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.

Community detection with Greedy Modularity disassembly strategy.

Community detection recognizes groups of densely connected nodes across networks, one of the fundamental procedures in network analysis. This research boosts the standard but locally optimized Greedy Modularity algorithm for community detection. We introduce innovative exploration techniques that include a variety of node and community disassembly strategies. These strategies include methods like non-triad creating, feeble, random as well as inadequate embeddedness for nodes, as well as low internal edge density, low triad participation ratio, weak, low conductance as well as random tactics for communities. We present a methodology that showcases the improvement in modularity across the wide variety of real-world and synthetic networks over the standard approaches. A detailed comparison against other well-known community detection algorithms further illustrates the better performance of our improved method. This study not only optimizes the process of community detection but also broadens the scope for a more nuanced and effective network analysis that may pave the way for more insights as to the dynamism and structures of its functioning by effectively addressing and overcoming the limitations that are inherently attached with the existing community detection algorithms.

Unveiling the anti-obesity potential of Kemuning (Murraya paniculata): A network pharmacology approach.

Obesity has become a global issue that affects the emergence of various chronic diseases such as diabetes mellitus, dysplasia, heart disorders, and cancer. In this study, an integration method was developed between the metabolite profile of the active compound of Murraya paniculata and the exploration of the targeting mechanism of adipose tissue using network pharmacology, molecular docking, molecular dynamics simulation, and in vitro tests. Network pharmacology results obtained with the skyline query technique using a block-nested loop (BNL) showed that histone acetyltransferase p300 (EP300), peroxisome proliferator-activated receptor gamma (PPARG), and peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PPARGC1A) are potential targets for treating obesity. Enrichment analysis of these three proteins revealed their association with obesity, thermogenesis, energy metabolism, adipocytokines, fat cell differentiation, and glucose homeostasis. Metabolite profiling of M. paniculata leaves revealed sixteen active compounds, ten of which were selected for molecular docking based on drug-likeness and ADME results. Molecular docking results between PPARG and EP300 with the ten active compounds showed a binding affinity value of ≤ -5.0 kcal/mol in all dockings, indicating strong binding. The stability of the protein-ligand complex resulting from docking was examined using molecular dynamics simulations, and we observed the best average root mean square deviation (RMSD) of 0.99 Å for PPARG with trans-3-indoleacrylic acid, which was lower than with the native ligand BRL (2.02 Å). Furthermore, the RMSD was 2.70 Å for EP300 and the native ligand 99E, and the lowest RMSD with the ligand (1R,9S)-5-[(E)-2-(4-Chlorophenyl)vinyl]-11-(5-pyrimidinylcarbonyl)-7,11-diazatricyclo[7.3.1.02,7]trideca-2,4-dien-6-one was 3.33 Å. The in vitro tests to validate the potential of M. paniculata in treating obesity showed that there was a significant decrease in PPARG and EP300 gene expressions in 3T3-L1 mature adipocytes treated with M. paniculata ethanolic extract starting at concentrations 62.5 µg/ml and 15.625 µg/ml, respectively. These results indicate that M. paniculata can potentially treat obesity by disrupting adipocyte maturation and influencing intracellular lipid metabolism.

Drug candidates and potential targets of Curculigo spp. compounds for treating diabetes mellitus based on network pharmacology, molecular docking and molecular dynamics simulation.

Curculigo spp. is a herb that is commonly used in Indonesia to treat diabetes mellitus (DM) . The main active components of Curculigo spp. were identified through our previous metabolomic study and online database platform. However, the biological mechanisms underlying Curculigo spp. activity in treating DM remain unclear. Therefore, in this study, a network pharmacology was used to explore the active compounds of Curculigo spp. and their potential molecular mechanisms for treating DM. Oral bioavailability and drug-likeness from the compounds of Curculigo spp. were screened using Lipinski's rule of five, BBB, HIA + and Caco-2 permeability criteria. A network of compound-target-disease-pathway was then constructed using Cytoscape. The highest degree compounds and targets were then confirmed by molecular docking and molecular dynamics (MD) simulations. The human body can absorb 33 compounds derived from Curculigo spp. In addition, 58 nodes and 62 edges generated a network analysis with the DM target. The highest degree of the compound-target-disease pathway was for orcinol glucoside, AKR1B1, autoimmune diabetes, bile acid and bile salt metabolism. Furthermore, the computational docking method on Curculigo spp. compounds with the highest degree revealed that orcinol glucoside interacted with PTPN1 through a hydrogen bond and resulted in a binding energy of -7.2 kcal mol-1. Through hydrogen bonds, orcinol glucoside in PTPN1 regulates multiple signaling pathways via the adherens junction pathway, which may play a therapeutic role in DM (type 2 diabetes: obesity). In addition, MD simulation confirmed that orcinol glucoside, is suitable for DM treatment by interacting with PTPN1.Communicated by Ramaswamy H. Sarma.

