Tianjin Institute of Pharmaceutical Research Co., Ltd.

The COVID-19 pandemic has significantly strained global health infrastructures while profoundly affecting the socio-economic landscape. RNA-dependent RNA polymerase (RdRp) plays a pivotal role in the replication and transcription of RNA viruses, making it a critical target for antiviral drug development. In this work, we describe the discovery, rational optimization, and synthesis of a novel series of non-nucleoside SARS-CoV-2 RdRp inhibitors featuring a 2,2'-((1H-indole-2,3-diyl)bis (thio))diacetamide core. The inhibitory activity of these compounds was evaluated, with most demonstrating a higher inhibitory effect than Remdesivir. Notably, the most potent candidates suppressed RNA synthesis dose-dependently and exhibited greater resistance to nsp14/nsp10 exonuclease-mediated proofreading compared to Remdesivir. Furthermore, 10b6 and 10b12 showed 1.6- to 2-fold lower EC₅₀ values against coronavirus HCoV-OC43 than Remdesivir, highlighting their potential for further development as broad-spectrum antiviral agents.

Sulfuretin is widely distributed in different families of nature, such as Anacardiaceae, Compositae and Leguminosae. Since it was isolated, modern pharmacological researches revealed that this compound exhibited various bioactivities, namely neuroprotection, antioxidant, anti-cancer, hepatoprotection, anti-microbial, anti-inflammation, anti-diabetes, etc., which were attributed to many important molecular targets, such as NF-κB, PI3K/AKT/mTOR, Nrf2/HO1, MAPKs and JNK/ERK. However, there was no comprehensive and critical review of this compound searched on the Internet since it was discovered, which have been recognized for more than seventy years. Hence, this review comprehensively summarizes the pharmacological effects of sulfuretin (from 1953 to 2024) by searching Scifinder, Web of Science, CNKI, PubMed and the Plant List (www.theplantlist.org) as well as other published databases and books. By this review, we hope that further study of sulfuretin will focus on the mechanism exploration and to evaluate its toxicity, then to perform related clinical assessment.

An accurate deep learning predictor is needed for enzyme optimal temperature (${T}_{opt}$), which quantitatively describes how temperature affects the enzyme catalytic activity. In comparison with existing models, a new model developed in this study, Seq2Topt, reached a superior accuracy on ${T}_{opt}$ prediction just using protein sequences (RMSE = 12.26°C and R2 = 0.57), and could capture key protein regions for enzyme ${T}_{opt}$ with multi-head attention on residues. Through case studies on thermophilic enzyme selection and predicting enzyme ${T}_{opt}$ shifts caused by point mutations, Seq2Topt was demonstrated as a promising computational tool for enzyme mining and in-silico enzyme design. Additionally, accurate deep learning predictors of enzyme optimal pH (Seq2pHopt, RMSE = 0.88 and R2 = 0.42) and melting temperature (Seq2Tm, RMSE = 7.57 °C and R2 = 0.64) were developed based on the model architecture of Seq2Topt, suggesting that the development of Seq2Topt could potentially give rise to a useful prediction platform of enzymes.