Integrated In Silico Profiling of Altissimacoumarin H in Prostate Cancer-Related Molecular Networks

Abstract

Prostate cancer involves interconnected androgenic, inflammatory, hypoxia-related, receptor tyrosine kinase, and survival pathways, motivating hypothesis-generating multi-target analyses. This study investigated Altissimacoumarin H (ATSC), a naturally occurring terpenylated coumarin, using an integrated in silico workflow combining target prediction, network pharmacology, transcriptomic assessment, molecular docking, molecular dynamics (MD), ADME/toxicity prediction, and density functional theory (DFT). Target prediction yielded 192 putative ATSC-associated proteins; intersection with 1,074 prostate cancer-associated genes identified 55 common targets. STRING/Cytoscape analysis highlighted ten hub targets, including STAT3, EGFR, NFKB1, TNF, HIF1A, ESR1, HSP90AB1, MAPK1, PTGS2, and GSK3B, with enrichment in cancer-, inflammatory-, oxidative stress-, receptor-, and hormone-related pathways. Transcriptomic assessment provided limited contextual support: five common targets were significantly downregulated in GSE89223, while GEPIA showed significant expression-level support mainly for PTGS2. Docking suggested plausible ATSC binding modes across the selected proteins, and a 100-ns MD simulation of the MAPK1/ERK2–ATSC complex showed generally stable structural behavior, with recurrent contacts involving LYS54, LYS114, VAL39, ASP111, SER153, and LEU156. MM/GBSA analysis yielded a median binding-energy estimate of -62.02 kcal/mol. SwissADME and toxicity predictions indicated reasonable drug-like features but also P-gp, structural-alert, and toxicity liabilities. DFT analyses indicated moderate electronic reactivity. Overall, these findings generate testable computational hypotheses regarding ATSC within prostate cancer-related molecular networks that require experimental validation.