Streptomycetes remain an important bacterial source of natural products (NPs) with significant therapeutic promise, particularly in the fight against antimicrobial resistance. Herein, we present StreptomeDB 4.0, a substantial update of the database that includes expanded content and several new features. Currently, StreptomeDB 4.0 contains over 8500 NPs originating from ∼3900 streptomycetes, manually annotated from ∼7600 PubMed-indexed peer-reviewed articles. The database was enhanced by two in-house developments: (i) automated literature-mined NP–protein relationships (hyperlinked to the CPRiL web server) and (ii) pharmacophore-based NP–protein interactions (predicted with the ePharmaLib dataset). Moreover, genome mining was supplemented through hyperlinks to the widely used antiSMASH database. To facilitate NP structural dereplication, interactive visualization tools were implemented, namely the JSpecView applet and plotly.js charting library for predicted nuclear magnetic resonance and mass spectrometry spectral data, respectively. Furthermore, both the backend database and the frontend web interface were redesigned, and several software packages, including PostgreSQL and Django, were updated to the latest versions. Overall, this comprehensive database serves as a vital resource for researchers seeking to delve into the metabolic intricacies of streptomycetes and discover novel therapeutics, notably antimicrobial agents. StreptomeDB is publicly accessible at https://www.pharmbioinf.uni-freiburg.de/streptomedb.

Sinomenine is a morphinan alkaloid with a variety of biological activities. Its derivatives have shown significant cytotoxic activity against different cancer cell lines in many studies. In this study, two series of sinomenine derivatives were designed and synthesized by modifying the active positions C₁ and C₄ on the A ring of sinomenine. Twenty-three compounds were synthesized and characterized by spectroscopy (IR, ¹H-NMR, ¹³C-NMR, and HRMS). They were further evaluated for their cytotoxic activity against five cancer cell lines, MCF-7, Hela, HepG2, SW480 and A549, and a normal cell line, Hek293, using MTT and CCK8 methods. The chlorine-containing compounds exhibited significant cytotoxic activity compared to the nucleus structure of sinomenine. Furthermore, we searched for cancer-related core targets and verified their interaction with derivatives through molecular docking. The chlorine-containing compounds 5g, 5i, 5j, 6a, 6d, 6e, and 6g exhibited the best against four core targets AKT1, EGFR, HARS and KARS. The molecular docking results were consistent with the cytotoxic results. Overall, results indicate that chlorine-containing derivatives might be a promising lead for the development of new anticancer agents.

The cysteine side chain has a free thiol group, making it the amino acid residue most often covalently modified by small molecules possessing weakly electrophilic warheads, thereby prolonging on-target residence time and reducing the risk of idiosyncratic drug toxicity. However, not all cysteines are equally reactive or accessible. Hence, to identify targetable cysteines, we propose a novel ensemble stacked machine learning (ML) model to predict hyper-reactive druggable cysteines, named HyperCys. First, the pocket, conservation, structural and energy profiles, and physicochemical properties of (non)covalently bound cysteines were collected from both protein sequences and 3D structures of protein–ligand complexes. Then, we established the HyperCys ensemble stacked model by integrating six different ML models, including K-nearest neighbors, support vector machine, light gradient boost machine, multi-layer perceptron classifier, random forest, and the meta-classifier model logistic regression. Finally, based on the hyper-reactive cysteines’ classification accuracy and other metrics, the results for different feature group combinations were compared. The results show that the accuracy, F1 score, recall score, and ROC AUC values of HyperCys are 0.784, 0.754, 0.742, and 0.824, respectively, after performing 10-fold CV with the best window size. Compared to traditional ML models with only sequenced-based features or only 3D structural features, HyperCys is more accurate at predicting hyper-reactive druggable cysteines. It is anticipated that HyperCys will be an effective tool for discovering new potential reactive cysteines in a wide range of nucleophilic proteins and will provide an important contribution to the design of targeted covalent inhibitors with high potency and selectivity.

Sinomenine is a morphinan alkaloid with a variety of biological activities. Its derivatives have shown significant cytotoxic activity against different cancer cell lines in many studies. In this study, two series of sinomenine derivatives were designed and synthesized by modifying the active positions C1 and C4 on the A ring of sinomenine. Twenty-three compounds were synthesized and characterized by spectroscopy (IR, 1H-NMR, 13C-NMR, and HRMS). They were further evaluated for their cytotoxic activity against five cancer cell lines, MCF-7, Hela, HepG2, SW480 and A549, and a normal cell line, Hek293, using MTT and CCK8 methods. The chlorine-containing compounds exhibited significant cytotoxic activity compared to the nucleus structure of sinomenine. Furthermore, we searched for cancer-related core targets and verified their interaction with derivatives through molecular docking. The chlorine-containing compounds 5g, 5i, 5j, 6a, 6d, 6e, and 6g exhibited the best against four core targets AKT1, EGFR, HARS and KARS. The molecular docking results were consistent with the cytotoxic results. Overall, results indicate that chlorine-containing derivatives might be a promising lead for the development of new anticancer agents.