Intercepting fleeting cyclic allenes with asymmetric nickel catalysis.

Strained cyclic organic molecules, such as arynes, cyclic alkynes and cyclic allenes, have intrigued chemists for more than a century with their unusual structures and high chemical reactivity1. The considerable ring strain (30-50 kilocalories per mole)2,3 that characterizes these transient intermediates imparts high reactivity in many reactions, including cycloadditions and nucleophilic trappings, often generating structurally complex products4. Although strategies to control absolute stereochemistry in these reactions have been reported using stoichiometric chiral reagents5,6, catalytic asymmetric variants to generate enantioenriched products have remained difficult to achieve. Here we report the interception of racemic cyclic allene intermediates in a catalytic asymmetric reaction and provide evidence for two distinct mechanisms that control absolute stereochemistry in such transformations: kinetic differentiation of allene enantiomers and desymmetrization of intermediate π-allylnickel complexes. Computational studies implicate a catalytic mechanism involving initial kinetic differentiation of the cyclic allene enantiomers through stereoselective olefin insertion, loss of the resultant stereochemical information, and subsequent introduction of absolute stereochemistry through desymmetrization of an intermediate π-allylnickel complex. These results reveal reactivity that is available to cyclic allenes beyond the traditional cycloadditions and nucleophilic trappings previously reported, thus expanding the types of product accessible from this class of intermediates. Additionally, our computational studies suggest two potential strategies for stereocontrol in reactions of cyclic allenes. Combined, these results lay the foundation for the development of catalytic asymmetric reactions involving these classically avoided strained intermediates.

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.

LassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling.

Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides with a slipknot conformation. With superior thermal stability, protease resistance, and antimicrobial activity, lasso peptides are promising candidates for bioengineering and pharmaceutical applications. To enable high-throughput computational prediction and design of lasso peptides, we developed a software, LassoHTP, for automatic lasso peptide structure construction and modeling. LassoHTP consists of three modules, including the scaffold constructor, mutant generator, and molecular dynamics (MD) simulator. With a user-provided sequence and conformational annotation, LassoHTP can either generate the structure and conformational ensemble as is or conduct random mutagenesis. We used LassoHTP to construct eight known lasso peptide structures de novo and to simulate their conformational ensembles for 100 ns MD simulations. For benchmarking, we calculated the root mean square deviation (RMSD) of these ensembles with reference to their experimental crystal or NMR PDB structures; we also compared these RMSD values against those of the MD ensembles that are initiated from the PDB structures. Dihedral principal component analysis was also conducted. The results show that the LassoHTP-initiated ensembles are similar to those of the PDB-initiated ensembles. LassoHTP offers a computational platform to develop strategies for lasso peptide prediction and design.

EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling.

Molecular simulations, including quantum mechanics (QM), molecular mechanics (MM), and multiscale QM/MM modeling, have been extensively applied to understand the mechanism of enzyme catalysis and to design new enzymes. However, molecular simulations typically require specialized, manual operation ranging from model construction to data analysis to complete the entire life cycle of enzyme modeling. The dependence on manual operation makes it challenging to simulate enzymes and enzyme variants in a high-throughput fashion. In this work, we developed a Python software, EnzyHTP, to automate molecular model construction, QM, MM, and QM/MM computation, and analyses of modeling data for enzyme simulations. To test the EnzyHTP, we used fluoroacetate dehalogenase (FAcD) as a model system and simulated the enzyme interior electrostatics for 100 FAcD mutants with a random single amino acid substitution. For each enzyme mutant, the workflow involves structural model construction, 1 ns molecular dynamics (MD) simulations, and quantum mechanical calculations in 100 MD-sampled snapshots. The entire simulation workflow for 100 mutants was completed in 7 h with 10 GPUs and 160 CPUs. EnzyHTP improves the efficiency of computational enzyme modeling, setting a basis for high-throughput identification of function-enhancing enzymes and enzyme variants. The software is expected to facilitate the fundamental understanding of catalytic origins across enzyme families and to accelerate the optimization of biocatalysts for non-native substrates.

Metal-Free C-H Functionalization via Diaryliodonium Salts with a Chemically Robust Dummy Ligand.

A two-step strategy for the transition-metal-free C-H functionalization of arenes using unsymmetrical iodonium salts as versatile synthetic linchpins is presented. The key to the success of this strategy is the identification of the 3,5-dimethyl-4-isoxazolyl (DMIX) group as a superior dummy ligand, which enables not only site-selective C-H functionalization to afford unsymmetrical iodonium salts, but also highly selective aryl transfer during the subsequent metal-free coupling reaction. Both electron-rich and moderately electron-deficient arenes can be converted into the iodonium salts through C-H functionalization, allowing for diverse structural elaboration by metal-free C-N, C-C, C-S, and C-O coupling.

