In Silico Design and Molecular Docking Analysis of a Novel RNA Aptamer Targeting EGFR Extracellular Domain

Title: In Silico Design and Molecular Docking Analysis of a Novel RNA Aptamer Targeting EGFR Extracellular Domain for CRISPR/Cas9 Delivery Abstract & Project Overview: This study presents a targeted, non-viral gene therapy approach for non-small cell lung cancer (NSCLC) by utilizing an EGFR-targeting RNA aptamer functionalized onto ionizable lipid nanoparticles (Apt-LNP) for precise CRISPR/Cas9 delivery. Key Highlights & Updates in this Revision: 1. Computational & In Silico Validation: Detailed molecular docking analysis using HDOCK demonstrating high structural binding affinity (docking score: -338.22) between the RNA aptamer and the EGFR extracellular domain. 2. Formulation & Biophysical Characterization: Standardized molar ratios for LNP formulation (DLin-MC3-DMA:Cholesterol:DSPC:DSPE-PEG2000-Maleimide at 50:38.5:10:1.5) with comprehensive mathematical modeling for N/P ratio, encapsulation efficiency (EE%), and Pearson Correlation Coefficient (PCC). 3. Experimental Preclinical Protocols: Rigorously defined standardized in vitro cellular uptake and in vivo mouse model protocols (0.5 mg/kg systemic tail-vein administration over 3, 7, 14, and 28-day timeframes) for evaluating gene knockout efficiency (Indel %). 4. Data Scope Clarification: Full distinction between computational/in silico predictions and recommended standardized preclinical assay workflows. Keywords: CRISPR/Cas9, Aptamer, Lipid Nanoparticles (LNP), EGFR, Molecular Docking, Gene Therapy, Non-Small Cell Lung Cancer (NSCLC). Authors & Affiliation: Hosein Hoseini et al., Department of Cell and Molecular Biology, Faculty of Science, Payame Noor University, Lorestan, Iran.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-12
DOI
https://doi.org/10.5281/zenodo.22721540
Primary Topic
CRISPR and Genetic Engineering
Type
preprint
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preprint

In Silico Design and Molecular Docking Analysis of a Novel RNA Aptamer Targeting EGFR Extracellular Domain

Hosein Hoseini
Zenodo (CERN European Organization for Nuclear Research)
CRISPR and Genetic Engineering
preprint

In Silico Design and Molecular Docking Analysis of a Novel RNA Aptamer Targeting EGFR Extracellular Domain

Hosein Hoseini
preprint en

Abstract

Title: In Silico Design and Molecular Docking Analysis of a Novel RNA Aptamer Targeting EGFR Extracellular Domain for CRISPR/Cas9 Delivery Abstract & Project Overview: This study presents a targeted, non-viral gene therapy approach for non-small cell lung cancer (NSCLC) by utilizing an EGFR-targeting RNA aptamer functionalized onto ionizable lipid nanoparticles (Apt-LNP) for precise CRISPR/Cas9 delivery. Key Highlights & Updates in this Revision: 1. Computational & In Silico Validation: Detailed molecular docking analysis using HDOCK demonstrating high structural binding affinity (docking score: -338.22) between the RNA aptamer and the EGFR extracellular domain. 2. Formulation & Biophysical Characterization: Standardized molar ratios for LNP formulation (DLin-MC3-DMA:Cholesterol:DSPC:DSPE-PEG2000-Maleimide at 50:38.5:10:1.5) with comprehensive mathematical modeling for N/P ratio, encapsulation efficiency (EE%), and Pearson Correlation Coefficient (PCC). 3. Experimental Preclinical Protocols: Rigorously defined standardized in vitro cellular uptake and in vivo mouse model protocols (0.5 mg/kg systemic tail-vein administration over 3, 7, 14, and 28-day timeframes) for evaluating gene knockout efficiency (Indel %). 4. Data Scope Clarification: Full distinction between computational/in silico predictions and recommended standardized preclinical assay workflows. Keywords: CRISPR/Cas9, Aptamer, Lipid Nanoparticles (LNP), EGFR, Molecular Docking, Gene Therapy, Non-Small Cell Lung Cancer (NSCLC). Authors & Affiliation: Hosein Hoseini et al., Department of Cell and Molecular Biology, Faculty of Science, Payame Noor University, Lorestan, Iran.

Zenodo (CERN European Organization for Nuclear Research)
CRISPR and Genetic Engineering
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