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Peer-reviewedCC BY 4.0PLOS One

Acute Myeloid Leukemia(AML) proteins for in silico drug design using pharmacophore modeling, molecular docking and molecular dynamics simulation

  • Partho Bosu1
  • Tonmoy Adhikary2
  • Abu Sayed Rafi3
  • Naveed Iqbal4
  • Fatima Rasool5
  • Payer Ahmed6
6 affiliations
  1. 1Department of Mathematics, National Institute of Technology, Durgapur, West Bengal, India
  2. 2Department of Mathematics, Jashore University of Science and Technology, Jashore, Bangladesh
  3. 3Department of Mathematics, Jagannath University, Dhaka, Bangladesh
  4. 4Department of Textile Engineering, University of Scholars, Dhaka, Bangladesh
  5. 5Department of Textile Engineering, Dhaka University of Engineering and Technology, Gazipur, Bangladesh
  6. 6Department of Textile Engineering, Jashore University of Science and Technology, Jashore, Bangladesh
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Abstract

Acute Myeloid Leukemia (AML) is a hematological malignancy characterized by abnormal myeloid cell differentiation, disrupting normal hematopoiesis. This study utilized an integrated computational approach to identify key molecular targets and potential inhibitors for AML therapy. Analysis of the GSE9476 microarray dataset revealed 566 differentially expressed genes (DEGs), including 437 upregulated and 129 downregulated genes. Functional enrichment indicated significant roles in immune response, apoptosis regulation, and hematopoietic pathways. Protein-protein interaction (PPI) network analysis identified ten hub genes—CD44, TNF, IL1B, MYC, STAT1, LCK, CCR7, FCGR3B, MMP9, and CD28—implicated in AML pathogenesis. After examining these ten hub genes’ resolution, R-factor, Diffraction Component Precision Index (DPI), least overfitting criteria, and other crystallographic features, we concluded that LCK and MMP9 were the best options for our future research. Pharmit was used to construct and validate an areceptor ligand pharmacophore model based on the crystal structures of LCK (PDB ID: 6PDJ) and MMP9 (PDB ID: 1GKC). Receptor-based pharmacophore models for LCK and MMP9 with AUC values of 0.79 and 0.85, respectively, were constructed and validated by virtual screening. The filtered compounds underwent ADME/toxicity profiling before being precisely docked in the MMP9 and LCK, which was automated using Python scripts, in order to optimize the screening’s dependability. Among all screened hits, favorable binding affinity values of −9.7 and −9.9 kcal/mol for CID-56715853 and CID-126445459 with LCK and −8.9 and −9.2 kcal/mol for MCULE-1367186142 and ZINC4896454 with MMP9, respectively, showed significant binding interactions. Molecular dynamics analyses, including RMSD, RMSF, PCA, and MM-GBSA, confirmed complex stability and binding affinity. This comprehensive in-silico framework integrates transcriptomic data, network analysis, pharmacophore modeling, and molecular simulations to identify novel biomarkers and drug candidates for AML, providing a foundation for further experimental validation and therapeutic development. These four hits (CID-56715853, CID-126445459 MCULE-1367186142 and ZINC4896454) are potential candidates for further in vitro and in vivo validation.

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Cite

BibTeXRIS
BibTeX
@article{bosu2026acute,
  title = {{Acute Myeloid Leukemia(AML) proteins for in silico drug design using pharmacophore modeling, molecular docking and molecular dynamics simulation}},
  author = {Partho Bosu and Tonmoy Adhikary and Abu Sayed Rafi and Naveed Iqbal and Fatima Rasool and Payer Ahmed},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0354456},
  url = {https://doi.org/10.1371/journal.pone.0354456},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Acute Myeloid Leukemia(AML) proteins for in silico drug design using pharmacophore modeling, molecular docking and molecular dynamics simulation
AU  - Partho Bosu
AU  - Tonmoy Adhikary
AU  - Abu Sayed Rafi
AU  - Naveed Iqbal
AU  - Fatima Rasool
AU  - Payer Ahmed
PY  - 2026
DA  - 2026/09/02
JO  - PLOS One
DO  - 10.1371/journal.pone.0354456
UR  - https://doi.org/10.1371/journal.pone.0354456
AB  - Acute Myeloid Leukemia (AML) is a hematological malignancy characterized by abnormal myeloid cell differentiation, disrupting normal hematopoiesis. This study utilized an integrated computational approach to identify key molecular targets and potential inhibitors for AML therapy. Analysis of the GSE9476 microarray dataset revealed 566 differentially expressed genes (DEGs), including 437 upregulated and 129 downregulated genes. Functional enrichment indicated significant roles in immune response, apoptosis regulation, and hematopoietic pathways. Protein-protein interaction (PPI) network analysis identified ten hub genes—CD44, TNF, IL1B, MYC, STAT1, LCK, CCR7, FCGR3B, MMP9, and CD28—implicated in AML pathogenesis. After examining these ten hub genes’ resolution, R-factor, Diffraction Component Precision Index (DPI), least overfitting criteria, and other crystallographic features, we concluded that LCK and MMP9 were the best options for our future research. Pharmit was used to construct and validate an areceptor ligand pharmacophore model based on the crystal structures of LCK (PDB ID: 6PDJ) and MMP9 (PDB ID: 1GKC). Receptor-based pharmacophore models for LCK and MMP9 with AUC values of 0.79 and 0.85, respectively, were constructed and validated by virtual screening. The filtered compounds underwent ADME/toxicity profiling before being precisely docked in the MMP9 and LCK, which was automated using Python scripts, in order to optimize the screening’s dependability. Among all screened hits, favorable binding affinity values of −9.7 and −9.9 kcal/mol for CID-56715853 and CID-126445459 with LCK and −8.9 and −9.2 kcal/mol for MCULE-1367186142 and ZINC4896454 with MMP9, respectively, showed significant binding interactions. Molecular dynamics analyses, including RMSD, RMSF, PCA, and MM-GBSA, confirmed complex stability and binding affinity. This comprehensive in-silico framework integrates transcriptomic data, network analysis, pharmacophore modeling, and molecular simulations to identify novel biomarkers and drug candidates for AML, providing a foundation for further experimental validation and therapeutic development. These four hits (CID-56715853, CID-126445459 MCULE-1367186142 and ZINC4896454) are potential candidates for further in vitro and in vivo validation.
SN  - 1932-6203
ER  -