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Results for “Molecular docking and molecular dynamics simulation”

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PubMed2026

The Mechanism of Celosiae Semen in the Treatment of Diabetic Cataract: Based on Network Pharmacology.

INTRODUCTION: Diabetes mellitus can be complicated by a variety of ocular diseases, among which the postoperative complications of diabetic cataract (DC) are significantly higher than those of non-DC patients. Therefore, finding drugs with natural active ingredients is an urgent challenge in the prevention and treatment of DC. Discovering the potential molecular mechanism of celosiae semen (CS) for the treatment of DC and providing new ideas and programs for the treatment and prevention of DC. METHODS: In this study, network pharmacology, molecular docking, and molecular dynamics simulations were utilized to predict the binding and functional enrichment of the main active ingredients of CS with DC-related targets, and to explore the potential pathways and mechanisms of CS for the treatment of DC. RESULTS: Through database searching and screening, a total of 45 potential targets of CS for the treatment of DC were identified, functionally enriched, and a protein-protein interaction network was constructed, and the key target, SRC, was finally found. The results of molecular docking and molecular dynamics simulation showed that the main active ingredient of CS, stigmasterol, could bind stably to the key target SRC protein. DISCUSSION: This study not only elucidates the phyto-pharmacological basis of CS in DC management but also provides a framework for developing natural product-derived targeted therapies against diabetic ocular complications. The integration of modern genomics and computational chemistry to deconstruct the therapeutic effects of traditional Chinese herbal medicines has great clinical significance in expanding the scope of traditional Chinese medicines for the treatment of DC and promoting precision targeting. However, this requires verification through basic experiments. CONCLUSION: These computational findings suggest that CS may exert its anti-cataract effects through the multi-target modulation of diabetic metabolic pathways and SRC-mediated signaling cascades.

Humans↗

PubMed2003

Plausible interaction of an alpha-fetoprotein cyclopeptide with the G-protein-coupled receptor model GPR30: docking study by molecular dynamics simulated annealing.

In this manuscript, the procedure of molecular dynamics simulated annealing is applied to locate a probable receptor and binding site of a cyclicpeptide that inhibits estrogen-stimulated proliferation of breast cancer. The hydrophilic cyclopeptide EMTOVNOGQ (O = 4-hydroxyproline), derived from alpha-fetoprotein, is an inhibitor of estrogen-stimulated proliferation of human breast cancer. This peptide has been shown to act through a mechanism different from that of estrogen; however, its receptor is unknown. We report computer experiments that suggest that this peptide may execute its actions by interacting with GPR30, a G-protein-coupled receptor. The subject of this work is the simulation, by molecular dynamics simulated annealing, of the interaction of cyclopeptide EMTOVNOGQ with receptor GPR30 protein. A conformational analysis of the cyclopeptide was undertaken and the final structure was docked on several sites of the GPR30 3D model. Our results show that the cyclopeptide interacts on the pocket located between TM6 and TM7 transmembrane helices of the G-protein, triggering a slight conformational change in the secondary structure of the receptor in the complex. Based on differences in accessible surface areas between GPR30 and its ligand, the residues in the interaction zone were identified. The cyclopeptide is stabilized in the active site by forming a network of hydrogen bonds between Glu, Thr, (1)Pro(OH) and GLn residues of the ligand and Arg-259, Cys-271, Asn-316, Asn-320 and Tyr-324 of the G-protein. Moreover, the study of the electrostatic surface potential on the GPR30 receptor shows that the active site is more positively charged than the other sites. Our modeling indicates a plausible interaction of the cyclopeptide with the seven transmembrane GPR30 protein. This may have profound implications for the treatment of breast cancer.

Amino Acid Sequence↗

PubMed2026

Exploring the Potential Molecular Targets of Cyanidin-3-O-glucoside for Type 2 Diabetes Mellitus Treatment.

