Computational identification of potential PAK1 inhibitors for anti-cancer therapy: an e-pharmacophore guided virtual

M Muthuvairam Subbulakshmi1, H Nagarajan1, S Pandi2,3

  • 1Structural Biology and Biocomputing Lab, Department of Bioinformatics, Alagappa University, Karaikudi, India.

Insights

Researchers identified potential cancer drug candidates targeting the PAK1 protein. Computational methods screened databases, revealing five compounds with favorable properties for further development against cancer.

Area of Science:

  • Computational chemistry and drug discovery
  • Molecular modeling and bioinformatics

Background:

  • Cancer is a significant global health issue, with millions of new cases annually.
  • The PAK1 protein is recognized as a key regulator in cancer progression, presenting a viable therapeutic target.

Purpose of the Study:

  • To identify novel small molecules that can inhibit the PAK1 protein.
  • To computationally evaluate the drug-likeness and binding potential of identified compounds.

Main Methods:

  • Downloaded and prepared the PAK1-dibenzodiazepine complex structure (PDB ID: 4ZLO).
  • Utilized Schrödinger suite for pharmacophore modeling and docking-based virtual screening of ZINC and Enamine databases.
  • Assessed compound properties using MM/GBSA, Qikprop, DFT, molecular dynamics (MD), and essential dynamics (ED) analyses.

Main Results:

  • Identified five potential PAK1 inhibitors (ZINC952869440, ZINC952869442, ENAMINE558, ENAMINE6304, ENAMINE8429).
  • Compounds exhibited favorable docking and MM/GBSA scores, adhered to drug-likeness rules (RO5), and showed suitable pharmacokinetic profiles.
  • DFT, MD, and ED studies confirmed the electronic stability and structural integrity of the ligand-protein complexes.

Conclusions:

  • The study computationally identified promising PAK1 inhibitors for cancer therapy.
  • These compounds demonstrate favorable drug-like properties and binding stability, warranting experimental validation.