Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes

Computational design of carboxylase for the synthesis of 4-hydroxyisophthalic acid from p-hydroxybenzoic acid by fixing CO2

Carbon dioxide (CO2) emissions constitute the primary contribution to global climate change. Synthetic CO2 fixation represents an exceptionally appealing and sustainable method for carbon neutralization. Unlike the limitations of chemical catalysis, biological CO2 fixation displays high selectivity and the ability to operate under mild conditions. The superfamily of amidohydrolases has demonstrated the ability to synthesize a range of aromatic monocarboxylic acids. However, there is a scarcity of reported carboxylases capable of synthesizing aromatic dicarboxylic acids. Among these, 4-hydroxyisophthalic acid holds significant potential for applications across various fields, yet no enzyme has been reported for its synthesis. In this study, we developed for the first time that exhibits starting activity in fixing CO2 to synthesize 4-hydroxyisophthalic acid. Furthermore, we have devised a computational strategy that effectively enhances the catalytic activity of this enzyme. A focused library comprising only 13 variants was generated. Experimental validation confirmed a threefold improvement in the carboxylation activity of the optimal variant (L47M). The computational enzyme design strategy proposed in this paper demonstrates broad applicability in developing carboxylases for synthesizing other aromatic dicarboxylic acids. This lays the groundwork for leveraging biocatalysis in industrial synthesis for CO2 fixation.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Elucidating the Enzymatic Mechanism of Dihydrocoumarin Degradation: Insight into the Functional Evolution of Methyl-Parathion Hydrolase from QM/MM and MM MD Simulations

Methyl-parathion hydrolase (MPH), which evolved from dihydrocoumarin hydrolase, offers one of the most efficient enzymes for the hydrolysis of methyl-parathion. Interestingly, the substrate preference of MPH shifts from the methyl-parathion to the lactone dihydrocoumarin (DHC) after its mutation of five specific residues (R72L, L273F, L258H, T271I, and S193Δ, m5-MPH). Here, extensive QM/MM calculations and MM MD simulations have been used to delve into the structure-function relationship of MPH enzymes and plausible mechanisms for the chemical and nonchemical steps, including the transportation and binding of the substrate DHC to the active site, the hydrolysis reaction, and the product release. The results reveal that the five mutations remodel the active pocket and reposition DHC within the active site, leading to stronger enzyme-substrate interactions. The MM/GBSA-estimated binding free energies are about -20.7 kcal/mol for m5-MPH and -17.1 kcal/mol for wild-type MPH. Furthermore, this conformational adjustment of the protein may facilitate the chemical step of DHC hydrolysis and the product release, although there is a certain influence on the substrate transport. The hydrolytic reaction begins with the nucleophilic attack of the bridging OH- with the energy barriers of 22.0 and 18.0 kcal/mol for the wild-type and m5-MPH enzymes, respectively, which is rate-determining for the entire process. Unraveling these mechanistic intricacies may help in the understanding of the natural evolution of enzymes for diverse substrates and establish the enzyme structure-function relationship.

Substrate Positioning Dynamics Involves a Non-Electrostatic Component to Mediate Catalysis

Substrate positioning dynamics (SPD) orients the substrate in the active site, thereby influencing catalytic efficiency. However, it remains unknown whether SPD effects originate primarily from electrostatic perturbation inside the enzyme or can independently mediate catalysis with a significant non-electrostatic component. In this work, we investigated how the non-electrostatic component of SPD affects transition state (TS) stabilization. Using high-throughput enzyme modeling, we selected Kemp eliminase variants with similar electrostatics inside the enzyme but significantly different SPD. The kinetic parameters of these mutants were experimentally characterized. We observed a valley-shaped, two-segment linear correlation between the TS stabilization free energy (converted from kinetic parameters) and substrate positioning index (a metric to quantify SPD). The energy varies by approximately 2 kcal/mol. Favorable SPD was observed for the distal mutant R154W, increasing the proportion of reactive conformations and leading to the lowest activation free energy. These results indicate the substantial contribution of the non-electrostatic component of SPD to enzyme catalytic efficiency.

Computational design of highly efficient thermostable MHET hydrolases and dual enzyme system for PET recycling

Recently developed enzymes for the depolymerization of polyethylene terephthalate (PET) such as FAST-PETase and LCC-ICCG are inhibited by the intermediate PET product mono(2-hydroxyethyl) terephthalate (MHET). Consequently, the conversion of PET enzymatically into its constituent monomers terephthalic acid (TPA) and ethylene glycol (EG) is inefficient. In this study, a protein scaffold (1TQH) corresponding to a thermophilic carboxylesterase (Est30) was selected from the structural database and redesigned in silico. Among designs, a double variant KL-MHETase (I171K/G130L) with a similar protein melting temperature (67.58 °C) to that of the PET hydrolase FAST-PETase (67.80 °C) exhibited a 67-fold higher activity for MHET hydrolysis than FAST-PETase. A fused dual enzyme system comprising KL-MHETase and FAST-PETase exhibited a 2.6-fold faster PET depolymerization rate than FAST-PETase alone. Synergy increased the yield of TPA by 1.64 fold, and its purity in the released aromatic products reached 99.5%. In large reaction systems with 100 g/L substrate concentrations, the dual enzyme system KL36F achieved over 90% PET depolymerization into monomers, demonstrating its potential applicability in the industrial recycling of PET plastics. Therefore, a dual enzyme system can greatly reduce the reaction and separation cost for sustainable enzymatic PET recycling.

Molecular dynamics simulations identify the topological weak spots of a protease CN2S8A

Thermophilic enzymes are highly desired in industrial applications due to their efficient catalytic activity at high temperature. However, most enzymes exhibit inferior thermostability and it remains challenging to identify the optimal sites for designing mutations to improve protein stability. To tackle this issue, we integrated topological analysis and all-atom molecular dynamics simulations to efficiently pinpoint the thermally-unstable regions in protein structures. Using a protease CN2S8A as the model, we analyzed the intramolecular hydrogen bonding interactions between adjacent secondary structure elements, and then identified the topological weak spots of CN2S8A where weak hydrogen bonding interactions were formed. To examine the role of these sites in protein structural stability, we designed three virtual mutations at different weak spots and characterized the effects of these mutations on the structural properties of CN2S8A. The results showed that all three mutations increased the protein structural stability. In conclusion, these findings provide a novel method to identify the topological weak spots of proteins, with implications in the rational design of biocatalysts with superior thermostability.