Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer.

Journal: Scientific reports
Published Date: Jun 10, 2024

Abstract

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Authors

  • Anuraj Nayarisseri
    In silico Research Laboratory, Eminent Biosciences, Mahalakshmi Nagar, Indore - 452010, Madhya Pradesh, India.
  • Mohnad Abdalla
    Pediatric Research Institute, Children's Hospital, Shandong University, Jinan 250022, China.
  • Isha Joshi
    In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
  • Manasi Yadav
    In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
  • Anushka Bhrdwaj
    In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
  • Ishita Chopra
    In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
  • Arshiya Khan
    In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
  • Arshiya Saxena
    In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
  • Khushboo Sharma
    In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
  • Aravind Panicker
    In silico Research Laboratory, Eminent Biosciences, 91, Sector-A, Mahalakshmi Nagar, Indore, Madhya Pradesh, 452010, India.
  • Umesh Panwar
    Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu, 630003, India.
  • Francisco Jaime Bezerra Mendonça Junior
    Laboratory of Synthesis and Drug Delivery, Department of Biological Sciences, State University of Paraiba, João Pessoa, 58429-500, Brazil.
  • Sanjeev Kumar Singh
    Computer Aided Drug Designing and Molecular Modeling Lab, Department of Bioinformatics, Alagappa University, Karaikudi-630003, Tamil Nadu, India.

Keywords

External Resources

Popular Topics
Recent Journals