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.

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.

Differentially Expressed Genes Study Shown Potential for BCG Stimulation in Reducing the Severity of COVID-19.

The incidence of COVID-19 infection and death is known to be lower in tuberculosis (TB) endemic countries than in nonendemic countries. The Bacillus Calmette-Guerin (BCG) vaccination, which is commonly administered in TB endemic countries, was previously reported to have a nonspecific protective effect against several infections, including COVID-19. In this study, we used a differentially expressed genes (DEG) approach to analyze the genes modulated by BCG vaccination and COVID-19 infection. The Gene Expression Omnibus (GEO) database was used to select a COVID-19 gene expression data set with GSE164805, GSE14408, and GSE58636, and DEG in each data set were identified using the GEO2R online tools and selected using the adjusted p value (padj) 0.05 criteria. The protein-protein interaction (PPI) network was constructed from DEGs with the same trend of expression (upregulation or downregulation) using STRING version 11. The PPI network was performed by using the highest confidence number (0.9). DEGs that have a high-trust network were collected and functional cluster analysis of biological processes from Gene Ontology (GO), pathway analysis from the Human KEGG pathway, and COVID-19-related gene analysis was carried out using the Enrichr database. We found that either BCG or tuberculin increased the expression of several genes related to hyperinflammation, such as CCL3, CCL4, CSF2, IL1B, and LTA. In severe COVID-19, these genes were downregulated. This leads to the hypothesis that revaccination may have a protective effect against the severity of COVID-19 by reducing the hyperinflammatory status.

Combination of Enrichment Using Gene Ontology and Transcriptomic Analysis Revealed Contribution of Interferon Signaling to Severity of COVID-19.

Introduction: The severity of coronavirus disease 2019 (COVID-19) was known to be affected by hyperinflammation. Identification of important proteins associated with hyperinflammation is critical. These proteins can be a potential target either as biomarkers or targets in drug discovery. Therefore, we combined enrichment analysis of these proteins to identify biological knowledge related to hyperinflammation. Moreover, we conducted transcriptomic data analysis to reveal genes contributing to disease severity. Methods: We performed large-scale gene function analyses using gene ontology to identify significantly enriched biological processes, molecular functions, and cellular components associated with our proteins. One of the appropriate methods to functionally group large-scale protein-protein interaction (PPI) data into small-scale clusters is fuzzy K-partite clustering. We collected the transcriptomics data from GEO Database (GSE 164805 and GPL26963 platform). Moreover, we created a data set and analyzed gene expression using Orange Data-mining version 3.30. PPI analysis was performed using the STRING database with a confidence score >0.9. Results: This study indicated that four proteins were associated with 25 molecular functions, three were associated with 22 cellular components, and one was associated with ten biological processes. All GOs of molecular function, cellular components, and 9 of 14 biological processes were associated with important cytokines related to the COVID-19 cytokine storm present in the resulting cluster. The expression analysis showed the interferon-related genes IFNAR1, IFI6, IFIT1, and IFIT3 were significant genes, whereas PPIs showed their interactions were closely related. Conclusion: A combination of enrichment using GOs and transcriptomic analysis showed that hyperinflammation and severity of COVID-19 may be caused by interferon signaling.

Metabolite profiling of Andrographis paniculata leaves and stem extract using UHPLC-Orbitrap-MS/MS.

Andrographis paniculata is known as the king of bitter and it has been widely used as a medicinal plant. The properties of A. paniculata are generally determined by the metabolite composition, which may be influenced by several factors, one of which is the part of the plant extracted. The objectives of this research are to identify putatively the metabolite composition of the stem and the leaves extracts using LC-MS/MS and classify them using PCA. The stem and the leaves samples were separated and extracted using sonication with 70% ethanol. A total of 31 metabolite compounds has been putatively identified. All compounds were identified in the stem and the leaves extracts, which only differed in their intensity. These metabolite compounds were divided into diterpene lactones, flavonoids, and phenolic acid groups. By using the peak intensities of the 18 compounds identified, the leaves and stem extracts were grouped using PCA.

