همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

"Molecular Docking Study of Natural Compounds from Daucus carota as Inhibitors of VEGFR-2: A Computational Approach for Drug Design"

نویسندگان :

MohammadMoein Bashi Mehrizi¹ Seyed Mohammad Mahdi Mortazavian² Hossein Hadavand Mirzaei³

1- Master's student of the Department of Agronomy and Plant Breeding Sciences, College of Aburaihan, University of Tehran, Tehran, Iran 2- Associate Professor of Plant Molecular Genetics and Genetic Engineering, Department of Agronomy and Plant Breeding Sciences, College of Aburaihan, University of Tehran, Tehran, Iran. 3- Department of Molecular Physiology, Agricultural Biotechnology Research Institute of Iran, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.

کلمات کلیدی :

Natural Compounds from Daucus carota،VEGFR-2 Inhibition،Molecular Docking

چکیده :

Abstract The utilization of natural compounds from plants for drug design and development has been a prominent area of interest in pharmaceutical research (Smith & Brown, 2020). This study focuses on identifying and evaluating the potential of bioactive compounds from Daucus carota (carrot) for inhibiting the vascular endothelial growth factor receptor-2 (VEGFR-2), a protein critically involved in angiogenesis and cancer progression (Lee & Chang, 2019). The primary objective of this research was to discover natural inhibitors of VEGFR-2 by analyzing compounds derived from carrot through computational methods. Initially, a comprehensive list of natural compounds found in Daucus carota was obtained from reliable databases. These compounds were screened and filtered based on Lipinski's rule of five to assess their drug-likeness properties (Johnson & Nguyen, 2021). A total of 27 compounds were identified, out of which 23 adhered to Lipinski's criteria, suggesting their potential suitability for oral administration and therapeutic application. The subsequent phase involved molecular docking studies to investigate the binding affinity of these compounds to the active site of VEGFR-2. Molecular docking was conducted using advanced computational tools to determine the interaction energies between the selected compounds and VEGFR-2 (Smith & Brown, 2020). The docking results revealed that the average binding energy of the carrot-derived compounds with the VEGFR-2 active site was -5.7 kcal/mol. Among the tested compounds, daucol and germacrene D emerged as the most promising inhibitors, exhibiting binding energies of -7.67 kcal/mol and -7.51 kcal/mol, respectively. These values indicate strong interactions between these compounds and VEGFR-2, suggesting their potential efficacy as inhibitors. The docking analysis further revealed that the compounds formed stable interactions with critical residues in the VEGFR-2 active site, which are essential for its enzymatic activity (Lee & Chang, 2019). This finding supports the hypothesis that the identified compounds can effectively block the VEGFR-2 pathway, thereby inhibiting angiogenesis. Such inhibition is particularly significant in the context of cancer, where angiogenesis plays a pivotal role in tumor growth and metastasis. In conclusion, this study highlights the potential of natural compounds from Daucus carota as inhibitors of VEGFR-2, paving the way for the development of plant-based therapeutic agents for cancer treatment. The promising results of daucol and germacrene D underscore the importance of further in vitro and in vivo validation to confirm their efficacy and safety. Future research should also focus on exploring the synergistic effects of these compounds with existing chemotherapeutic agents to enhance their therapeutic impact. This study not only contributes to the growing body of knowledge on plant-derived bioactive compounds but also emphasizes the importance of integrating computational approaches in drug discovery to identify novel therapeutic candidates efficiently (Smith & Brown, 2020; Johnson & Nguyen, 2021).

لیست مقالات

لیست مقالات بایگانی شده

Homology modeling and molecular docking studies for discovering FlgK protein inhibitors; Helicobacter pylori flagellar subunit.

Vajiheh Eskandari

Bioinformatics studies on S35K mutation on Mnemiopsin 2 photoprotein

ََAmirReza Mohammadi - Vahab Jafarian - Fatemeh Khatami

Validation of Prognostic Biomarkers in Pancreatic Cancer Through Multi-Dataset Analysis and Pathway Enrichment

Ana Roohani - Somayeh Reiisi

Comprehensive Analysis of DCLK Family Kinases in Gastrointestinal Cancers and Neoplastic Progression

Zahra Salehi - Leili Rejali - Moein Piroozkhah - Pooya Jalali - Ali Aghajani - Mahsa Saeedi Niyasar - Binazir Khanabadi - Amir Sadeghi - Ehsan Nazemalhossein Mojarad

Identification of Driver Genes in Glioblastoma Based on Single-Cell Gene Expression Data Using Integrated Pseudotime and Phylogenetic Analysis

Fateme Mirza-Abolhassani - Sobhan Ahmadian Moghadam - Fatemeh Zare-Mirakabad - Kaveh Kavousi

HLA Class I Alleles as prognostic marker in Hepatocellular Carcinoma

Shahram Aliyari - Zahra Salehi - Mohammad Hossein Norouzi-Beirami - Kaveh Kavousi

A novel approach to find Biomarkers affecting Autism Spectrum Disorder (ASD) Using Machine Learning

Amir Zarghami - Milad Besharatifard - Fatemeh Zare-Mirakabad

Predicting drug response using omics data and artificial intelligence approach in cancer

Abdolhamid Nazerpanahi - Mohammad Ali Hossein Beigy - Amirhossein Keyhanipour - Kaveh Kavousi - Siavash Kavousi

In Silico Design of DNA G-Quadruplex Aptamers Targeting Lipopolysaccharide Core and Capsular Polysaccharide in Multidrug-Resistant Klebsiella pneumoniae

Aida Arezoumandchafi - Maryam Azimzadeh Irani - Hamidreza Mollasalehi

Comprehensive Analysis of EEG Signals for Machine Learning-Based Depression Detection

Mikaeil Tabarraei - Sepideh Jabbari

بیشتر

ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 42.7.0