Post-translational modifications (PTMs) are major regulators of G protein-coupled receptor (GPCR) signaling, trafficking and surface expression, yet residue-level evidence is fragmented across databases and computational predictions often ignore the compartment constraints imposed by the seven-transmembrane topology. We developed GPCR-PTM, an interpretable framework that integrates database-backed annotations with topology-constrained, rule-based prediction of phosphorylation, N-linked glycosylation, S-palmitoylation and ubiquitination. Sites are separated into Verified (UniProt ECO:0000269 experimental annotation), Supported (other database-backed evidence) and Predicted (rule-based) evidence layers; database citations are screened at the PubMed title/abstract level; and predicted sites are ranked using explicit sequence rules, membrane topology and evolutionary conservation. Across 839 reviewed human GPCRs, the pipeline consolidated 9559 source records into 7247 unique database-backed PTM sites spanning 827 receptors. Among 13,453 merged citation-site pairs, 13,216 (98.2%) had retrievable cached abstracts, but only 353 (2.7%) were residue-explicit, showing that abstract granularity should not be equated with evidence quality. In a retrospective benchmark against 246 UniProt ECO:0000269 experimental sites, the rule system recovered 216 sites (87.8%), including 75.4% of phosphorylation sites and all evaluable N-linked glycosylation and S-palmitoylation sites. Because the glycosylation and palmitoylation rules encode the canonical recognition patterns themselves, complete recovery for these classes reflects rule coverage rather than predictive discrimination; phosphorylation, the least pattern-bound class, reached a strict precision of 7.0% and an AUPRC of 0.074, so the overall recovery should be read as coverage under blinded lookup rather than calibrated predictive performance. Compartment-matched enrichment further supported the pXpp phosphorylation cluster (3.19-fold enrichment) and confirmed the expected localization of canonical N-glycosylation and juxtamembrane palmitoylation rules. Interactive bilingual reports combine evidence filtering, a PTM landscape overview, topology-aware sequence annotation and AlphaFold-based 3D context. GPCR-PTM is intended as an auditable hypothesis-generation resource for basic GPCR research and pharmacology rather than a calibrated probability predictor. Prospective validation and direct comparison with learned predictors will be required to quantify predictive performance on previously unseen biology.
The decline in pharmaceutical R&D productivity necessitates innovative strategies to explore nature's vast chemical diversity. Traditional natural product discovery is hampered by slow, resource-intensive methods where novel therapeutics may reside. This study introduces a high-throughput workflow integrating untargeted metabolomics with an artificial intelligence (AI) drug discovery platform to rapidly identify anti-diabetic candidates from the mushroom Pleurotus citrinopileatus. High-resolution LC-MS/MS analysis identified 1025 distinct molecular features, which were annotated with high confidence using sub-ppm mass accuracy and MS2 fragmentation validation. These metabolites were subjected to in silico ADMET profiling and pharmacokinetic clustering, which prioritized a subset of 27 compounds (Cluster 14) with favorable pharmacokinetic profiles, including predicted active transport permeability. Subsequent multi-target molecular docking of this cluster against α-amylase, α-glucosidase, and DPP4 prioritized 5S-hydroxynorvaline-S-Ile as a high-confidence multi-target hit. This dipeptide-like compound exhibited favorable multi-target binding profiles across all three enzymes (dG = −6.43, −6.26, and − 4.75 kcal/mol for DPP4, α-amylase, and α-glucosidase, respectively) by forming critical hydrogen bonds with their respective catalytic residues. Docking simulation via AutoDock further validated 5S-hydroxynorvaline-S-Ile as the top-ranked binder among the 27 compounds against the three targets. This work successfully demonstrates a novel, AI-driven paradigm that efficiently navigates complex metabolomic data to discover and prioritize a putative multi-target hit requiring experimental validation, demonstrating this integrated approach as an efficient prioritization framework for future natural product drug discovery.
