Screening of Oxygenated Aromatic Compounds for Potential Antifungal Activity against Geotrichum citri-aurantii through Structure-Activity Relationship Analysis

Metabolomic profiles and differential metabolites of volatile components in Citrus aurantium Changshan-huyou pericarp during different growth and development stages

Citrus fruits possess a distinctive aroma and flavor, with Citrus aurantium Changshan-huyou (CACH) standing out due to their considerable edible and medicinal value. However, the volatile components (VOCs) in the CACH pericarp (CP) remain underexplored. In this study, gas chromatography-mass spectrometry (GC-MS) was utilized to qualitatively analyze VOCs in 27 CP samples across different growth stages. A total of 544 VOCs were identified, including 91 terpenoids. The types, quantities and distributions of VOCs were conducted. Detailed discussions on the major terpenoids in CP were also presented. A metabolomics approach combining multivariate statistical analysis with univariate analysis was employed for screening the differential metabolites. The study provides comprehensive insights into the VOCs in CP and citrus plants. Moreover, it delivers the first in-depth analysis of differential metabolites in CP throughout the entire CACH growth and development process, laying a foundation for ongoing research and development of the VOCs in CP.

Virtual screening strategy for anti-DPP-IV natural flavonoid derivatives based on machine learning

Flavonoids, especially their inhibitory effect on DPP-IV activity, have been widely recognized for their antidiabetic effects. However, the variety of natural flavonoid derivatives is very rich, and even subtle structural differences can lead to several orders of magnitude differences in their inhibitory activities against DPP-IV, which makes it challenging to find novel and potent anti-DPP-IV flavonoid derivatives experimentally. Therefore, there is an urgent need to develop an efficient screening pipeline that targets active natural products. Here, we propose a fusion strategy based on a QSAR model, and to simplify this process, it was applied to the discovery of flavonoid derivatives with potent anti-DPP-IV activity. First, the high-quality QSAR model (R test 2 = 0.816, MAEtest = 0.14, MSEtest = 0.026) was composed of seven key molecular property parameters, which were constructed with the genetic algorithm (GA) and passed the leave-one-out cross-validation evaluation. A total of 1,668 flavonoid derivatives were obtained from the natural product enriched by NPCD based on molecular fingerprint similarity (> 0.8). Further, the enriched flavonoid derivatives were further predicted and screened using the QED score combined with the QSAR model, and a total of 33 flavonoid derivatives (IC50pre < 6.5 μM) were found. Subsequently, three flavonoid derivatives (5,7,3',5'-tetrahydroxyflavone, 3,7-dihydroxy-5,3',4'-trimethoxyflavone, and 5,7,2',5'-tetrahydroxyflavone) with highly effective anti-DPP-IV activity were obtained by ADMET analysis. Finally, the DPP-IV inhibitory potential of these three flavonoid derivatives was verified by 100 ns MD simulation and MM/PB(GB)SA.Communicated by Ramaswamy H. Sarma.

Discovery of potential anti- Staphylococcus aureus natural products and their mechanistic studies using machine learning and molecular dynamic simulations

The structure-activity analysis (SAR) and machine learning were used to investigate potential anti-S. aureus agents in a faster method. In this study, 24 oxygenated benzene ring components with S. aureus inhibition capacity were confirmed by literature exploring and in-house experiments, and the SAR analysis suggested that the hydroxyl group position may affect the anti-S. aureus activity. The 2D-MLR-QSAR model with 9 descriptors was further evaluated as the best model among the 21 models. After that, hesperetic acid and 2-HTPA were further explored and evaluated as the potential anti-S. aureus agents screening in the natural product clustering library through the best QSAR model calculation. The antibacterial capacities of hesperetic acid and 2-HTPA had been investigated and proved the similar predictive pMIC value resulting from the QSAR model. Besides, the two novel components were able to inhibit the growth of S. aureus by disrupting the cell membrane through the molecular dynamics simulation (MD), which further evidenced by scanning electron microscopy (SEM) test and PI dye results. Overall, these results are highly suggested that QSAR can be used to predict the antibacterial agents targeting S. aureus, which provides a new paradigm to research the molecular structure-antibacterial capacity relationship.

Computation-Directed Molecular Design, Synthesis, and Fungicidal Activity of Succinate Dehydrogenase Inhibitors

Succinate dehydrogenase inhibitors (SDHIs) are a class of fungicides targeting the pathogenic fungi mitochondrial SDH. Here, molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR), and molecular dynamics (MD) simulations were used to guide SDHI innovation. Molecular docking was performed to explore the binding modes of SDH and its inhibitors. 3D-QSAR models were carried out on 33 compounds with activity against Rhizoctonia cerealis (R. cerealis); their structure-activity relationships were analyzed using comparative molecular field analysis and comparative molecular similarity indices analysis. MD simulations were used to assess the stability of the complexes under physiological conditions, and the results were consistent with molecular docking. Binding free energy was calculated through the molecular mechanics generalized born surface area method, and the binding free energy was decomposed. The results are consistent with the activity of bioassay and indicate that van der Waals and lipophilic interactions contribute the most in the molecular binding process. Afterward, we designed and synthesized 12 compounds under the guidance of the above-mentioned analyses, bioassay found that F9 was active against R. cerealis with the EC50 value of 9.43 μg/mL, and F4, F5, and F9 were active against Botrytis cinerea with an EC50 values of 5.80, 3.17, and 1.63 μg/mL, respectively. They all showed good activity between positive controls of pydiflumetofen and thifluzamide. Our study provides new considerations for effective SDHIs discovery.

3D-QSAR Combination with Molecular Dynamics Simulations to Effectively Design the Active Ryanodine Receptor Agonists against Spodoptera frugiperda

Computer-aided molecular modeling was applied to design a series of Spodoptera frugiperda RyR agonists. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. MD simulations in the complex with S. frugiperda native, mutant RyR, and mammalian RyR1 under physiological conditions were used to validate the detailed binding mechanism. Binding free energy calculation by molecular mechanics generalized surface area (MM-GBSA) explained the role of key amino acid residues in ligand-receptor binding. Therefore, 14 new compounds were effectively designed and synthesized, and a bioassay indicated that compounds A-2 and A-3 showed comparable activity to that of chloranthraniliprole with LC50 values of 0.27, 0.18, and 0.20 mg L-1, respectively, against S. frugiperda. Most target compounds also displayed good activity against Mythinma separata at 0.1 mg L-1. Molecular docking and MM-GBSA calculations demonstrated that A-3 had a better binding capacity with native and mutant S. frugiperda RyRs.