High Site Selectivity in Electrophilic Aromatic Substitutions: Mechanism of C-H Thianthrenation.

The introduction of thianthrene as a linchpin has proven to be a versatile strategy for the C-H functionalization of aromatic compounds, featuring a broad scope and fast diversification. The synthesis of aryl thianthrenium salts has displayed an unusually high para regioselectivity, notably superior to those observed in halogenation or borylation reactions for various substrates. We report an experimental and computational study on the mechanism of aromatic C-H thianthrenation reactions, with an emphasis on the elucidation of the reactive species and the nature of the exquisite site selectivity. Mechanisms involving a direct attack of arene to the isolated O-trifluoracetylthianthrene S-oxide (TT+-TFA) or to the thianthrene dication (TT2+) via electron transfer under acidic conditions are identified. A reversible interconversion of the different Wheland-type intermediates before a subsequent, irreversible deprotonation is proposed to be responsible for the exceptional para selectivity of the reaction.

Development of α,α-Disubstituted Crotylboronate Reagents and Stereoselective Crotylation via Brønsted or Lewis Acid Catalysis.

The development of α,α-disubstituted crotylboronate reagents is reported. Chiral Brønsted acid-catalyzed asymmetric aldehyde addition with the developed E-crotylboron reagent gave (E)-anti-1,2-oxaborinan-3-enes with excellent enantioselectivities and E-selectivities. With BF3·OEt2 catalysis, the stereoselectivity is reversed, and (Z)-δ-boryl-anti-homoallylic alcohols are obtained with excellent Z-selectivities from the same E-crotylboron reagent. The Z-crotylboron reagent also participates in BF3·OEt2-catalyzed crotylation to furnish (Z)-δ-boryl-syn-homoallylic alcohols with good Z-selectivities. DFT computations establish the origins of observed enantio- and stereoselectivities of chiral Brønsted acid-catalyzed asymmetric allylation. Stereochemical models for BF3·OEt2-catalyzed reactions are proposed to rationalize the Z-selective allyl additions. These reactions generate highly valuable homoallylic alcohol products with a stereodefined trisubstituted alkene unit. The synthetic utility is further demonstrated by the total syntheses of salinipyrones A and B.

Bioinspired Synthesis of (-)-PF-1018.

The combination of electrocyclizations and cycloadditions accounts for the formation of a range of fascinating natural products. Cascades consisting of 8π electrocyclizations followed by a 6π electrocyclization and a cycloaddition are relatively common. We now report the synthesis of the tetramic acid PF-1018 through an 8π electrocyclization, the product of which is immediately intercepted by a Diels-Alder cycloaddition. The success of this pericyclic cascade was critically dependent on the substitution pattern of the starting polyene and could be rationalized through DFT calculations. The completion of the synthesis required the instalment of a trisubstituted double bond by radical deoxygenation. An unexpected side product formed through 4-exo-trig radical cyclization could be recycled through an unprecedented triflation/fragmentation.

Why •CF2H is nucleophilic but •CF3 is electrophilic in reactions with heterocycles.

Radical substitution is a useful method to functionalize heterocycles, as in the venerable Minisci reaction. Empirically observed regiochemistries indicate that the CF2H radical has a nucleophilic character similar to alkyl radicals, but the CF3 radical is electrophilic. While the difference between •CH3 and •CF3 is well understood, the reason that one and two Fs make little difference but the third has a large effect is puzzling. DFT calculations with M06-2X both reproduce experimental selectivities and also lead to an explanation of this difference. Theoretical methods reveal how the F inductive withdrawal and conjugative donation alter radical properties, but only CF3 becomes decidedly electrophilic toward heterocycles. Here, we show a simple model to explain the radical orbital energy trends and resulting nucleophilicity or electrophilicity of fluorinated radicals.

Ligand efficacy shifts a nuclear receptor conformational ensemble between transcriptionally active and repressive states.

Nuclear receptors (NRs) are thought to dynamically alternate between transcriptionally active and repressive conformations, which are stabilized upon ligand binding. Most NR ligand series exhibit limited bias, primarily consisting of transcriptionally active agonists or neutral antagonists, but not repressive inverse agonists-a limitation that restricts understanding of the functional NR conformational ensemble. Here, we report a NR ligand series for peroxisome proliferator-activated receptor gamma (PPARγ) that spans a pharmacological spectrum from repression (inverse agonism) to activation (agonism) where subtle structural modifications switch compound activity. While crystal structures provide snapshots of the fully repressive state, NMR spectroscopy and conformation-activity relationship analysis reveals that compounds within the series shift the PPARγ conformational ensemble between transcriptionally active and repressive conformations that are populated in the apo/ligand-free ensemble. Our findings reveal a molecular framework for minimal chemical modifications that enhance PPARγ inverse agonism and elucidate their influence on the dynamic PPARγ conformational ensemble.

EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution.

Hydrolase-catalyzed kinetic resolution is a well-established biocatalytic process. However, the computational tools that predict favorable enzyme scaffolds for separating a racemic substrate mixture are underdeveloped. To address this challenge, we trained a deep learning framework, EnzyKR, to automate the selection of hydrolases for stereoselective biocatalysis. EnzyKR adopts a classifier-regressor architecture that first identifies the reactive binding conformer of a substrate-hydrolase complex, and then predicts its activation free energy. A structure-based encoding strategy was used to depict the chiral interactions between hydrolases and enantiomers. Different from existing models trained on protein sequences and substrate SMILES strings, EnzyKR was trained using 204 substrate-hydrolase complexes, which were constructed by docking. EnzyKR was tested using a held-out dataset of 20 complexes on the task of predicting activation free energy. EnzyKR achieved a Pearson correlation coefficient (R) of 0.72, a Spearman rank correlation coefficient (Spearman R) of 0.72, and a mean absolute error (MAE) of 1.54 kcal mol-1 in this task. Furthermore, EnzyKR was tested on the task of predicting enantiomeric excess ratios for 28 hydrolytic kinetic resolution reactions catalyzed by fluoroacetate dehalogenase RPA1163, halohydrin HheC, A. mediolanus epoxide hydrolase, and P. fluorescens esterase. The performance of EnzyKR was compared against that of a recently developed kinetic predictor, DLKcat. EnzyKR correctly predicts the favored enantiomer and outperforms DLKcat in 18 out of 28 reactions, occupying 64% of the test cases. These results demonstrate EnzyKR to be a new approach for prediction of enantiomeric outcomes in hydrolase-catalyzed kinetic resolution reactions.

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.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Convergence in determining enzyme functional descriptors across Kemp eliminase variants.

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on 18 Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field (EF) on the breaking C-H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the EF was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for 18 Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSDactive_site and SASAratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EFC-H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

Highly selective synthesis of all-carbon tetrasubstituted alkenes by deoxygenative alkenylation of carboxylic acids.

The synthesis of all-carbon tetrasubstituted olefins under mild reaction conditions is challenging because of the inevitable issues including significant steric hindrance and the uncontrolled Z/E stereoselectivity. In this paper, we report the synthesis of all-carbon tetrasubstituted alkenes from readily available carboxylic acids and alkenyl triflates with the synergistic catalysis of cyclo-octa-1,5-diene(tetramethyl-1,4-benzoquinone)nickel and visible light under an air atmosphere, thus avoiding the need for a glovebox or a Schlenk line. A wide range of aromatic carboxylic acids and cyclic and acyclic alkenyl triflates undergo the C-C coupling process smoothly, forming structurally diverse alkenes stereospecifically in moderate to good yields. The practicality of the method is further illustrated by the late-stage modification of complex molecules, the one pot synthesis and gram-scale applications. This is an important step towards the valuable utilization of carboxylic acids, and it also simplifies the experimental operation of metallophotoredox catalysis with moisture sensitive nickel(0) catalysis.

Rate-Perturbing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling.

Hydrolases are a critical component for modern chemical, pharmaceutical, and environmental sciences. Identifying mutations that enhance catalytic efficiency presents a roadblock to design and to discover new hydrolases for broad academic and industrial uses. Here, we report the statistical profiling for rate-perturbing mutant hydrolases with a single amino acid substitution. We constructed an integrated structure-kinetics database for hydrolases, IntEnzyDB, which contains 3907 kcats, 4175 KMs, and 2715 Protein Data Bank IDs. IntEnzyDB adopts a relational architecture with a flattened data structure, enabling facile and efficient access to clean and tabulated data for machine learning uses. We conducted statistical analyses on how single amino acids mutations influence the turnover number (i.e., kcat) and efficiency (i.e., kcat/KM), with a particular emphasis on profiling the features for rate-enhancing mutations. The results show that mutation to bulky nonpolar residues with a hydrocarbon chain involves a higher likelihood for rate acceleration than to other types of residues. Linear regression models reveal geometric descriptors of substrate and mutation residues that mediate rate-perturbing outcomes for hydrolases with bulky nonpolar mutations. On the basis of the analyses of the structure-kinetics relationship, we observe that the propensity for rate enhancement is independent of protein sizes. In addition, we observe that distal mutations (i.e., >10 Å from the active site) in hydrolases are significantly more prone to induce efficiency neutrality and avoid efficiency deletion but involve similar propensity for rate enhancement. The studies reveal the statistical features for identifying rate-enhancing mutations in hydrolases, which will potentially guide hydrolase discovery in biocatalysis.