Molecular Dynamics Simulation of Hyperphosphorylated Tau Protein with Potential Ligands from Drug Repurposing

EasyChair Preprint 15931

Abstract

Background: Alzheimer’s disease(AD) is a condition characterised by cognitive dysfunction, mental symptoms and behavioural abnormalities. Clinical failures of amyloid beta-based treatments necessitate alternative approaches in developing new therapies for AD. Hyperphosphorylated tau protein(pTau) involvement in neurofibrillary tangle production in AD disease process opens new doors for AD drug discovery. This research aims to investigate if asenapine, paliperidone and pentazocine could be repurposed as pTau aggregation inhibitors by performing molecular dynamics(MD) simulations.

Methodology: MD simulations were performed using AMBER. Minimisation was performed followed by heating and NVT equilibration runs, and finally 20ns of NPT production phase. Trajectories were analysed by using CPPTRAJ.

Results: All 3 MD simulations had stable thermodynamic properties such as energy, temperature, pressure, volume and density. Asenapine showed the lowest root mean square deviation for entire system(7-10Å) and binding site(1-2Å), as compared to paliperidone and pentazocine. Asenapine also showed an overall lowest root mean square fluctuation value. Hydrogen bond analysis showed asenapine formed 46 hydrogen bonds. Only 5 hydrogen bonds formed in paliperidone. No hydrogen bond formed in pentazocine.

Conclusion: Asenapine is the most promising candidate among the 3 drugs as pTau aggregation inhibitor for AD due to its stability and the highest number of hydrogen bond formation.

Keyphrases: Alzheimer's disease, Hyperphosphorylated Tau Protein, MD simulation, Molecular dynamics simulation, drug discovery, pTau aggregation inhibitor

@booklet{EasyChair:15931,
  author    = {Yih Jeng Cheong and Wai Keat Yam},
  title     = {Molecular Dynamics Simulation of Hyperphosphorylated Tau Protein with Potential Ligands from Drug Repurposing},
  howpublished = {EasyChair Preprint 15931},
  year      = {EasyChair, 2025}}