Bioscience Evidence 2024, Vol.14, No.4, 184-194 http://bioscipublisher.com/index.php/be 191 2 1 0 0 A m A 1000000 c CAS number to identify the compounds. The relative content of each component was calculated using the total ion peak area normalization method. Based on the component analysis results, β-caryophyllene in the sample was quantified using an external standard method, with chromatographic grade β-caryophyllene (Sigma, 99.9%) as the standard. A standard curve was plotted based on the concentration and peak area to determine the release amount of β-caryophyllene per gram of sample. The absolute content of trans-caryophyllene in the Cypress shells was determined using the standard curve. The content of other BVOCs components in the Cypress shells was semi-quantified using trans-caryophyllene as a reference (Koziel et al., 2017), with the calculation formula as follows: Where: m0 is the injection amount of the standard sample (g); A1 is the peak area of the sample; A2 is the peak area of the standard; C0 is the sample content (μg/g); 1 000 000 is the conversion factor. 4.4 BVOCs target prediction Based on the GC-MS detection results of Cypress BVOCs, the molecular information of the BVOCs was obtained. The CAS numbers of the small molecules were queried on the PubChem website (https://pubchem.ncbi.nlm.nih.gov) to obtain the SMILES information of the small molecules. The SMILES information was then input into the SwissTargetPrediction website (http://www.swisstargetprediction.ch/) to obtain the target prediction results. The prediction results for all small molecules of BVOCs were screened using the interaction probability >0.05 as the criterion, and the obtained results were considered as effective target molecules of Cypress BVOCs. 4.5 BVOCs-protein interaction modeling Based on the target prediction results, the predicted major proteins were used as receptor proteins, and α-pinene, a highly abundant component of BVOCs, as well as β-caryophyllene, which has a high probability of interaction with the proteins, were used as drug small molecules. The interaction between the small molecules and proteins was simulated using Autodock software. The main process involved three steps (Sarkar et al., 2024). First, the preparation of the macromolecular protein receptor: the 3D structure of the protein molecule was downloaded from the National Center for Biotechnology Information (NCBI) protein database (https://www.ncbi.nlm.nih.gov/protein), and the downloaded 3D model was converted to the mol2 (Molecular file) chemical file format containing molecular structure and related property information using OpenBabel software. Second, the preparation of the small molecule receptor file: the 3D structures of α-pinene (CID: 440968) and β-caryophyllene (CID: 5281515) were retrieved from the PubChem database, and the SDF (Solvent Description File) files containing information on molecular structure, charge, and interactions were downloaded. Finally, Autodock software was used: first, the macromolecular mol2 file was opened, dewatered, hydrogenated, and the charge calculated, and atomic types were added before being saved as a pdbqt format file, which was used as the macromolecular receptor. Then, the small molecule was opened, hydrogenated, the ligand root was determined, and flexible torsion was set before being saved as a pdbqt format file, which was used as the small molecule ligand. Blind docking was used to set the docking site and docking times, and semi-flexible docking was performed. The conformation with the lowest binding free energy was selected for model evaluation, and the docking results were visualized using Discovery Studio (https://www.3ds.com/products/biovia/discovery-studio). Acknowledgments The author sincerely appreciates the two anonymous peer reviewers for their feedback on the manuscript of this study. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.
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