Site-selective photocatalytic functionalization of peptides and proteins at selenocysteine.

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 21st amino acid selenocysteine, and the C-terminal modification of a ubiquitin diselenide.

Umpolung Synthesis of Pyridyl Ethers by BiV -Mediated O-Arylation of Pyridones.

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 SN Ar 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 BiV -mediated pyridone arylations-is attributed primarily to the geometric constraints imposed by the bismacyclic scaffold.

Mechanistic Studies Yield Improved Protocols for Base-Catalyzed Anti-Markovnikov Alcohol Addition Reactions.

The catalytic anti-Markovnikov addition of alcohols to simple alkenes is a longstanding synthetic challenge. We recently disclosed the use of organic superbase catalysis for the nucleophilic addition of alcohols to activated styrene derivatives. This article describes mechanistic studies on this reversible reaction, including thermodynamic and kinetic profiling as well as computational modeling. Our findings show the negative entropy of addition is counterbalanced by an enthalpy that is most favored in nonpolar solvents. However, a large negative alcohol rate order under these conditions indicates excess alcohol sequesters the active alkoxide ion pairs, slowing the reaction rate. These observations led to an unexpected solution to a thermodynamically challenging reaction: use of less alcohol enables faster addition, which in turn allows for lower reaction temperatures to counteract Le Chatelier's principle. Thus, our original method has been improved with new protocols that do not require excess alcohol stoichiometry, enable an expanded alkene substrate scope, and allow for the use of more practical catalyst systems. The generality of this insight for other challenging hydroetherification reactions is also demonstrated through new alkenol cyclization and oxa-Michael addition reactions.

Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network.

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 1H and 13C 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 13C 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.

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties.

Machine-readable chemical structure representations are foundational in all attempts to harness machine learning for the prediction of reactivities, selectivities, and chemical properties directly from molecular structure. The featurization of discrete chemical structures into a continuous vector space is a critical phase undertaken before model selection, and the development of new ways to quantitatively encode molecules is an active area of research. In this Account, we highlight the application and suitability of different representations, from expert-guided "engineered" descriptors to automatically "learned" features, in different prediction tasks relevant to organic and organometallic chemistry, where differing amounts of training data are available. These tasks include statistical models of stereo- and enantioselectivity, thermochemistry, and kinetics developed using experimental and quantum chemical data.The use of expert-guided molecular descriptors provides an opportunity to incorporate chemical knowledge, domain expertise, and physical constraints into statistical modeling. In applications to stereoselective organic and organometallic catalysis, where data sets may be relatively small and 3D-geometries and conformations play an important role, mechanistically informed features can be used successfully to obtain predictive statistical models that are also chemically interpretable. We provide an overview of several recent applications of this approach to obtain quantitative models for reactivity and selectivity, where topological descriptors, quantum mechanical calculations of electronic and steric properties, along with conformational ensembles, all feature as essential ingredients of the molecular representations used.Alternatively, more flexible, general-purpose molecular representations such as attributed molecular graphs can be used with machine learning approaches to learn the complex relationship between a structure and prediction target. This approach has the potential to out-perform more traditional representation methods such as "hand-crafted" molecular descriptors, particularly as data set sizes grow. One area where this is particularly relevant is in the use of large sets of quantum mechanical data to train quantitative structure-property relationships. A general approach toward curating useful data sets and training highly accurate graph neural network models is discussed in the context of organic bond dissociation enthalpies, where this strategy outperforms regression using precomputed descriptors.Finally, we describe how graph neural network predictions can be incorporated into mechanistically informed statistical models of chemical reactivity and selectivity. Once trained, this approach avoids the expensive computational overhead associated with quantum mechanical calculations, while maintaining chemical interpretability. We illustrate examples for which fast predictions of bond dissociation enthalpy and of the identities of radicals formed through cleavage of a molecule's weakest bond are used in simple physical models of site-selectivity and reactivity.