Molecular insights into the anti-inflammatory activity of fermented pineapple juice using multimodal computational studies.

Pineapple has been recognized for its potential to enhance health and well-being. This study aimed to gain molecular insights into the anti-inflammatory properties of fermented pineapple juice using multimodal computational studies. In this study, pineapple juice was fermented using Lactobacillus paracasei, and the solution underwent liquid chromatography-mass spectrometry analysis. Network pharmacology was applied to investigate compound interactions and targets. In silico methods assessed compound bioactivities. Protein-protein interactions, network topology, and enrichment analysis identified key compounds. Molecular docking explored compound-receptor interactions in inflammation regulation. Molecular dynamics simulations were conducted to confirm the stability of interactions between the identified crucial compounds and their respective receptors. The study revealed several compounds including short-chain fatty acids, peptides, dihydroxyeicosatrienoic acids, and glycerides that exhibited promising anti-inflammatory properties. Leucyl-leucyl-norleucine and Leu-Leu-Tyr exhibited robust and stable interactions with mitogen-activated protein kinase 14 and IκB kinase ß, respectively, indicating their potential as promising therapeutic agents for inflammation modulation. This proposition is grounded in the pivotal involvement of these two proteins in inflammatory signaling pathways. These findings provide valuable insights into the anti-inflammatory potential of these compounds, serving as a foundation for further experimental validation and exploration. Future studies can build upon these results to advance the development of these compounds as effective anti-inflammatory agents.

Potential Utilization of Phenolic Acid Compounds as Anti-Inflammatory Agents through TNF-α Convertase Inhibition Mechanisms: A Network Pharmacology, Docking, and Molecular Dynamics Approach.

Inflammation is a dysregulated immune response characterized by an excessive release of proinflammatory mediators, such as cytokines and prostanoids, leading to tissue damage and various pathological conditions. Natural compounds, notably phenolic acid phytocompounds from plants, have recently garnered substantial interest as potential therapeutic agents to bolster well-being and combat inflammation recently. Based on previous research, the precise molecular mechanism underlying the anti-inflammatory activity of phenolic acids remains elusive. Therefore, this study aimed to predict the molecular mechanisms underpinning the anti-inflammatory properties of selected phenolic acid phytocompounds through comprehensive network pharmacology, molecular docking, and dynamic simulations. Network pharmacology analysis successfully identified TNF-α convertase as a potential target for anti-inflammatory purposes. Among tested compounds, chlorogenic acid (-6.90 kcal/mol), rosmarinic acid (-6.82 kcal/mol), and ellagic acid (-5.46 kcal/mol) exhibited the strongest binding affinity toward TNF-α convertase. Furthermore, phenolic acid compounds demonstrated molecular binding poses similar to those of the native ligand, indicating their potential as inhibitors of TNF-α convertase. This study provides valuable insights into the molecular mechanisms that drive the anti-inflammatory effects of phenolic compounds, particularly through the suppression of TNF-α production via TNF-α convertase inhibition, thus reinforcing their anti-inflammatory attributes.

Prediction of the interaction between Calloselasma rhodostoma venom-derived peptides and cancer-associated hub proteins: A computational study.

The use of peptide drugs to treat cancer is gaining popularity because of their efficacy, fewer side effects, and several advantages over other properties. Identifying the peptides that interact with cancer proteins is crucial in drug discovery. Several approaches related to predicting peptide-protein interactions have been conducted. However, problems arise due to the high costs of resources and time and the smaller number of studies. This study predicts peptide-protein interactions using Random Forest, XGBoost, and SAE-DNN. Feature extraction is also performed on proteins and peptides using intrinsic disorder, amino acid sequences, physicochemical properties, position-specific assessment matrices, amino acid composition, and dipeptide composition. Results show that all algorithms perform equally well in predicting interactions between peptides derived from venoms and target proteins associated with cancer. However, XGBoost produces the best results with accuracy, precision, and area under the receiver operating characteristic curve of 0.859, 0.663, and 0.697, respectively. The enrichment analysis revealed that peptides from the Calloselasma rhodostoma venom targeted several proteins (ESR1, GOPC, and BRD4) related to cancer.