Glioblastoma remains one of the most lethal and therapeutically intractable primary brain malignancies, with a critical need for novel therapeutic discoveries. Phenanthrenes and dihydrophenanthrenes, gaining attention for their cytotoxic and antiproliferative activities, represent a promising yet underexplored chemical space for glioblastoma drug discovery. Phytochemical investigation of Vanda curvifolia, a species of phenanthrene-rich family Orchidaceae, afforded ten previously undescribed phenanthrene and dihydrophenanthrene constituents, designated vandafolins A–J (1−10), together with twelve known compounds. Structures were elucidated by NMR and HR-ESI-MS, with absolute configurations assigned by ECD spectroscopy and optical rotation measurements supported by TDDFT calculations. Vandafolins A, B, G, and H exhibited moderate cytotoxicity against U-87 MG glioblastoma cells, while vandafolins A, G, and H further demonstrated moderate antiproliferative activity. Vandafolin A induced G0/G1 cell cycle arrest, while vandafolins G and H induced apoptosis in U-87 MG glioblastoma cells, as determined by propidium iodide (PI) and Annexin V/PI staining flow cytometry, respectively. These results identified vandafolins A, G, and H as preliminary cytotoxic hits against glioblastoma cells, warranting further mechanistic and structural investigation.
Background Tenofovir disoproxil fumarate (TDF) is one of the first-line antiretroviral therapies in patients with HIV infection. It may be associated with renal impairment. This study aimed to evaluate risk factors for a rapid decline in estimated glomerular filtration rate (eGFR) in patients with HIV infection treated with a TDF-based regimen. Methods This was a retrospective cohort study conducted in adult patients with HIV infection who were aged 18 years or older, had a baseline eGFR of 90 ml/min/1.73m2, and were treated at an HIV clinic with TDF. The primary outcome of this study was a decline in eGFR of 5 or more ml/min/1.73m2 with stable or deteriorating eGFR in the following year. Predictors of rapid eGFR decline were calculated by logistic regression analysis. Results A total of 533 patients with HIV infection met the study criteria. Of those, 174 patients (32.65%) experienced an eGFR decline of 5 ml/min/1.73m2 or more after treatment with TDF. The average follow-up time was 7.94 years (SD 1.80). Four factors were identified in the predictive model for a decline in eGFR of 5 or more ml/min/1.73m2, including age, hepatitis B virus infection, urine albumin-to-creatinine ratio, and duration of TDF treatment. Only duration of TDF treatment was independently associated with a decline in eGFR of 5 or more ml/min/1.73m2, with an adjusted odds ratio of 1.188 (95% confidence interval: 1.063–1.328). Conclusion This long-term cohort study of patients with HIV infection treated with TDF showed that duration of TDF treatment may be associated with a rapid decline in eGFR. Annual monitoring of eGFR is warranted.
Lung cancer remains a leading cause of cancer mortality, and resistance to apoptosis limits the effectiveness of conventional and targeted therapies. This systematic review synthesized preclinical evidence on natural products that induce non-apoptotic regulated cell death in lung cancer, focusing on pyroptosis, cuproptosis, and necroptosis. Six databases were searched for original studies evaluating natural products, phytochemicals, plant extracts, or related bioactive compounds in lung cancer models. Overall, 25 studies met the inclusion criteria: 18 investigated pyroptosis, 5 investigated necroptosis, and 2 investigated cuproptosis. Because the included agents represented diverse chemical classes and most primary studies did not perform formal structure-activity relationship analyses, this review was designed as a mechanistic evidence map rather than a scaffold-optimization guide. Evidence was predominantly preclinical and focused on non-small cell lung cancer, especially A549-based models; 17 studies also included xenograft, allograft, or related animal experiments. Across chemically diverse agents, natural products were associated with reduced viability, proliferation, migration, invasion, colony formation, spheroid growth, metastasis, or tumor growth. Pyroptosis most frequently involved ROS/NF-κB/NLRP3/caspase-1/GSDMD signaling or caspase-3/GSDME activation. Cuproptosis studies highlighted copper accumulation, DLAT-associated mitochondrial stress, redox imbalance, and metabolic disruption, whereas necroptosis studies emphasized RIPK1/RIPK3-centered signaling with contributions from ROS, calcium flux, lysosomal injury, mitochondrial dysfunction, and autophagy. Overall, natural products show promise as multi-target modulators of non-apoptotic cell death in lung cancer, but the field remains clinically immature and requires further validation in standardized preclinical and clinical studies.
