No abstract

Three anti-HIV drugs, ritonavir, lopinavir and darunavir, might have therapeutic effect on coronavirus disease 2019 . In this study, the structure models of two severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) proteases, coronavirus endopeptidase C30 (CEP_C30) and papain like viral protease (PLVP), were built by homology modeling. Ritonavir, lopinavir and darunavir were then docked to the models, respectively, followed by energy minimization of the protease-drug complexes. In the simulations, ritonavir can bind to CEP_C30 most suitably, and induce significant conformation changes of CEP_C30; lopinavir can also bind to CEP_C30 suitably, and induce significant conformation changes of CEP_C30; darunavir can bind to PLVP suitably with slight conformation changes of PLVP. It is suggested that the therapeutic effect of ritonavir and lopinavir on COVID-19 may be mainly due to their inhibitory effect on CEP_C30, while ritonavir may have stronger efficacy; the inhibitory effect of darunavir on SARS-CoV-2 and its potential therapeutic effect may be mainly due to its inhibitory effect on PLVP.

Kirigami, with facile and automated fashion of three-dimensional (3D) transformations, offers an unconventional approach for realizing cutting-edge optical nano-electromechanical systems. Here, we demonstrate an on-chip and electromechanically reconfigurable nano-kirigami with optical functionalities. The nano-electromechanical system is built on an Au/SiO2/Si substrate and operated via attractive electrostatic forces between the top gold nanostructure and bottom silicon substrate. Large-range nano-kirigami like 3D deformations are clearly observed and reversibly engineered, with scalable pitch size down to 0.975 μm. Broadband nonresonant and narrowband resonant optical reconfigurations are achieved at visible and near-infrared wavelengths, respectively, with a high modulation contrast up to 494%. On-chip modulation of optical helicity is further demonstrated in submicron nano-kirigami at near-infrared wavelengths. Such small-size and high-contrast reconfigurable optical nano-kirigami provides advanced methodologies and platforms for versatile on-chip manipulation of light at nanoscale.

Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural features and ensure reasonable prediction accuracy. Even though large datasets are becoming the new normal in drug discovery, especially when it comes to high-throughput screening or metabolomics datasets, one should also consider smaller datasets with challenging endpoints to model and forecast. Thus, it would be highly relevant to better utilize the tremendous compendium of unlabeled compounds from publicly-available datasets for improving the model performances for the user's particular series of compounds. In this study, we propose the Molecular Prediction Model Fine-Tuning (MolPMoFiT) approach, an effective transfer learning method based on self-supervised pre-training + task-specific fine-tuning for QSPR/QSAR modeling. A large-scale molecular structure prediction model is pre-trained using one million unlabeled molecules from ChEMBL in a self-supervised learning manner, and can then be fine-tuned on various QSPR/QSAR tasks for smaller chemical datasets with specific endpoints. Herein, the method is evaluated on four benchmark datasets (lipophilicity, FreeSolv, HIV, and blood-brain barrier penetration). The results showed the method can achieve strong performances for all four datasets compared to other state-of-the-art machine learning modeling techniques reported in the literature so far.

Ergothioneine, a natural longevity vitamin and antioxidant, is a thiol-histidine derivative. Recently, two types of biosynthetic pathways were reported. In the aerobic ergothioneine biosyntheses, non-heme iron enzymes incorporate a sulfoxide into an sp 2 C−H bond from trimethyl-histidine (hercynine) through oxidation reactions. In contrast, in the anaerobic ergothioneine biosynthetic pathway in a green-sulfur bacterium, Chlorobium limicola, a rhodanese domain containing protein (EanB), directly replaces this unreactive hercynine C−H bond with a C−S bond. Herein, we demonstrate that polysulfide (HSS n SR) is the direct sulfur source in EanB catalysis. After identifying EanB's substrates, X-ray crystallography of several intermediate states along with mass spectrometry results provide additional mechanistic details for this reaction. Further, quantum mechanics/molecular mechanics (QM/MM) calculations reveal that the protonation of N π of hercynine by Tyr353 with the assistance of Thr414 is a key activation step for the hercynine sp 2 C−H bond in this trans-sulfuration reaction.