INTRODUCTION: This study aims to elucidate the multi-target molecular mechanism of cyanidin-3-O-glucoside (C3G) in treating Type 2 Diabetes (T2DM) through network pharmacology methods. METHODS: The study was designed to predict the targets of C3G through public databases and to screen for T2DM-related targets. Protein-protein interaction (PPI) network analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the common targets. Core targets were further validated through molecular docking and molecular dynamics (MD) simulations. RESULTS: This research identified a total of 57 potential targets of C3G in the treatment of T2DM. Subsequent PPI analysis identified ALB (Degree=43), AKT1 (Degree=41), and TNF (Degree=41) as the top three hub proteins. Pathway analysis indicated significant involvement in the insulin signaling pathway (P = 4.205×10-9), AMPK signaling pathway (P = 9.582×10-7), and FoxO signaling pathway (P = 1.315×10-6). Molecular docking revealed strong binding affinities between C3G and NOS3 (-9.5 kcal/mol), PPARG (-9.0 kcal/mol), TNF (-8.5 kcal/mol), and INSR (-8.4 kcal/mol). MD simulations further confirmed that the C3G-target complex has excellent binding stability. DISCUSSION: C3G may intervene in the pathological progression of T2DM by regulating key pathways such as insulin sensitivity, inflammatory responses, and oxidative stress. Further studies suggest that INSR and NOS3 may be new targets through which C3G exerts its effects, but their specific mechanisms and in vivo biological functions still need to be elucidated by subsequent experiments. CONCLUSION: C3G may intervene in the progression of T2DM in a multi-pathway synergistic manner by targeting key molecules such as INSR and NOS3.

Anthocyanins↗

PubMed2001

Metabolism of sirolimus and its derivative everolimus by cytochrome P450 3A4: insights from docking, molecular dynamics, and quantum chemical calculations.

A combination of quantum chemical calculations and molecular simulations (DOCKing and molecular dynamics) is used to investigate the metabolism of sirolimus (rapamycin) and its derivative everolimus (SDZ-RAD) by cytochrome P450 3A4. Both molecules are drugs with high immunosuppressive activity. Our calculations yield qualitative predictions of the regiospecificities of the hydroxylations and O-dealkylations occurring in these two substrates which are in good agreement with recent experimental results. An analysis of the modeled enzyme-substrate interactions allows us to rationalize the reduced metabolic activity of the larger substrate everolimus compared to sirolimus. Moreover, our simulations suggest that hydrogen donor functionalities close to the metabolic site are important for anchoring the substrate at the active center of the enzyme. In particular, we predict that replacing one hydroxyl group by a fluorine atom should considerably suppress the major metabolic reaction in sirolimus, 39-O-demethylation.

Algorithms↗

PubMed2026

Leveraging bioinformatics approaches for drug repositioning in space radiation protection.

The health effects of space radiation, primarily Galactic Cosmic Rays (GCRs), on humans remain largely unknown, with potential cardiovascular consequences posing a significant threat to astronauts on long-duration spaceflight missions. Currently, there are no established pharmacological countermeasures for GCR exposure. Drug repositioning offers a promising strategy to accelerate pharmaceutical research in space medicine. This study leverages existing bioinformatics techniques to identify and prioritize potential drug candidates associated with proteomic perturbations following simulated GCR exposure using previously published murine cardiac proteomic data. A protein-protein interaction (PPI) network was constructed using the top differentially expressed proteins (DEPs) from murine heart tissue following exposure to 5-ion GCRs as seed nodes, focusing on experimentally supported interactions. Network topology, Markov clustering, and functional enrichment analyses were used to characterize biologically relevant proteins and pathways. Drug-protein interactions were predicted using Drugst.One and mapped to PPI clusters of interest to identify candidate drugs. Selected drug-macromolecule interactions were further explored using CB-Dock2 molecular docking and short-duration molecular dynamics simulations as hypothesis-generating structural assessments. Analysis of a key PPI network cluster consisting of several ATP synthase proteins identified 23 unique drug candidates. These analyses demonstrate a systematic approach for leveraging bioinformatics techniques to identify candidate molecular targets and generate pharmacological hypotheses in the context of space radiation countermeasures. Ultimately, this strategy introduces a hypothesis-generating framework for the prioritization of potential drug candidates for future computational characterization and experimental investigation against spaceflight stressors.

Animals↗

PubMed2026

Exploring the mechanism of Shengmai San in treating lung adenocarcinoma based on bioinformatics and molecular dynamics simulation.

