Ellagic acid (EA) is a naturally occurring polyphenolic compound with antioxidant properties found in a variety of fruits and vegetables, including strawberries, raspberries, pomegranates, and nuts such as pecans and walnuts. EA appears to have potential health benefits, but more research is needed to fully understand its effects and mechanisms and how it can be used safely and effectively as a complimentary medicine. Notably, accumulating evidence demonstrates the anti-inflammatory effects of EA, but a unified mechanism is not well understood. A network pharmacology approach was used to understand the anti-inflammatory effects of EA. It was revealed that there are 52 targets of EA that are related to inflammation. GO and KEGG revealed many relevant signaling pathways, diseases relevant to inflammation. Topological analysis determined 10 important targets that are AKT1, VEGFA, TNF, MAPK3, ALB, SELP, MMP9, MMP2, PTGS2, and ICAM1. Molecular docking and molecular dynamics simulations were employed to help determine AKT1, PTGS2, VEGFA, MAPK3 as the most probable targets of EA with molecular mechanics Poisson-Boltzmann surface area binding energies of -82.37, -52.06, -19.24, and -13.98 kJ/mol, respectively. Overall, this paper confirmed the anti-inflammatory effects of EA demonstrated in previous studies and pinpointed very probably targets of EA.

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The missense mutation Y174H of FGF5 (FGF5-Y174H) had been associated with trichomegaly, characterized by abnormally long and pigmented eyelashes. The amino acid tyrosine (Tyr/Y) is conserved across many species, proposedly holding important characteristics for the functions of FGF5. One-microsecond molecular dynamics simulations along with protein-protein docking and residue interacting network analysis were employed to investigate the structural dynamics and binding mode of both wild-type (FGF5-WT) and its mutated counterpart (FGF5-Y174H). It was found that the mutation caused decreases in number of hydrogen bonds within the protein, sheet secondary structure, interaction of residues 174 with others, and number of salt-bridges. On the other hand, the mutation showed increases in solvent accessible surface area, number of hydrogen bonds between the protein and solvent, coil secondary structure, protein C-alpha backbone root mean square deviation, protein residue root mean square fluctuations, as well as occupied conformational space. In addition, protein-protein docking demonstrated that the mutated variant showed stronger binding affinity towards fibroblast growth factor receptor 1 (FGFR1). However, residue interacting network analysis demonstrated that the binding mode was drastically different from that of the FGF5-WT-FGFR1 complex. In conclusion, the mutation conferred more instability, stronger binding affinity towards FGFR1 with alteration in binding mode. These findings might help explain the decreased activation of FGFR1, underlying trichomegaly.

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