Mutation Maker, An Open Source Oligo Design Platform for Protein Engineering

GRACE: Generative Redesign in Artificial Computational Enzymology

Designing de novo enzymes is complex and challenging, especially to maintain the activity. This research focused on motif design to identify the crucial domain in the enzyme and uncovered the protein structure by molecular docking. Therefore, we developed a Generative Redesign in Artificial Computational Enzymology (GRACE), which is an automated workflow for reformation and creation of the de novo enzymes for the first time. GRACE integrated RFdiffusion for structure generation, ProteinMPNN for sequence interpretation, CLEAN for enzyme classification, and followed by solubility analysis and molecular dynamic simulation. As a result, we selected two gene sequences associated with carbonic anhydrase from among 10,000 protein candidates. Experimental validation confirmed that these two novel enzymes, i.e., dCA12_2 and dCA23_1, exhibited favorable solubility, promising substrate-active site interactions, and achieved activity of 400 WAU/mL. This workflow has the potential to greatly streamline experimental efforts in enzyme engineering and unlock new avenues for rational protein design.

Engineering Hydroxylase Activity, Selectivity, and Stability for a Scalable Concise Synthesis of a Key Intermediate to Belzutifan

Biocatalytic oxidations are an emerging technology for selective C-H bond activation. While promising for a range of selective oxidations, practical use of enzymes catalyzing aerobic hydroxylation is presently limited by their substrate scope and stability under industrially relevant conditions. Here, we report the engineering and practical application of a non-heme iron and α-ketoglutarate-dependent dioxygenase for the direct stereo- and regio-selective hydroxylation of a non-native fluoroindanone en route to the oncology treatment belzutifan, replacing a five-step chemical synthesis with a direct enantioselective hydroxylation. Mechanistic studies indicated that formation of the desired product was limited by enzyme stability and product overoxidation, with these properties subsequently improved by directed evolution, yielding a biocatalyst capable of >15,000 total turnovers. Highlighting the industrial utility of this biocatalyst, the high-yielding, green, and efficient oxidation was demonstrated at kilogram scale for the synthesis of belzutifan.

Development of antiviral carbon quantum dots that target the Japanese encephalitis virus envelope protein

Japanese encephalitis is a mosquito-borne disease caused by the Japanese encephalitis virus (JEV) that is prevalent in Asia and the Western Pacific. Currently, there is no effective treatment for Japanese encephalitis. Curcumin (Cur) is a compound extracted from the roots of Curcuma longa, and many studies have reported its antiviral and anti-inflammatory activities. However, the high cytotoxicity and very low solubility of Cur limit its biomedical applications. In this study, Cur carbon quantum dots (Cur-CQDs) were synthesized by mild pyrolysis-induced polymerization and carbonization, leading to higher water solubility and lower cytotoxicity, as well as superior antiviral activity against JEV infection. We found that Cur-CQDs effectively bound to the E protein of JEV, preventing viral entry into the host cells. In addition, after continued treatment of JEV with Cur-CQDs, a mutant strain of JEV was evolved that did not support binding of Cur-CQDs to the JEV envelope. Using transmission electron microscopy, biolayer interferometry, and molecular docking analysis, we revealed that the S123R and K312R mutations in the E protein play a key role in binding Cur-CQDs. The S123 and K312 residues are located in structural domains II and III of the E protein, respectively, and are responsible for binding to receptors on and fusing with the cell membrane. Taken together, our results suggest that the E protein of flaviviruses represents a potential target for the development of CQD-based inhibitors to prevent or treat viral infections.

Successes and challenges in multiscale modelling of artificial metalloenzymes: the case study of POP-Rh2 cyclopropanase

Molecular modelling applications in metalloenzyme design are still scarce due to a series of challenges. On top of that, the simulations of metal-mediated binding and the identification of catalytic competent geometries require both large conformational exploration and simulation of fine electronic properties. Here, we demonstrate how the incorporation of new tools in multiscale strategies, namely substrate diffusion exploration, allows taking a step further. As a showcase, the enantioselective profiles of the most outstanding variants of an artificial Rh₂-based cyclopropanase (GSH, HFF and RFY) developed by Lewis and co-workers (Nat. Commun., 2015, 6, 7789 and Nat. Chem., 2018, 10, 318-324) have been rationalized. DFT calculations on the free-cofactor-mediated process identify the carbene insertion and the cyclopropanoid formation as crucial events, the latter being the enantiodetermining step, which displays up to 8 competitive orientations easily altered by the protein environment. The key intermediates of the reaction were docked into the protein scaffold showing that some mutated residues have direct interaction with the cofactor and/or the co-substrate. These interactions take the form of a direct coordination of Rh in GSH and HFF and a strong hydrophobic patch with the carbene moiety in RFY. Posterior molecular dynamics sustain that the cofactor induces global re-arrangements of the protein. Finally, massive exploration of substrate diffusion, based on the GPathFinder approach, defines this event as the origin of the enantioselectivity in GSH and RFY. For HFF, fine molecular dockings suggest that it is likely related to local interactions upon diffusion. This work shows how modelling of long-range mutations on the catalytic profiles of metalloenzymes may be unavoidable and software simulating substrate diffusion should be applied.