Essential oil composition, anti-tyrosinase activity, and molecular docking studies of Knema intermedia Warb. (Myristicaceae).
Abubakar Siddiq SalihuWan Mohd Nuzul Hakimi Wan SallehWilliam N SetzerPublished in: Zeitschrift fur Naturforschung. C, Journal of biosciences (2023)
Knema is one of the genera in the Myristicaceae family. The genus includes 60 species in Southeast Asia and is traditionally used for treating skin disorders. Here, for the first time, the essential oil, anti-tyrosinase, and molecular docking studies of Knema intermedia were evaluated. The essential oil was obtained by hydrodistillation and fully characterized by gas chromatography (GC-FID) and gas chromatography-mass spectrometry (GC-MS). Anti-tyrosinase activity was evaluated against mushroom tyrosinase, whereas molecular docking studies were performed using Autodock vina embedded in PyRx to evaluate the binding interactions of major components. A total of 37 components (97.3%) were successfully identified in the essential oil, which was characterized by high amounts of t-muurolol (20.1%), α-copaene (14.4%), δ-cadinene (13.9%), germacrene B (9.5%), and δ-selinene (7.0%). The essential oil displayed moderate inhibitory activity towards tyrosinase with an IC 50 value of 70.2 μg/mL. The best docking energy was observed with δ-selinene (-7.8 kcal/mol), and it also forms interactions with His85, His263, and His244 which are important amino acid residues of the tyrosinase receptor. Hence, this study provides valuable scientific data on K. intermedia as potential candidate for the development of natural antiaging formulations.
Keyphrases
- essential oil
- molecular docking
- gas chromatography
- molecular dynamics simulations
- gas chromatography mass spectrometry
- mass spectrometry
- tandem mass spectrometry
- case control
- amino acid
- solid phase extraction
- high resolution mass spectrometry
- molecular dynamics
- high intensity
- electronic health record
- transcription factor
- high resolution
- liquid chromatography
- soft tissue
- machine learning
- deep learning