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We report transition metal catalysis using novel chiral metal-chelating ligands featuring a silanol coordinating group and peptide-like aminoamide scaffold. The catalytic properties of the silanol ligand are demonstrated through an enantioselective Cu-catalyzed N–H insertion affording unnatural amino acid derivatives in high selectivity. Our investigations into the silanol coordination mode include DFT calculations, ligand structure investigations, and X-ray structure analyses, which support the formation of an H-bond stabilized silanol-chelating copper carbenoid complex. A p–p stacking interaction revealed by DFT calculations is proposed to enable selectivity for aryl diazoacetate substrates, overcoming some of the traditional limitations of using these substrates. 

De novo design of protein catalysts with high efficiency and stereoselectivity provides an attractive approach toward the design of environmentally benign catalysts. Here, we design proteins that incorporate histidine-ligated synthetic porphyrin and heme ligands. Four of 10 designed proteins catalyzed cyclopropanation with an enantiomeric ratio greater than 99:1. A second class of proteins were designed to catalyze a silicon-hydrogen insertion and were optimized by directed evolution in whole cells. The evolved proteins incorporated features unlikely to be generated by computational design alone, including a proline in an α helix. Molecular dynamics simulations showed that as the proteins evolved toward higher activity, their conformational ensembles narrowed to favor more productive conformations. Our work demonstrates that efficient de novo protein catalysts are designable and should be useful for manifold chemical processes. 

Abstract A three‐component coupling approach toward structurally complex dialkylsulfides is described via the nickel‐catalyzed 1,2‐carbosulfenylation of unactivated alkenes with organoboron nucleophiles and alkylsulfenamide (N−S) electrophiles. Efficient catalytic turnover is facilitated using a tailored N−S electrophile containing anN‐methyl methanesulfonamide leaving group, allowing catalyst loadings as low as 1 mol %. Regioselectivity is controlled by a collection of monodentate, weakly coordinating native directing groups, including sulfonamides, amides, sulfinamides, phosphoramides, and carbamates. Key to the development of this transformation is the identification of quinones as a family of hemilabile and redox‐active ligands that tune the steric and electronic properties of the metal throughout the catalytic cycle. Density functional theory (DFT) results show that the duroquinone (DQ) ligand adopts different coordination modes in different stages of the Ni‐catalyzed 1,2‐carbosulfenylation‐binding as an η⁶capping ligand to stabilize the precatalyst/resting state and prevent catalyst decomposition, binding as an X‐type redox‐active durosemiquinone radical anion to promote alkene migratory insertion with a less distorted square planar Ni(II) center, and binding as an L‐type ligand to promote N−S oxidative addition at a relatively more electron‐rich Ni(I) center. 

Abstract AQME, automated quantum mechanical environments, is a free and open‐source Python package for the rapid deployment of automated workflows using cheminformatics and quantum chemistry. AQME workflows integrate tasks performed across multiple computational chemistry packages and data formats, preserving all computational protocols, data, and metadata for machine and human users to access and reuse. AQME has a modular structure of independent modules that can be implemented in any sequence, allowing the users to use all or only the desired parts of the program. The code has been developed for researchers with basic familiarity with the Python programming language. The CSEARCH module interfaces to molecular mechanics and semi‐empirical QM (SQM) conformer generation tools (e.g., RDKit and Conformer–Rotamer Ensemble Sampling Tool, CREST) starting from various initial structure formats. The CMIN module enables geometry refinement with SQM and neural network potentials, such as ANI. The QPREP module interfaces with multiple QM programs, such as Gaussian, ORCA, and PySCF. The QCORR module processes QM results, storing structural, energetic, and property data while also enabling automated error handling (i.e., convergence errors, wrong number of imaginary frequencies, isomerization, etc.) and job resubmission. The QDESCP module provides easy access to QM ensemble‐averaged molecular descriptors and computed properties, such as NMR spectra. Overall, AQME provides automated, transparent, and reproducible workflows to produce, analyze and archive computational chemistry results. SMILES inputs can be used, and many aspects of tedious human manipulation can be avoided. Installation and execution on Windows, macOS, and Linux platforms have been tested, and the code has been developed to support access through Jupyter Notebooks, the command line, and job submission (e.g., Slurm) scripts. Examples of pre‐configured workflows are available in various formats, and hands‐on video tutorials illustrate their use. This article is categorized under:Data Science > ChemoinformaticsData Science > Computer Algorithms and ProgrammingSoftware > Quantum Chemistry