To investigate the mechanism of Shengmai San (SMS) in the treatment of lung adenocarcinoma (LUAD) based on an integrated strategy combining "network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulation," aiming to provide a precise combination therapy strategy and identify potential bioactive compounds. Differentially expressed genes in LUAD were identified from the Gene Expression Omnibus database using R (originally developed at Bell Laboratories and currently managed by Lucent Technologies). SMS components (ginseng, Ophiopogon japonicus, and Schisandra chinensis) were retrieved from encyclopaedia of traditional Chinese medicine, with Lipinski-compliant compounds selected. Compound targets were predicted via SwissTargetPrediction and Similarity Ensemble Approach. Intersecting targets between differentially expressed genes and compound targets were identified for "herbs-compounds-targets-disease" network construction. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. Hub targets were identified by analyzing the protein-protein interaction network. High-prognostic relevance targets were screened from The Cancer Genome Atlas. Compounds targeting these were identified through the herbs-compounds-targets-disease network, and absorption, distribution, metabolism, excretion, and toxicity-compliant compounds were selected using SwissADME (a web-based tool provided by the Molecular Modeling Group of the Swiss Institute of Bioinformatics). Core regulatory targets were identified through molecular docking, with complex stability assessed by molecular dynamics simulations. The key bioactive compounds of SMS for treating LUAD were identified as 7-hydroxy-2,5-dimethyl-4H-1-benzopyran-4-one, N-trans-feruloyltyramine, paprazine, and (E)-N-[(2S)-2-hydroxy-2-(4-hydroxyphenyl)ethyl]-3-(4-hydroxyphenyl)prop-2-enamide. Hub targets included AURKA, CCNA2, CCNB1, CDK1, CHEK1, KIF11, NEK2, PLK1, TTK, and TYMS. Among these, CDK1, CHEK1, and PLK1 demonstrated both high-prognostic relevance and strong binding affinity with SMS, emerging as core regulatory targets for SMS in LUAD treatment. Mechanistically, SMS exerts its anticancer effects primarily by modulating the tumor necrosis factor, interleukin-17, cell cycle, and Lipid and atherosclerosis signaling pathways. The active components of SMS, such as paprazine, may exert antitumor effects partly through downregulating CDK1, CHEK1, and PLK1 expression. Although the present study did not examine drug-resistance models or combination regimens, our findings raise the possibility that, in patients with high expression of these genes, combining SMS with standard chemotherapy or targeted therapy could potentially enhance chemosensitivity and mitigate the development of resistance. This hypothesis, however, requires formal testing in appropriate preclinical models and functional validation studies.

Molecular Dynamics Simulation↗

PubMed2004

Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP.

Most current docking methods to identify possible ligands and putative binding sites on a receptor molecule assume a rigid receptor structure to allow virtual screening of large ligand databases. However, binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a bound ligand. An approach is presented that allows relaxation of the protein conformation in precalculated soft flexible degrees of freedom during ligand-receptor docking. For the immunosuppressant FK506-binding protein FKBP, the soft flexible modes are extracted as principal components of motion from a molecular dynamics simulation. A simple penalty function for deformations in the soft flexible mode is used to limit receptor protein deformations during docking that avoids a costly recalculation of the receptor energy by summing over all receptor atom pairs at each step. Rigid docking of the FK506 ligand binding to an unbound FKBP conformation failed to identify a geometry close to experiment as favorable binding site. In contrast, inclusion of the flexible soft modes during systematic docking runs selected a binding geometry close to experiment as lowest energy conformation. This has been achieved at a modest increase of computational cost compared to rigid docking. The approach could provide a computationally efficient way to approximately account for receptor flexibility during docking of large numbers of putative ligands and putative docking geometries.

Binding Sites↗

PubMed2000

Selectivity analysis of 5-(arylthio)-2,4-diaminoquinazolines as inhibitors of Candida albicans dihydrofolate reductase by molecular dynamics simulations.

A series of 5-(arylthio)-2,4-diaminoquinazolines are known as selective inhibitors of dihydrofolate reductase (DHFR) from Candida albicans. We have performed docking and molecular dynamics simulations of these inhibitors with C. albicans and human DHFR to understand the basis for selectivity of these agents. Study was performed on a selected set of 10 compounds with variation in structure and activity. Molecular dynamics simulations were performed at 300 K for 45 ps with equilibration for 10 ps. Trajectory data was analyzed on the basis of hydrogen bond interactions, energy of binding and conformational energy difference. The results indicate that hydrogen bonds formed between the compound and the active site residues are responsible for inhibition and higher potency. The selectivity index, i.e the ratio of I50 against human DHFR to I50 against fungal DHFR, is mainly determined by the conformation adapted by the compounds within the active site of two enzymes. Since the human DHFR active site is rigid, the compound is trapped in a higher energy conformation. This energy difference between the two conformations deltaE mainly governs the selectivity against fungal DHFR. The information generated from this analysis of potency and selectivity should be useful for further work in the area of antifungal research.

