Exploring the Synergistic Anticancer Effects of Iranian Medicinal Plants on EGFR Driven Tumor Cell Lines: Integrative Computational and Molecular Dynamics Study)

  • سال انتشار: 1402
  • محل انتشار: دوازدهمین همایش ملی و سومین همایش بین المللی بیوانفورماتیک
  • کد COI اختصاصی: IBIS12_129
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 149
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نویسندگان

Mohammad Shirinpoor

Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

Shokoofeh Ghiam

School of biological sciences, Institute for research in fundamental sciences, Tehran, Iran

Changiz Eslahchi

Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran

چکیده

Chemotherapy, a crucial component in cancer treatment, presents considerable challenges,including high costs, substantial side effects, and variable efficacy. Despite advancements in EGFRtargetedtherapies for EGFR-positive cancers, the persistent challenge of resistance necessitatesinnovative solutions. This study focuses on targeting the epidermal growth factor receptor (EGFR)using compounds derived from Iranian medicinal plants. To efficiently navigate the abundance ofpotential compounds, we employ a computational approach to streamline candidates for laboratoryexperimentation, optimizing resources and accelerating the identification of promising anti-EGFRagents.inspired by the computational methods proposed in three seminal papers by (Ahmadi Moughari et al.,۲۰۲۱; Emdadi et al, ۲۰۲۱; Yassaee Meybodi et al, ۲۰۲۱) our study leverages their methodologies. Weemploy their models to generate outputs, subsequently used as inputs for our deep neural networkdesigned to predict IC۵۰ values of Iranian medicinal plant compounds against EGFR-associated celllines. This integrative approach aims to enhance the predictive accuracy and efficiency of identifyingpotential anti-EGFR agents, contributing to the advancement of cancer treatment strategies.Leveraging the ChemBL database, we meticulously extract the structural features of drugs using RDKit.Incorporating expression and miRNA data as cell line descriptors results in a dataset comprising۱۵۳,۹۱۴ drugs, each characterized by ۳۸ features across ۲۳ unique cell lines. This comprehensiveapproach enables the merging of chemical and biological information, providing a holistic perspectiveon the relationships between compound properties and efficacy against EGFR-associated cell lines.Following the IC۵۰ prediction of natural compounds, our subsequent step involves molecular dockingstudies with the EGFR۱-۴ kinase domain. This analysis evaluates binding affinities and interactions,facilitating the identification of promising compounds. Prioritized candidates undergo moleculardynamics simulations to explore the stability and behavior of the compound-EGFR complexes overtime, offering valuable insights into their structural aspects and potential as anti-EGFR agents.This integrative computational and molecular dynamics approach thoroughly evaluates naturalcompounds, promising a multi-faceted understanding of their potential as effective EGFR inhibitors.Such initiatives significantly contribute to developing cost-effective and efficient strategies fordiscovering novel therapies tailored to EGFR-positive cancers.

کلیدواژه ها

Computational methods, molecular docking, molecular dynamics, anti-EGFR agents, natural compounds

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