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

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.

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Publication Details

Journal
SAR and QSAR in environmental research
Published
2025-12-02
DOI
https://doi.org/10.1080/1062936x.2025.2595472
Primary Topic
Protein Kinase Regulation and GTPase Signaling
Type
article
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article

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

Jeyaraman Jeyakanthan, Siva Bala Subramaniyan, Maharaja Muthuvairam Subbulakshmi, H. Nagarajan et al.
SAR and QSAR in environmental research
Protein Kinase Regulation and GTPase Signaling
article

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

Jeyaraman Jeyakanthan, Siva Bala Subramaniyan, Maharaja Muthuvairam Subbulakshmi, H. Nagarajan, Samuthira Pandi, T. Berchmans
article en

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.

SAR and QSAR in environmental researchVol. 36(12)
Alagappa University (IN), Karpagam Academy of Higher Education (IN)
Openalex Percentile: Top 63%
Protein Kinase Regulation and GTPase Signaling
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