Antifungal Agents↗

PubMed2001

Molecular dynamics simulations of the three dimensional model of plasmepsin II-peptidic inhibitor complexes.

Plasmodium falciparum is a major causative agent of malaria, a disease of worldwide importance. Inhibition of a hemoglobin degrading P. falciparum aspartic protease Plasmepsin II (Plm II) provides a viable strategy for antimalarial therapy. Linear peptidic inhibitors based on the 4(S)-amino-3(S)-hydroxy-5-phenylpentanoic acid at the P1-P1' positions are known which inhibit Plm II with improved selectivity over cathepsin D. A series of computations were performed in order to gain insight into the interactions of these inhibitors with Plm II. The docking and molecular dynamics simulations were performed on a model ligand/enzyme complex to optimize the variables involved in the generation of ligand/enzyme models. This protocol of docking and molecular dynamics (MD) simulation was then used to derive the ligand-enzyme complexes of the molecules used in the present study. Different modes of binding of pepstatin and the three linear inhibitors were studied. Molecular dynamics simulation was performed at 300K for 100ps with a time step of Ifs. The structural effects of ligand binding were analyzed on the basis of hydrogen bond interactions, interaction energies, hydrophobic contacts and RMS deviations in the resulting energy-minimized structures of the receptor-ligand complexes. The results indicate that hydrophobic and hydrogen bonding interactions are responsible for selective inhibition of Plm II and improved selectivity over cathepsin D. Hydrogen bonding interaction plays an important role for amino acid residues such as Asp-34, Asp-214, Thr-217, Ser-218, Val-78, Ser-79, Tyr-192 and Gly-216. The binding of the inhibitors to the enzyme, while producing no large distortions in the enzyme active site cleft, results in significant RMS deviations of the inhibitor, which represent the distortion of the inhibitor, effected by the proteinase. Thus, the information generated from this analysis should be useful for further work in the area of antimalarial research.

Aspartic Acid Endopeptidases↗

PubMed2026

Integrated dual transcriptome sequencing and experimental validation reveal potential mechanisms of baicalin against pneumocystis pneumonia in immunosuppressed rats.

BACKGROUND: Pneumocystis pneumonia (PCP) remains a major cause of morbidity and mortality in immunocompromised individuals. Although baicalin (Ba), a natural bioactive flavonoid, has demonstrated protective and therapeutic effects against PCP, its molecular mechanisms remain undefined. We employed dual RNA sequencing (dual RNA-seq) to characterize host and pathogen transcriptional responses to Ba treatment in an immunosuppressed rat model of PCP. METHODS: Comparative transcriptomic analyses identified differentially expressed genes in both the host and Pneumocystis, followed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analyses. Candidate targets were further investigated using network pharmacology, protein-protein interaction analysis, molecular docking, and molecular dynamics simulations. Key findings were validated by immunohistochemistry, enzyme-linked immunosorbent assay, and quantitative PCR. RESULTS: Ba markedly remodeled host and pathogen transcriptomes. Host transcriptomic analyses showed that Ba attenuated inflammatory and oxidative stress responses by modulating immune-related pathways, including Toll-like receptor, NF-κB, cytokine-cytokine receptor interaction, chemokine signaling, Th17 cell differentiation, and antigen processing and presentation. Experimental validation demonstrated that Ba reduced pulmonary expression of indoleamine 2,3-dioxygenase 1 (IDO1), Toll-like receptor 2 (TLR2), and TLR4 while increasing nuclear factor erythroid 2-related factor 2 (Nrf2) and its downstream antioxidant enzyme heme oxygenase-1 (HO-1). Pathogen transcriptomic analysis identified Pneumocystis Rtt109 (PcRtt109), a fungal histone acetyltransferase, as a potential pathogen-specific target that was significantly downregulated after Ba treatment. Molecular docking and molecular dynamics simulations supported stable interactions between Ba and IDO1, Nrf2, TLR2, TLR4, and PcRtt109, with the strongest predicted binding observed for PcRtt109. CONCLUSION: Dual RNA-seq revealed that Ba exerts anti-PCP activity through coordinated modulation of host and pathogen molecular networks. Its therapeutic effects are associated with suppression of inflammatory signaling, enhancement of antioxidant defenses, and inhibition of a fungal virulence-associated target. These findings provide mechanistic insights into host-pathogen interactions during PCP and support Ba as a potential therapeutic candidate for PCP.

