Computational Prediction of ω-Transaminase Specificity by a Combination of Docking and Molecular Dynamics Simulations

Sequence-Guided Redesign of an Omega-Transaminase from Bacillus megaterium for the Asymmetric Synthesis of Chiral Amines

ω-Transaminases (ω-TAs) are attractive biocatalysts asymmetrically catalyzing ketones to chiral amines. However, poor non-native catalytic activity and substrate promiscuity severely hamper its wide application in industrial production. Protein engineering efforts have generally focused on reshaping the substrate-binding pockets of ω-TAs. However, hotspots around the substrate tunnel as well as distant sites outside the pockets may also affect its activity. In this study, the ω-TA from Bacillus megaterium (BmeTA) was selected for engineering. The tunnel mutation Y164F synergy with distant mutation A245T which was acquired through a multiple sequence alignment showed improved soluble expression, a 3.7-fold higher specific activity and a 19.9-fold longer half-life at 45 °C. Molecule Dynamics simulation explains the mechanism of improved catalytic activity, enhanced thermostability and improved soluble expression of BmeTAY164F/A245T(2 M). Finally, the resting cells of 2 M were used for biocatalytic processes. 450 mM of S-methoxyisopropylamine (S-MOIPA) was obtained with an ee value of 97.3 % and a conversion rate of 90 %, laying the foundation for its industrial production. Mutant 2 M was also found to be more advantageous in catalyzing the transamination of various ketones. These results demonstrated that sites that are far away from the active center also play an important role in the redesign of ω-TAs.

One substrate many enzymes virtual screening uncovers missing genes of carnitine biosynthesis in human and mouse

The increasing availability of experimental and computational protein structures entices their use for function prediction. Here we develop an automated procedure to identify enzymes involved in metabolic reactions by assessing substrate conformations docked to a library of protein structures. By screening AlphaFold-modeled vitamin B6-dependent enzymes, we find that a metric based on catalytically favorable conformations at the enzyme active site performs best (AUROC Score=0.84) in identifying genes associated with known reactions. Applying this procedure, we identify the mammalian gene encoding hydroxytrimethyllysine aldolase (HTMLA), the second enzyme of carnitine biosynthesis. Upon experimental validation, we find that the top-ranked candidates, serine hydroxymethyl transferase (SHMT) 1 and 2, catalyze the HTMLA reaction. However, a mouse protein absent in humans (threonine aldolase; Tha1) catalyzes the reaction more efficiently. Tha1 did not rank highest based on the AlphaFold model, but its rank improved to second place using the experimental crystal structure we determined at 2.26 Å resolution. Our findings suggest that humans have lost a gene involved in carnitine biosynthesis, with HTMLA activity of SHMT partially compensating for its function.

Rational design of transaminases based on comparative analysis of catalytically active and distance-free modes of the high-energy intermediate state

Bioproduction of chiral amines is limited by low transaminase (TA) activity on nonnatural substrates, leading to the need for protein engineering. To address the challenge of quickly and precisely identifying the engineering targets, a strategy was proposed based on analyzing the mode changes in the high-energy intermediate state (H-state) of the substrate-enzyme complex during catalysis. By substituting the residues with minimal structural changes in catalytically active mode (A-mode) and distance-free mode (F-mode) of the H-state complex with more conserved ones to stabilize it, a TA mutant M5(T295C/L387A/V436A) with 121.9-fold higher activity for synthesizing the (S)-Rivastigmine precursor (S)-1-(3-methoxyphenyl)ethylamine was created. The applicability of this strategy was also validated by engineering another TA for 1.52-fold higher activity and >99% selectivity toward (R)-3-amino-1-butanol biopreparation. The much higher stereoselectivity of the mutant compared with the wild type (28.3%) demonstrated that this strategy is not only advantageous in engineering enzyme activity but also applicable for modulating stereoselectivity.

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.

EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation

Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building "smart libraries" to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.

Molecular basis for short-chain thioester hydrolysis by acyl hydrolase domains in trans -acyltransferase polyketide synthases

Polyketide synthases (PKSs) are multi-domain enzymatic assembly lines that biosynthesise a wide selection of bioactive natural products from simple building blocks. In contrast to their cis -acyltransferase (AT) counterparts, trans -AT PKSs rely on stand-alone AT domains to load extender units onto acyl carrier protein (ACP) domains embedded in the core PKS machinery. Trans -AT PKS gene clusters also encode acyl hydrolase (AH) domains, which are predicted to share the overall fold of AT domains, but hydrolyse aberrant acyl chains from ACP domains, thus ensuring efficient polyketide biosynthesis. How such domains specifically target short acyl chains, in particular acetyl groups, tethered as thioesters to the substrate-shuttling ACP domains, with hydrolytic rather than acyl transfer activity, has remained unclear. To answer these questions, we solved the first structure of an AH domain and performed structure-guided activity assays on active site variants. Our results offer key insights into chain length control and selection against coenzyme A-tethered substrates, and clarify how the interaction interface between AH and ACP domains contributes to recognition of cognate and non-cognate ACP domains. Combining our experimental findings with molecular dynamics simulations allowed for the production of a data-driven model of an AH:ACP domain complex. Our results advance the currently incomplete understanding of polyketide biosynthesis by trans -AT PKSs, and provide foundations for future bioengineering efforts.

Virtual screening of carboxylic acid reductases for biocatalytic synthesis of 6-aminocaproic acid and 1,6-hexamethylenediamine

The key precursors for nylon synthesis, that is, 6-aminocaproic acid (6-ACA) and 1,6-hexamethylenediamine (HMD), are produced from petroleum-based feedstocks. A sustainable biocatalytic alternative method from bio-based adipic acid has been demonstrated recently. However, the low efficiency and specificity of carboxylic acid reductases (CARs) used in the process hampers its further application. Herein, we describe a highly accurate protein structure prediction-based virtual screening method for the discovery of new CARs, which relies on near attack conformation frequency and the Rosetta Energy Score. Through virtual screening and functional detection, five new CARs were selected, each with a broad substrate scope and the highest activities toward various di- and ω-aminated carboxylic acids. Compared with the reported CARs, KiCAR was highly specific with regard to adipic acid without detectable activity to 6-ACA, indicating a potential for 6-ACA biosynthesis. In addition, MabCAR3 had a lower K_m with regard to 6-ACA than the previously validated CAR MAB4714, resulting in twice conversion in the enzymatic cascade synthesis of HMD. The present work highlights the use of structure-based virtual screening for the rapid discovery of pertinent new biocatalysts.

Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks

Finding new enzyme variants with the desired substrate scope requires screening through a large number of potential variants. In a typical in silico enzyme engineering workflow, it is possible to scan a few thousands of variants, and gather several candidates for further screening or experimental verification. In this work, we show that a Graph Convolutional Neural Network (GCN) can be trained to predict the binding energy of combinatorial libraries of enzyme complexes using only sequence information. The GCN model uses a stack of message-passing and graph pooling layers to extract information from the protein input graph and yield a prediction. The GCN model is agnostic to the identity of the ligand, which is kept constant within the mutant libraries. Using a miniscule subset of the total combinatorial space (20⁴-20⁸ mutants) as training data, the proposed GCN model achieves a high accuracy in predicting the binding energy of unseen variants. The network's accuracy was further improved by injecting feature embeddings obtained from a language module pretrained on 10 million protein sequences. Since no structural information is needed to evaluate new variants, the deep learning algorithm is capable of scoring an enzyme variant in under 1 ms, allowing the search of billions of candidates on a single GPU.

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

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.