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Nuclear magnetic resonance (NMR) is one of the primary techniques used to elucidate the chemical structure, bonding, stereochemistry, and conformation of organic compounds. The distinct chemical shifts in an NMR spectrum depend upon each atom's local chemical environment and are influenced by both through-bond and through-space interactions with other atoms and functional groups. The in silico prediction of NMR chemical shifts using quantum mechanical (QM) calculations is now commonplace in aiding organic structural assignment since spectra can be computed for several candidate structures and then compared with experimental values to find the best possible match. However, the computational demands of calculating multiple structural- and stereo-isomers, each of which may typically exist as an ensemble of rapidly-interconverting conformations, are expensive. Additionally, the QM predictions themselves may lack sufficient accuracy to identify a correct structure. In this work, we address both of these shortcomings by developing a rapid machine learning (ML) protocol to predict 1 H and 13 C chemical shifts through an efficient graph neural network (GNN) using 3D structures as input. Transfer learning with experimental data is used to improve the final prediction accuracy of a model trained using QM calculations. When tested on the CHESHIRE dataset, the proposed model predicts observed 13 C chemical shifts with comparable accuracy to the best-performing DFT functionals (1.5 ppm) in around 1/6000 of the CPU time. An automated prediction webserver and graphical interface are accessible online at http://nova.chem.colostate.edu/cascade/. We further demonstrate the model in three applications: first, we use the model to decide the correct organic structure from candidates through experimental spectra, including complex stereoisomers; second, we automatically detect and revise incorrect chemical shift assignments in a popular NMR database, the NMRShiftDB; and third, we use NMR chemical shifts as descriptors for determination of the sites of electrophilic aromatic substitution. 

Abstract We report that O‐selective arylation of 2‐ and 4‐pyridones with arylboronic acids is affected by a modular, bismacycle‐based system. The utility of this umpolung approach to pyridyl ethers, which is complementary to conventional methods based on S_NAr or cross‐coupling, is demonstrated through the concise synthesis of Ki6783 and picolinafen, and the formal synthesis of cabozantib and golvatinib. Computational investigations reveal that arylation proceeds in a concerted fashion via a 5‐membered transition state. The kinetically‐controlled regioselectivity for O‐arylation—which is reversed relative to previous Bi^V‐mediated pyridone arylations—is attributed primarily to the geometric constraints imposed by the bismacyclic scaffold. 

Abstract The importance of modified peptides and proteins for applications in drug discovery, and for illuminating biological processes at the molecular level, is fueling a demand for efficient methods that facilitate the precise modification of these biomolecules. Herein, we describe the development of a photocatalytic method for the rapid and efficient dimerization and site-specific functionalization of peptide and protein diselenides. This methodology, dubbed the photocatalytic diselenide contraction, involves irradiation at 450 nm in the presence of an iridium photocatalyst and a phosphine and results in rapid and clean conversion of diselenides to reductively stable selenoethers. A mechanism for this photocatalytic transformation is proposed, which is supported by photoluminescence spectroscopy and density functional theory calculations. The utility of the photocatalytic diselenide contraction transformation is highlighted through the dimerization of selenopeptides, and by the generation of two families of protein conjugates via the site-selective modification of calmodulin containing the 21^stamino acid selenocysteine, and the C-terminal modification of a ubiquitin diselenide.