Integrating untargeted metabolomics and computational docking for biomarker evaluation: A case study on marine algae-derived ligands

In the post-genomic era, liquid chromatography-mass spectrometry (LC-MS) has emerged as a powerful tool for profiling metabolic profiles in marine macroalgae, which are known for their chemical diversity and pharmacological potential. This study integrates untargeted metabolomics and computational chemistry to accelerate the discovery of therapeutic agents from two red algae (Jania rubens and Scinaia fascicularis) and two brown algae (Hydroclathrus clathratus and Sargassum cinereum). LC-MS-based analysis revealed genotypic variations influencing compound structures, functional groups, and physicochemical properties, which correlated with biological activity scores. Ligand-based virtual screening identified lead compounds with high therapeutic potential, while structure-based virtual screening highlighted stigmasta-5,24(28)-dien-3-ol (3α,24Z) (SM) as the top-ranked ligand, exhibiting a binding affinity of −11.40 kcal/mol. Docking optimization at exhaustiveness levels of 8, 16, and 32 demonstrated that level 8 achieved the best balance of accuracy and computational efficiency, completing in 49.74 s. Post-docking evaluation, including statistical analysis, validated the results, with ubiquinol-cytochrome-c reductase protein showing moderate-to-high activity scores for the selected compounds. These findings underscore the potential of marine algae-derived compounds as therapeutic agents, though further in vitro and in vivo studies are needed to confirm their bioactivity. This work highlights the importance of precise extraction and identification of bioactive compounds for advancing marine natural product research.