Target-specific toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology

Analysis of Structures of SARS-CoV-2 Papain-like Protease Bound with Ligands Unveils Structural Features for Inhibiting the Enzyme

The COVID-19 pandemic, driven by the novel coronavirus SARS-CoV-2, has drastically reshaped global health and socioeconomic landscapes. The papain-like protease (PLpro) plays a critical role in viral polyprotein cleavage and immune evasion, making it a prime target for therapeutic intervention. Numerous compounds have been identified as inhibitors of SARS-CoV-2 PLpro, with many characterized through crystallographic studies. To date, over 70 three-dimensional (3D) structures of PLpro complexed ligands have been deposited in the Protein Data Bank, offering valuable insight into ligand-binding features that could aid the discovery and development of effective COVID-19 treatments targeting PLpro. In this study, we reviewed and analyzed these 3D structures, focusing on the key residues involved in ligand interactions. Our analysis revealed that most inhibitors bind to PLpro's substrate recognition sites S3/S4 and SUb2. While these sites are highly attractive and have been extensively explored, other potential binding regions, such as SUb1 and the Zn(II) domain, are less explored and may hold untapped potential for future COVID-19 drug discovery and development. Our structural analysis provides insights into the molecular features of PLpro that could accelerate the development of novel therapeutics targeting this essential viral enzyme.

Triclosan: antimicrobial mechanisms, antibiotics interactions, clinical applications, and human health

The large-scale applications of Triclosan in industrial and household products have created many health and environmental concerns. Despite the fears of its drug-resistance and other issues, Triclosan is still an effective drug against many infectious organisms. Knowing the cross-interactions of Triclosan with different antibiotics, bacteria, and humans can provide much-needed information for the risk assessment of this drug. We review the current understanding of the antimicrobial mechanisms of Triclosan, how microbes become resistant to Triclosan, and the synergistic and antagonistic effects of Triclosan with different antibiotics. Current literature on the clinical applications of Triclosan and its effect on fetus/child development are also summarized.

Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data

Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.