Bibliometric Analysis of Literature in Snake Venom-Related Research Worldwide (1933-2022).

Snake envenomation is a severe economic and health concern affecting countries worldwide. Snake venom carries a wide variety of small peptides and proteins with various immunological and pharmacological properties. A few key research areas related to snake venom, including its applications in treating cancer and eradicating antibiotic-resistant bacteria, have been gaining significant attention in recent years. The goal of the current study was to analyze the global profile of literature in snake venom research. This study presents a bibliometric review of snake venom-related research documents indexed in the Scopus database between 1933 and 2022. The overall number of documents published on a global scale was 2999, with an average annual production of 34 documents. Brazil produced the highest number of documents (n = 729), followed by the United States (n = 548), Australia (n = 240), and Costa Rica (n = 235). Since 1963, the number of publications has been steadily increasing globally. At a worldwide level, antivenom, proteomics, and transcriptomics are growing hot issues for research in this field. The current research provides a unique overview of snake venom research at global level from 1933 through 2022, and it may be beneficial in guiding future research.

Bipartite graph search optimization for type II diabetes mellitus Jamu formulation using branch and bound algorithm.

Jamu is an Indonesian traditional herbal medicine that has been practiced for generations. Jamu is made from various medicinal plants. Each plant has several compounds directly related to the target protein that are directly associated with a disease. A pharmacological graph can form relationships between plants, compounds, and target proteins. Research related to the prediction of Jamu formulas for some diseases has been carried out, but there are problems in finding combinations or compositions of Jamu formulas because of the increase in search space size. Some studies adopted the drug-target interaction (DTI) implemented using machine learning or deep learning to predict the DTI for discovering the Jamu formula. However, this approach raises important issues, such as imbalanced and high-dimensional dataset, overfitting, and the need for more procedures to trace compounds to their plants. This study proposes an alternative approach by implementing bipartite graph search optimization using the branch and bound algorithm to discover the combination or composition of Jamu formulas by optimizing the search on a plant-protein bipartite graph. The branch and bound technique is implemented using the search strategy of breadth first search (BrFS), Depth First Search, and Best First Search. To show the performance of the proposed method, we compared our method with a complete search algorithm, searching all nodes in the tree without pruning. In this study, we specialize in applying the proposed method to search for the Jamu formula for type II diabetes mellitus (T2DM). The result shows that the bipartite graph search with the branch and bound algorithm reduces computation time up to 40 times faster than the complete search strategy to search for a composition of plants. The binary branching strategy is the best choice, whereas the BrFS strategy is the best option in this research. In addition, the the proposed method can suggest the composition of one to four plants for the T2DM Jamu formula. For a combination of four plants, we obtain Angelica Sinensis, Citrus aurantium, Glycyrrhiza uralensis, and Mangifera indica. This approach is expected to be an alternative way to discover the Jamu formula more accurately.

Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches.

BACKGROUND: The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. METHODS: We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. RESULTS: From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. CONCLUSIONS: Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4'-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions.

Proteomic study of bioactive peptides from tempe.

Tempe is a traditional Indonesian fermented soybean mostly produced in small industries and sold locally throughout the country. Studies on the bioactive peptides in tempe are rare. Here, we studied bioactive peptides in samples from three tempe producers with different degrees of sanitation. The peptide sub-fractions of tempe from each producer were collected following water extraction, ultrafiltration (<3 kDa), gel filtration chromatography, and reversed phase-high performance liquid chromatography (RP-HPLC) separation followed by liquid chromatography-mass spectrometry (LC-MS). The MS spectra were then predicted using FindPept tools, and their biofunctionalities were confirmed with BIOPEP databases. There were few similar peptides found in tempe from the three producers. Peptides Val-His and Ala-Leu-Glu-Pro were found in tempe from all producers. Producers having a good sanitation level had more bioactive peptides than those with moderate or poor sanitation levels (58%, 43% and 35%, from good to poor sanitation). This work showed that the tempe from the three producers had antihypertensive, antidiabetic, antioxidative and antitumor peptides.