Volume 6, Issue 1 (Avicenna Journal of Pharmaceutical Research, Jun 2025)
Avicenna J Pharma Res 2025, 6(1): 15-26 |
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Fahimi Y, Dastan D, Ebadi A. Integrating Fragment-Based Design, Machine Learning, and Molecular Dynamics to Identify Selective Anticancer Peptides Targeting 14-3-3σ. , Avicenna J Pharma Res. 2025; 6 (1) :15-26
URL: http://ajpr.umsha.ac.ir/article-1-110-en.html
URL: http://ajpr.umsha.ac.ir/article-1-110-en.html
1- 1 Department of Medicinal Chemistry, School of Pharmacy, Medicinal Plants and Natural Products Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
2- 2 Department of Pharmacognosy, School of Pharmacy, Medicinal Plants and Natural Products Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
2- 2 Department of Pharmacognosy, School of Pharmacy, Medicinal Plants and Natural Products Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
Abstract: (8 Views)
Background: The modulation of disease-relevant protein–protein interactions (PPIs) using small-molecule inhibitors is an evolving strategy in drug discovery. The 14-3-3 protein family engages in nearly 700 PPIs, making its members attractive therapeutic targets in a wide range of diseases. Among them, 14-3-3σ is closely associated with cancer initiation and progression, and its overexpression has been reported in gallbladder, liver, and breast cancers. The highly conserved amphipathic groove of 14-3-3σ is essential for mediating PPIs involved in cell-cycle regulation. Anticancer peptides (ACPs) have therefore emerged as promising PPI modulators for the treatment of related malignancies.
Methods: In the present study, we designed a library of 1,059,049 putative ACPs targeting 14-3-3σ using a fragment-based peptide design strategy integrated with a machine-learning (ML) predictor. A total of 198 high-scoring peptides were docked into the amphipathic groove of 14-3-3σ. The selected peptides were synthesized via solid-phase peptide synthesis and characterized by RP-HPLC and LC–MS.
Results: Subsequent molecular docking, long coarse-grained molecular dynamics (CG-MD), and all-atom MD simulations identified two hexapeptides, p8 (FRRVFN) and p9 (FRRWRN), with the strongest binding energies (−70.7 and −80.3 kcal.mol⁻¹, respectively). In vitro cytotoxicity assays against MCF-7 cells demonstrated potent anticancer activity, with IC₅₀ values of 127.1±9.3 µM for p8 and 12.3±1.7 µM for p9.
Conclusion: Overall, this study demonstrates that integrating fragment-based peptide design with ML predictions and MD simulations provides an efficient and reliable strategy for identifying potent ACPs targeting 14-3-3σ.
Methods: In the present study, we designed a library of 1,059,049 putative ACPs targeting 14-3-3σ using a fragment-based peptide design strategy integrated with a machine-learning (ML) predictor. A total of 198 high-scoring peptides were docked into the amphipathic groove of 14-3-3σ. The selected peptides were synthesized via solid-phase peptide synthesis and characterized by RP-HPLC and LC–MS.
Results: Subsequent molecular docking, long coarse-grained molecular dynamics (CG-MD), and all-atom MD simulations identified two hexapeptides, p8 (FRRVFN) and p9 (FRRWRN), with the strongest binding energies (−70.7 and −80.3 kcal.mol⁻¹, respectively). In vitro cytotoxicity assays against MCF-7 cells demonstrated potent anticancer activity, with IC₅₀ values of 127.1±9.3 µM for p8 and 12.3±1.7 µM for p9.
Conclusion: Overall, this study demonstrates that integrating fragment-based peptide design with ML predictions and MD simulations provides an efficient and reliable strategy for identifying potent ACPs targeting 14-3-3σ.
Keywords: 14-3-3σ, Anticancer peptide, Fragment-based peptide design, Machine Learning, MD simulation, Protein-protein interaction
Type of Study: Research |
Subject:
Special
Received: 2026/10/31 | Accepted: 2025/09/1 | Published: 2026/10/31
Received: 2026/10/31 | Accepted: 2025/09/1 | Published: 2026/10/31
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