Nrf2↗

PubMed2004

Investigation of glucose binding sites on insulin.

Possible insulin binding sites for D-glucose have been investigated theoretically by docking and molecular dynamics (MD) simulations. Two different docking programs for small molecules were used; Multiple Copy Simultaneous Search (MCSS) and Solvation Energy for Exhaustive Docking (SEED) programs. The configurations resulting from the MCSS search were evaluated with a scoring function developed to estimate the binding free energy. SEED calculations were performed using various values for the dielectric constant of the solute. It is found that scores emphasizing non-polar interactions gave a preferential binding site in agreement with that inferred from recent fluorescence and NMR NOESY experiments. The calculated binding affinity of -1.4 to -3.5 kcal/mol is within the measured range of -2.0 +/- 0.5 kcal/mol. The validity of the binding site is suggested by the dynamical stability of the bound glucose when examined with MD simulations with explicit solvent. Alternative binding sites were found in the simulations and their relative stabilities were estimated. The motions of the bound glucose during molecular dynamics simulations are correlated with the motions of the insulin side chains that are in contact with it and with larger scale insulin motions. These results raise the question of whether glucose binding to insulin could play a role in its activity. The results establish the complementarity of molecular dynamics simulations and normal mode analyses with the search for binding sites proposed with small molecule docking programs.

Binding Sites↗

PubMedDate not supplied

Structure based prediction of binding affinity of human immunodeficiency virus-1 protease inhibitors.

A series of computations were performed to derive a strategy for the prediction of binding affinities of non-peptidic human immunodeficiency virus-1 (HIV-1) protease inhibitors. This paper describes the development of a 3D quantitative structure-activity relationship (3D-QSAR) methodology by using receptor information of HIV-1 protease. The docking and molecular dynamics simulations were performed on a model ligand/enzyme complex to optimize the variables involved in the generation of ligand/enzyme models. The protonation scheme of the active site aspartic acid residues of HIV-1 protease was derived from a computational study. The active site aspartate is monoprotonated with a proton placed on the OD1 atom of the ASP B25. This protocol of docking and molecular dynamics (MD) simulation was then used to derive the ligand-enzyme complexes of the molecules used in the present study. The molecular mechanics interaction descriptors were calculated from these ligand/enzyme models. A partial least squares (PLS) method was used to derive a linear correlation between the interaction descriptors and the biological activity. A good correlation was observed when the change in the energy of the ligand upon complex formation and the electrostatic contributions to the solvation energy of the ligand were included in the QSAR analysis. A highest cross-validated q2 value of 0.649 was observed. This model had a conventional r2 of 0.725, and when this model was used to predict the activity of the external test set, it produced a r2pred of 0.761. The total interaction energy was partitioned into interactions in different subsites and interactions with each of the amino acid residues of the enzyme. The PLS analysis using these descriptors helped to identify the important interactions which can be exploited for the design of HIV-1 protease inhibitors.

HIV Protease↗

PubMed2026

Targeting EGFR in cancer using Terminalia arjuna: An integrated In Silico, molecular dynamics, experimental validation, and network pharmacology study.

