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Mechanistic insight into the binding between Ferritin and Serotonin: Possible implications in neurodegenerative diseases

  • Farah Anjum
  • , Moyad Shahwan
  • , Fahad A. Alhumaydhi
  • , Sharaf E. Sharaf
  • , Waleed Al Abdulmonem
  • , Alaa Shafie
  • , Anwar L. Bilgrami
  • , Anas Shamsi*
  • , Ghulam Md Ashraf
  • *Corresponding author for this work
  • Taif University
  • Ajman University
  • Qassim University
  • Umm Al-Qura University
  • Makkah Health Cluster
  • King Abdulaziz University
  • Jamia Millia Islamia
  • Pre-Clinical Research Unit
  • Department of Medical Laboratory Sciences

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Ferritin is considered to be a crucial iron-binding protein associated with Alzheimer's disease (AD). The computational approaches, such as molecular docking, molecular dynamics (MD) simulations and principal component analysis (PCA), are helpful to explore potent compounds with predefined targets such as Ferritin in the modern drug discovery pipeline. In this study, the molecular docking calculations revealed that Serotonin has an appreciable binding potential towards Ferritin by forming stable hydrogen bonds (H-bonds). In MD simulation, the analyses of RMSD, RMSF, Rg, SASA, H-bonds, and PCA reveal that the Ferritin-Serotonin docked complex is stable throughout the trajectory. Likewise, the free energy landscape (FEL) analysis exposed that the Ferritin-Serotonin complex stabilized to the global minimum. Overall, the results of this study contribute in numerous aspects to our understanding of Ferritin activity and provide an essential insight into the binding prototype of Serotonin as a potential binding partner of Ferritin to be explored in AD after required experimentation.

Original languageEnglish
Article number118618
JournalJournal of Molecular Liquids
Volume351
DOIs
Publication statusPublished - 1 Apr 2022
Externally publishedYes

Keywords

  • Ferritin
  • Free energy landscape
  • MD simulations
  • Molecular docking
  • Principal component analysis
  • Serotonin

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