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<p><i>Rhizophora apiculata</i> Blume (RAP; Rhizophoraceae), a marine true mangrove, exhibits notable immunomodulatory potential, though its molecular mechanisms remain poorly understood. This study employed a pharmacoinformatics-based approach to predict the mechanism of its anti-inflammatory potential. Literature mining and GC–MS profiling identified 10 drug-like phytochemicals. Target prediction using SwissTargetPrediction, SuperPred, and PharmMapper, integrated with inflammation-associated gene databases, predicted 70 overlapping inflammation-related targets. Protein–protein interaction analysis of these targets yielded a highly enriched network (56 nodes, 190 edges; PPI enrichment <i>p</i> &lt; 1.0e<sup>−16</sup>). Functional enrichment analyses indicated strong associations with immune and inflammatory processes, including leukocyte migration, chemotaxis, and lipopolysaccharide response, alongside key inflammatory signalling pathways. Site-specific molecular docking of RAP phytochemicals against the topologically prominent targets EGFR, COX2, JAK2 and MAPK14 revealed favourable binding affinities. Notably, glycosin (<b>5</b>), ( +)-dihydroquercetin (<b>1</b>), and isolariciresinol (2) were predicted to bind stably to druggable anti-inflammatory targets. Molecular dynamics simulations further supported the stability of these complexes, particularly COX2 + <b>5</b>, JAK2 + <b>1</b>, and MAPK14 + <b>2</b>. These findings provide testable mechanistic hypotheses for the anti-inflammatory effects of <i>R. apiculata</i>.</p>

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Pharmaco-informatics based prediction of anti-inflammatory targets modulated by Rhizophora apiculata

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Walsan Kalarikkal Vishnu,
  • Chandrasekharan Guruvayoorappan

摘要

Rhizophora apiculata Blume (RAP; Rhizophoraceae), a marine true mangrove, exhibits notable immunomodulatory potential, though its molecular mechanisms remain poorly understood. This study employed a pharmacoinformatics-based approach to predict the mechanism of its anti-inflammatory potential. Literature mining and GC–MS profiling identified 10 drug-like phytochemicals. Target prediction using SwissTargetPrediction, SuperPred, and PharmMapper, integrated with inflammation-associated gene databases, predicted 70 overlapping inflammation-related targets. Protein–protein interaction analysis of these targets yielded a highly enriched network (56 nodes, 190 edges; PPI enrichment p < 1.0e−16). Functional enrichment analyses indicated strong associations with immune and inflammatory processes, including leukocyte migration, chemotaxis, and lipopolysaccharide response, alongside key inflammatory signalling pathways. Site-specific molecular docking of RAP phytochemicals against the topologically prominent targets EGFR, COX2, JAK2 and MAPK14 revealed favourable binding affinities. Notably, glycosin (5), ( +)-dihydroquercetin (1), and isolariciresinol (2) were predicted to bind stably to druggable anti-inflammatory targets. Molecular dynamics simulations further supported the stability of these complexes, particularly COX2 + 5, JAK2 + 1, and MAPK14 + 2. These findings provide testable mechanistic hypotheses for the anti-inflammatory effects of R. apiculata.