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Updated: Jan 7, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
Published on: October 23, 2019
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.
Abstract:
Cancer remains a major global health challenge, with approximately 18 million new cases reported annually. Existing evidence highlights the PAK1 protein as a critical regulator of cancer progression, making it a promising therapeutic target. The PAK1 protein complexed with dibenzodiazepine was fetched from the PDB with the identifier 4ZLO. The structure was preprocessed through preparation and exposed to pharmacophore hypotheses on the Schrödinger suite programme, indicating key features of RRH. A multi-tiered docking-based screening workflow from the libraries of ZINC and Enamine databases identified five potential bioactive compounds: ZINC952869440, ZINC952869442, ENAMINE558, ENAMINE6304, and ENAMINE8429. The docking and MM/GBSA scores ranked from -5.02 to -8.34 kcal/mol and -46.10 to -50.41 kcal/mol. Remarkably, none of these candidates violated the rules of five, and the Qikprop parameters complied with pharmacokinetic suitability. The DFT analysis revealed energy gap scores ranged from -0.182 to -0.225 eV, indicating favourable electronic properties and stability of the ligands. Furthermore, molecular dynamics (MD) and essential dynamics (ED) studies validated the structural stability of the complexes. The secondary structure analysis indicated stable retention of α-helices and β-strands throughout the simulation. Moreover, the computational investigation identified potential PAK1 inhibitors that warrant further experimental testing and therapeutic development.
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.
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