CYP102A1 is a prototypic biocatalyst that has great potential in chemical synthesis, drug discovery, and biotechnology. CYP102A1 variants engineered by directed evolution and/or rational design are capable of catalyzing the oxidation of a wide range of organic compounds. However, it is difficult to foresee the outcome of engineering CYP102A1 for a compound of interest. Here, we introduce UniDesign as a computational framework for enzyme design and engineering. We tested UniDesign by redesigning CYP102A1 for stereoselective metabolism of omeprazole (OMP), a proton pump inhibitor, starting from an active but nonstereoselective triple mutant (TM: A82F/F87V/L188Q). To shift stereoselectivity toward (R)-OMP, we computationally scanned three active site positions (75, 264, and 328) for mutations that would stabilize the binding of the transition state of (R)-OMP while destabilizing that of (S)-OMP and picked three variants, namely UD1 (TM/L75I), UD2 (TM/A264G), and UD3 (TM/A328V), for experimentation, based on computed energy scores and models. UD1, UD2, and UD3 exhibit high turnover rates of 55 ± 4.7, 84 ± 4.8, and 79 ± 5.7 min-1, respectively, for (R)-OMP hydroxylation, whereas the corresponding rates for (S)-OMP are only 2.2 ± 0.19, 6.0 ± 0.68, and 14 ± 2.8 min-1, yielding an enantiomeric excess value of 92, 87, and 70%, respectively. These results suggest the critical roles of L75I, A264G, and A328V in steering OMP in the optimal orientation for stereoselective oxidation and demonstrate the utility of UniDesign for engineering CYP102A1 to produce drug metabolites of interest. The results are discussed in the context of protein structures.

The Versatile Biocatalyst of Cytochrome P450 CYP102A1: Structure, Function, and Engineering

Wild-type cytochrome P450 CYP102A1 from Bacillus megaterium is a highly efficient monooxygenase for the oxidation of long-chain fatty acids. The unique features of CYP102A1, such as high catalytic activity, expression yield, regio- and stereoselectivity, and self-sufficiency in electron transfer as a fusion protein, afford the requirements for an ideal biocatalyst. In the past three decades, remarkable progress has been made in engineering CYP102A1 for applications in drug discovery, biosynthesis, and biotechnology. The repertoire of engineered CYP102A1 variants has grown tremendously, whereas the substrate repertoire is avalanched to encompass alkanes, alkenes, aromatics, organic solvents, pharmaceuticals, drugs, and many more. In this article, we highlight the major advances in the past five years in our understanding of the structure and function of CYP102A1 and the methodologies used to engineer CYP102A1 for novel applications. The objective is to provide a succinct review of the latest developments with reference to the body of CYP102A1-related literature.

Rational design of enzyme activity and enantioselectivity

The strategy of rational design to engineer enzymes is to predict the potential mutants based on the understanding of the relationships between protein structure and function, and subsequently introduce the mutations using the site-directed mutagenesis. Rational design methods are universal, relatively fast and have the potential to be developed into algorithms that can quantitatively predict the performance of the designed sequences. Compared to the protein stability, it was more challenging to design an enzyme with improved activity or selectivity, due to the complexity of enzyme molecular structure and inadequate understanding of the relationships between enzyme structures and functions. However, with the development of computational force, advanced algorithm and a deeper understanding of enzyme catalytic mechanisms, rational design could significantly simplify the process of engineering enzyme functions and the number of studies applying rational design strategy has been increasing. Here, we reviewed the recent advances of applying the rational design strategy to engineer enzyme functions including activity and enantioselectivity. Five strategies including multiple sequence alignment, strategy based on steric hindrance, strategy based on remodeling interaction network, strategy based on dynamics modification and computational protein design are discussed and the successful cases using these strategies are introduced.