AI screening and molecular dynamic simulation-driven identification of novel inhibitors of TGFßR1 for pancreatic

Samvedna Singh1, Kiran Bharat Lokhande2, Aman Chandra Kaushik3

  • 1School of Biotechnology, Gautam Buddha University, Greater Noida, UP, India.

PubMed

Insights

Researchers identified potent small molecule inhibitors for TGFßR1 to combat deadly pancreatic cancer. Compounds 4 and 5 showed exceptional binding, offering new therapeutic potential for pancreatic cancer treatment.

Area of Science:

  • Computational chemistry and drug discovery
  • Oncology and molecular biology

Background:

  • Pancreatic cancer has a low survival rate, necessitating novel therapeutic strategies.
  • The TGF-ß pathway is implicated in pancreatic cancer, making TGFßR1 a key drug target.
  • Previous TGF-β targeting trials faced efficacy and toxicity issues, requiring improved inhibitors.

Purpose of the Study:

  • To identify novel small molecule inhibitors targeting TGFßR1 for pancreatic cancer.
  • To computationally evaluate the binding affinity and stability of potential inhibitors.

Main Methods:

  • Utilized AI-driven screening and structure-based high-throughput virtual screening (HTVS) on compound libraries.
  • Performed pharmacokinetic profiling, molecular dynamics (MD) simulations, and binding energy analysis (MM-GBSA).
  • Analyzed key residues, receptor dynamics (PCA, DCCM), and ligand-protein interactions.

Main Results:

  • Identified five hit compounds with stable complexes formed with TGFßR1.
  • Compounds 4 and 5 demonstrated exceptionally strong binding energies (-81.0 kcal/mol and -85.5 kcal/mol, respectively).
  • Key interactions involving residues HIS283, ASP351, LYS232, SER280, ILE211, and LYS213 were identified.

Conclusions:

  • Compounds 4 and 5 are promising TGFßR1 inhibitors with potential for pancreatic cancer therapy.
  • The study validates a computational approach for identifying effective small molecule inhibitors.