Asymmetric dearomative [3 + 2] cycloaddition reactions of 3-nitroindoles with vinyl aziridine and vinyl cyclopropanes have been respectively successfully developed in the presence of a chiral box/Pd(0) complex. A series of enantiomerically enriched 3a-nitro-hexahydropyrrolo[2,3-b]indole and 8b-nitrohexahydrocyclopenta[b]indole derivatives containing three contiguous chiral centers are smoothly obtained in high yields with satisfactory regio-, chemo-, and enantioselectivity. Remarkably, the synthetic utility of this process was demonstrated through direct reductive amination and functionalization of the carbon-carbon double bond of the desired products.

The synthesis of open‐shell polycyclic hydrocarbons with large diradical characters is challenging because of their high reactivities. Herein, two diindeno‐fused corannulene regioisomers DIC‐1 and DIC‐2, curved fragments of fullerene C104, were synthesized that exhibit open‐shell singlet ground states. The incorporation of the curved and non‐alternant corannulene moiety within diradical systems leads to significant diradical characters as high as 0.98 for DIC‐1 and 0.89 for DIC‐2. Such high diradical characters can presumably be ascribed to the re‐aromatization of the corannulene π system. Although the DIC compounds have large diradical characters, they display excellent stability under ambient conditions. The half‐lives are 37 days for DIC‐1 and 6.6 days for DIC‐2 in solution. This work offers a new design strategy towards diradicaloids with large diradical characters yet maintain high stability.

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date.