The Epidermal Growth Factor Receptor (EGFR) plays a pivotal role in 20-60% of cancer cases, including glioblastoma, lung adenocarcinoma, and head and neck squamous cell carcinoma, as reported in The Cancer Genome Atlas (TCGA) dataset. The present study employed an integrated in silico and experimental workflow to evaluate EGFR-targeted compounds from Terminalia arjuna. Drug-likeness and ADMET screening were performed, followed by molecular docking and 1000 ns molecular dynamics simulations. In vitro validation was conducted using cancer cell-based assays and network pharmacology to explore the molecular mechanisms associated with the identified compound. Screening shortlisted eight compounds from T. arjuna. Molecular docking identified Arjunaside C (-8.2 kcal/mol), Arjunapthanoloside (-7.7 kcal/mol), and Beta-sitosterol (-7.4 kcal/mol) as potential EGFR inhibitors compared to Erlotinib (-6.6 kcal/mol). Arjunapthanoloside formed more H-bonds and exhibited most stable interactions with EGFR. MD simulations at 1000 ns revealed lower RMSD, RMSF, SASA, and Rg values for the Arjunapthanoloside-EGFR complex, indicating enhanced stability. Direct binding validation was limited by the unavailability of purified Arjunapthanoloside; therefore, Arjuna extract was evaluated, which demonstrated potent cytotoxicity with an IC₅₀ of 9 µg/mL in H357 oral cancer cells. Flow cytometry confirmed apoptosis-mediated cell death by increased early- and late-apoptotic cell populations. Network pharmacology analysis further identified additional targets (MMP3, MMP7, MMP9, and HRAS) that are directly involved in various cancers. Overall, the findings provide new insights into the therapeutic potential of Arjunapthanoloside as a stable compound that interacts with EGFR from T. arjuna, highlighting its significance in EGFR-targeted anticancer research.

ErbB Receptors↗

PubMed2006

Molecular dynamics simulation of the P2Y14 receptor. Ligand docking and identification of a putative binding site of the distal hexose moiety.

A rhodopsin-based homology model of the P2Y14 receptor was inserted into a phospholipid bilayer and refined by molecular dynamics (MD) simulation. The binding modes of several known agonists, namely UDP-glucose and its analogues, were proposed using automatic molecular docking combined with Monte Carlo Multiple Minimum calculations. Compared to other P2Y receptors, the P2Y14 receptor has an atypical binding mode of the nucleobase, ribose, and phosphate moieties. The diphosphate moiety interacts with only one cationic residue, namely Lys171 of EL2, while in other P2Y receptor subtypes three Arg or Lys residues interact with the phosphate chain. Two other conserved cationic residues, namely Arg253 (6.55) and Lys277 (7.35) of the P2Y14 receptor together with two anionic residues (Glu166 and Glu174, located in EL2), are likely involved in interactions with the distal hexose moiety.

Binding Sites↗

PubMed1999

Docking of flexible ligands to flexible receptors in solution by molecular dynamics simulation.

In this paper, a method of simulating the docking of small flexible ligands to flexible receptors in water is reported. The method is based on molecular dynamics simulations and is an extension of an algorithm previously reported by Di Nola et al. (Di Nola et al., Proteins 1994;19:174-182). The method allows a fast exploration of the receptor surface, using a high temperature of the center of mass translational motion, while the ligand internal motions, the solvent, and the receptor are simulated at room temperature. In addition, the method allows a fast center of mass motion of the ligand, even in solution. The dampening effect of the solvent can be overcome by applying different weights to the interactions between system subsets (solvent, receptor, and ligand). Specific ligand-receptor distances have been used to compare the results of the simulations with the crystal structure. The method is applied, as a test system, to the docking of the phosphocholine to the immunoglobulin McPC603. The results show the similarity of structure between the complex in solution and in the crystal.

Algorithms↗

PubMed2026

Investigating the mechanisms of PhIP-induced colorectal cancer through network toxicology, machine learning, and molecular dynamics simulation.

BACKGROUND: Over the past few years, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP)- a compound from grilled or processed meats-has emerged as a major player in cancer development, especially colorectal cancer (CRC). This work dives into its potential links to CRC and uncovers the key genes that bridge this connection. METHODS: We tapped into various databases to pinpoint target genes tied to PhIP and CRC, then ran protein-protein interaction (PPI) analyses for visualization. Next, we explored underlying mechanisms through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. To nail down predictions, we tested 107 machine learning pipelines and picked the best one, validating its accuracy and the core genes' prognostic value across datasets. Next, molecular docking and dynamics simulations probed the interactions between these genes and PhIP. Finally, cell proliferation was assessed using Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) assays, and polymerase chain reaction (PCR) was performed to validate the expression levels of the hub genes. RESULTS: Our analysis identified 39 overlapping genes, from which a machine learning model (glmBoost + Enet) identified six candidate targets: CDK4, CEBPB, COMT, SOX9, TIMP1, and TOP2A. To prioritize these, a hierarchical screening framework was applied. Molecular docking and dynamics simulations identified CDK4, COMT, and TIMP1 as the most stable interactors with PhIP. Functional assays confirmed that PhIP treatment significantly enhanced the proliferation of CRC cells. Crucially, quantitative PCR (qPCR) validation in multiple CRC cell lines identified TIMP1 as the primary target, showing the most consistent and significant upregulation upon PhIP exposure. CONCLUSIONS: In essence, these genes drive PhIP is role in CRC, offering novel insights into its molecular pathways. This could reshape how we tackle food-related pollutants, paving the way for better prevention and targeted therapies.

