0% Complete
the 4th international edition and 13th Iranian Conference on Bioinformatics
international edition and Iranian Conference on Bioinformatics
صفحه اصلی
/
4th international edition and 13th Iranian Conference on Bioinformatics
Exploring the Anticancer Potential of Flavonoids from Morus alba Against Breast Cancer: An In Silico Approach
نویسندگان :
Kimia Asadi
1
Negar Ghaleh Navi
2
Ehsan Karimi
3
1- Islamic Azad University of Mashhad
2- Islamic Azad University of Mashhad
3- Islamic Azad University of Mashhad
کلمات کلیدی :
Breast Cancer،Morus alba،Flavonoids،Molecular Docking،In Silico
چکیده :
Morus alba, commonly known as white mulberry, is a Moraceae family plant widely used in traditional Chinese medicine. The leaves, bark, and fruit of this plant have been valued for their various medicinal properties, including antibacterial, anti-inflammatory, anti-cancer, anti-obesity, antidiabetic, and antioxidant effects (Batiha and Teibo, 2023). Morus alba contains a variety of compounds, such as tannins, saponins, triterpenes, phenolics, flavonoids, benzofuran, anthocyanins, anthraquinones, aromatic compounds, and minerals (Gryn-Rynko and Bazylak, 2016). Previous studies have shown that the fruit of Morus alba possesses anti-cancer properties, largely attributed to its high concentration of bioactive compounds, particularly flavonoids (Zhumabayev and Zhakipbekov, 2024). Flavonoids exhibit a range of biological activities, including the inhibition of proliferation, cell cycle arrest, induction of apoptosis, antioxidant effects, and anti-metastatic properties (Wen and Fang, 2021). They play a significant role in regulating cell proliferation, invasion, angiogenesis, and oxidative stress. Consequently, flavonoids have become subjects of considerable interest in drug discovery research. Recent studies indicate that natural compounds, such as flavonoids, show promising outcomes with fewer side effects compared to conventional treatments (Slika and Mansour, 2022). Breast cancer is one of the most prevalent types of cancer affecting women worldwide (Ke and Wang, 2021). Caspase-3 is a crucial target in the development of anticancer drugs, as its cleavage and activation lead to the apoptosis of cancer cells (Eskandari and Eaves, 2022). In this study, we evaluate the binding affinities of the main flavonoids found in Morus alba—specifically rutin, morin, quercetin, and myricetin—against the caspase-3 protein. The 3D structure of the caspase-3 protein (PDB ID: 2XYP) was obtained from the RCSB Protein Data Bank, while the 3D structures of rutin, morin, quercetin, and myricetin were downloaded from the PubChem database (PubChem CID: 5280805, 5281670, 5280343, and 5281672, respectively). We employed iGEMDOCK (version 2.1) to conduct molecular docking, utilizing the following docking accuracy parameters: a population size of 300, 70 generations, and 3 solutions. Docking scores (DOS) represent the estimated interaction energy between the ligands and the protein, with more negative scores indicating higher binding affinity (Bhowmik and Nandi, 2021). The interaction between the caspase-3 protein and four selected ligands was investigated. The calculated energy levels for the ligands were as follows: Rutin (-104.15), Morin (-101.85), Quercetin (-97.96), and Myricetin (-99.31). These energy values indicate that the ligand with the highest negative energy affinity will exhibit a stronger binding affinity to the protein. Based on our results, rutin is the most effective ligand for the caspase-3 protein among the flavonoids studied, showing great potential as a candidate for developing caspase-3-targeted anticancer agents. Future studies should focus on validating these findings and exploring the mechanisms underlying the interactions between the ligands and the protein.
لیست مقالات
لیست مقالات بایگانی شده
Reconstruction and analysis of lncRNA-miRNA-mRNA ceRNA network to explore the potential biomarkers for colorectal cancer
Sara Nafei Milani - Negar Sadat Soleimani Zakeri - Habib MotieGhader
Predicting Anticancer Drug Repurposing Candidates using Knowledge Graphs
Marzieh Khodadadi AghGhaleh - Rooholah Abedian - Reza Zarghami - Sajjad Gharaghani
Physical parametric study of bacterial biofilm disruption and removal by jet impingement: A CFD investigation
Fatemeh Ebrahimi Tarki - Mahboobeh Zarrabi - Mahkame Sharbatdar - Ahya Abdi Ali
Bioinformatic Approach to Predict the Regulatory Mechanisms: TF–miRNA–mRNA–lncRNA Network during Cluster Development in Grape
Shahla Sahraei - Nafiseh Mahdinezhad - Abbasali Emamjomeh - Kaveh Kavousi - Mahmood Solouki - Massimo Delledonne
Predicting Adverse Drug Reactions with Advanced Machine Learning Techniques
AlI Mohammadian - Sara Haghighi Bardine - Fatemezahra Alizade
Designing Guide RNAs Considering Essential Genes for Genome Editing of Yarrowia lipolytica Using Deep Learning in the CRISPR System
Zahra Vahdani - Farshad Darvishi - Fatemeh Zare-Mirakabad
Enhancing Predictive Accuracy of CRISPR-Cas9 on-target efficiency using Deep Learning and Active Learning Optimization for Small Datasets
Masoud Mahdavifar - Fateme Zaremirakabad
Using structural controllability to analyze signaling pathways and PPI networks for the identification of therapeutic targets in colorectal cancer
Zoha RashidiPour - Farinaz Roshani
Comprehensive Gene and Protein Catalog for Antimicrobial Environments: A Metagenomic Approach to Mitigate Antimicrobial Resistance
Donya Afshar Jahanshahi - Arad Ariaeenejad - Arman Hasannejad - Mohammad Reza Zabihi - Shohreh Ariaeenejad - Kaveh Kavousi
Diabetes nephropathy indicators for early diagnosis
Sedigheh Momenzadeh - Masumeh Jalalvand - Reza Nedaeinia
بیشتر
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 44.5.0