Ask about this productRelated genes to: DOCK2 antibody
- Gene:
- DOCK2 NIH gene
- Name:
- dedicator of cytokinesis 2
- Previous symbol:
- -
- Synonyms:
- KIAA0209
- Chromosome:
- 5q35.1
- Locus Type:
- gene with protein product
- Date approved:
- 1998-05-13
- Date modifiied:
- 2019-04-23
Related products to: DOCK2 antibody
Related articles to: DOCK2 antibody
- Natural products are widely used in the treatment of cancer due to the side effects of chemotherapeutics, and a large number of natural compounds have been reported to have anticancer activities in different types of cancer. The aim of the study was to analyze the antiproliferative and apoptotic effects of capsaicin and alpha-lipoic acid on prostate cancer cells in silico and in vitro. The effects of capsaicin and alpha-lipoic acid on the proliferation of prostate cancer cells were assessed using MTT cell viability assays. Apoptotic protein levels were measured using Western blot analysis. Ligand-protein potential interactions of alpha-lipoic acid and capsaicin with survivin and bax proteins were examined using CB-Dock2 and SwissDock software. Capsaicin and alpha-lipoic acid significantly inhibited the proliferation of both prostate cancer cell lines in a dose- and time-dependent manner. Our data revealed that various concentrations of capsaicin, alpha-lipoic acid and their combinations caused remarkable downregulation of survivin expression on prostate cancer cells. According to the Vina scores, alpha-lipoic acid and capsaicin have the potential to interact strongly with survivin and bax proteins. We suggest that both natural compounds have the potential for treating prostate cancer according to the in vitro and in silico results. - Source: PubMed
Publication date: 2026/04/04
Meral OguncBalkan Burcu MenekseBestil Dilek NurSayiner SerkanCeylanli DenizUyurca SarePehlivan SinemOzkurt GuzinKismali GorkemSel Tevhide - - Source: PubMed
Publication date: 2026/04/22
Lu MinXu ShaotingTan YingDeng JianpingFeng BingbingQiu DingWang XinyingSun HaiyangJi KaileZhu BowenHu ShixianWu XuetingChen MinhuIwakura YoichiroWang XinyingFeng RuiQi YangfanTang Ce - Human telomerase reverse transcriptase (hTERT) plays a key role in cancer cell immortalization and represents an important therapeutic target for anticancer drug discovery. In this study, a computational pipeline combining genetic algorithms (GAs) and machine learning (ML) was developed to design and screen garlic-derived bioactive compounds as potential telomerase inhibitors. Garlic phytochemicals were used as the initial chemical space, which was iteratively evolved through mutation and fragment expansion to generate novel ligand candidates. A multi-parameter fitness function incorporating Lipinski, Veber, and Ghose drug-likeness rules, quantitative estimate of drug-likeness (QED), hydrogen bond donor/acceptor balance, and aromatic ring constraints was used to guide optimization. In addition, a RandomForest-based classifier was applied to pre-screen compounds for predicted telomerase activity prior to molecular docking. The shortlisted ligands were evaluated using CB-Dock2, and further assessed for pharmacokinetic and toxicity properties using SwissADME, including solubility, lipophilicity, and bioavailability. Out of 125 generated ligands, 14 met both drug-likeness and predicted activity criteria and progressed through the full pipeline. The highest binding affinity observed was -10.5 kcal/mol; however, this top-scoring compound was excluded due to ADMET rule violations. The remaining candidates exhibited favorable physicochemical properties, acceptable solubility, balanced lipophilicity, and good predicted oral bioavailability. Overall, the results demonstrate that genetic algorithms can efficiently generate structurally diverse and pharmacologically relevant scaffolds, and that integrating an activity-based machine learning filter prior to docking improves screening efficiency by prioritizing biologically meaningful candidates. Compared with traditional GA-based docking workflows, this integrated strategy provides a more selective and cost-effective approach for early-stage ligand discovery. - Source: PubMed
Publication date: 2026/04/21
Elmir HassenGhazli AbdelkaderBoubchir LarbiDaoudi Abdelaziz - Bergapten, a furocoumarin, was assessed for its potential as a therapeutic agent targeting non-small cell lung cancer (NSCLC) using comprehensive computational approaches. Geometry optimization and electronic property calculations were performed via Density Functional Theory (DFT) using Gaussian 16 with the B3LYP functional and 6-311G basis set. Physicochemical and ADMET profiles were predicted with SwissADME and ADMET-AI. Bergapten's pharmacological targets were identified from the CTD Database and intersected with NSCLC-related genes from GeneCards; overlapping genes underwent PPI analysis in STRING, hub gene ranking in Cytoscape, and enrichment analyses for Gene ontology and pathways. Differential expression and survival impacts of top hub genes (TP53, CASP3, AKT1) were evaluated using GEPIA2. Molecular docking was conducted with CB-Dock2 and post-docking flexibility examined using iMODS. Molecular dynamic simulation was executed with GROMACS software. DFT calculations suggested a stable molecular conformation for bergapten, highlighting reactive oxygen sites and enabling mapping of charge distribution relevant for biological interactions. Drug-likeness analysis predicted potentially high oral bioavailability, favorable physicochemical and ADMET profiles, minimal toxicity risks, and suitable synthetic accessibility. Target identification identified 33 NSCLC-associated genes that may interact with bergapten interactions, enriched for apoptosis, cell signaling, and inflammation. PPI analysis revealed a strongly interconnected network, with central regulatory hubs (muTP53, CASP3, AKT1) linked to key cancer pathways. GO and pathway enrichment mapped bergapten's action to proliferative and apoptotic mechanisms, platinum drug resistance, and inflammation. Survival analysis suggested that high muTP53 expression may be linked to reduce DFS, while AKT1 and CASP3 expression showed non-significant trends toward poorer and improved survival, respectively. Molecular docking predicted possible binding between bergapten and the three hub proteins, with iMODS confirming complex adaptability and structural stability. Collectively, RMSD, RMSF, SASA, and hydrogen bond analyses consistently predicted that Bergapten possibly forms a stable and well-accommodated complex with AKT1 during the 100 ns MD simulation. Bergapten exhibits molecular and pharmacokinetic characteristics with predicted regulatory interactions across NSCLC-associated networks that probabilistically support its classification as a potential lead candidate, warranting further experimental validation against lung cancer. - Source: PubMed
Publication date: 2026/04/16
Yang LiuCui YufengCui ShihanSun Wei - To investigate the potential risk of nicotine exposure on glioblastoma multiforme (GBM). - Source: PubMed
Publication date: 2026/04/09
Yu ShuyaoLong MinHuang NanquLuo YongCai Jing