Colorectal cancer (CRC)↗

PubMed2002

Molecular modeling of protein tyrosine phosphatase 1B (PTP 1B) inhibitors.

Binding modes of a series of aryloxymethylphosphonates and monoanionic biosteres of phosphate group from a series of benzylic alpha,alpha-diflluoro phosphate and its biosteres as protein tyrosine phosphatase 1B (PTP 1B) inhibitors have been identified by molecular modeling techniques. We have performed docking and molecular dynamics simulations of these inhibitors with PTP 1B enzyme. The initial conformation of the inhibitors for docking was obtained from simulated annealing technique. Solvent accessible surface area calculations suggested that active site of PTP 1B is highly hydrophobic. The results indicate that for aryloxymethylphosphonates, in addition to hydrogen bonding interactions, Tyr46, Arg47, Asp48, Val49, Glu115, Lys116, Lys120 amino acid residues of PTP 1B are responsible for governing inhibitor potency of the compounds. The sulfonate and tetrazole functional groups have been identified as effective monoanionic biosteres of phosphate group and biphenyl ring system due to its favorable interactions with Glu115, Lys116, Lys120 residues of PTP 1B found to be more suitable aromatic functionality than naphthalene ring system for benzylic alpha,alpha-diflluoro phosphate and its biosteres. The information generated from the present study should be useful in the design of more potent PTP 1B inhibitors as anti diabetic agents.

Binding Sites↗

PubMed2026

Proteomic regulation of anti-proliferative and anti-migratory activity by potent phytochemicals from Pistacia integerrima J.L. Steward Ex Brandis via PI3K, AKT1, and KRAS for Lung Cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the leading cause of mortality worldwide and remains a major therapeutic challenge due to high metastasis, drug resistance and limited treatments. Pistacia integerrima J.L. Steward Ex Brandis (PI) consists of flavonoids, steroids, terpenoids and phenolic compounds reported for pharmacological activities. The efficacy of potent bioactives from P. integerrima may be ascertained employing cytotoxic, antiproliferative, anti-migratory, and anti-metastatic evaluations in A549 NSCLC cells with proteomic profiling, molecular docking, and dynamics simulation study. METHODS AND RESULTS: PI EtAc produced significant dose-dependent cytotoxicity in A549 cells (100 µg/mL, p < 0.0001 in the MTT assay. There was a pronounced decrease in colony formation after treatment with EtAc, with 18.41% (p < 0.002), and markedly. Furthermore. PI EtAC markedly inhibited cell migration emphasized by wound healing and Transwell migration (p < 0.01) assays, indicating reduced metastatic migratory potential. Proteomic analysis demonstrated significant downregulation of Endoglin (CD105), KLK5 and MMP-2, indicating suppression of angiogenic and metastatic signalling pathways in the Human XL Oncology protein array. The interaction of major PI phytochemicals with key NSCLC-associated targets was recorded in Molecular docking, revealing favourable binding affinities of kaempferol, β- sitosterol, luteolin, and quercetin towards several oncogenic targets, including AKT1(- 7.6 kcal/mol), PI3K(- 9.4 kcal/mol), KRAS (- 8.5 kcal/mol) and MMP9 (- 8.1 kcal/mol). Molecular dynamics simulation confirmed the structural stability of the kaempferol -AKT1 complex throughout the 100 ns simulation. CONCLUSION: Pistacia integerrima bioactives exhibited significant anti-proliferative, anti-migratory, and anti-metastatic activities in vitro, which may provide scientific rationale identifying newer promising candidates for NSCLC.

Humans↗