10.57647/ijm2c.2025.150421

Advanced Computational Design of Targeted Anticancer Peptides Using Docking, Molecular Dynamics, and Deep Learning

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  • Seyedeh Fatemeh Ahmadi1,
  • Hamidreza Samadikhah*1,
  • Seyed Shahriar Arab2,
  1. Department of Biology, CT.C. Islamic Azad University, Tehran, Iran.
  2. Center for Molecular Medicine, University of Georgia, Athens, USA.

Received: 05-05-2025

Revised: 12-07-2025

Accepted: 26-07-2025

Published in Issue 28-07-2025

Copyright (c) 2025 Seyedeh Fatemeh Ahmadi, Hamidreza Samadikhah, Seyed Shahriar Arab (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Ahmadi, S. F., Samadikhah, H., & Arab, S. S. (2025). Advanced Computational Design of Targeted Anticancer Peptides Using Docking, Molecular Dynamics, and Deep Learning. International Journal of Mathematical Modelling & Computations, 15(4). https://doi.org/10.57647/ijm2c.2025.150421
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Abstract

The computational design of peptides is increasingly employed in anticancer drug development, particularly by targeting protein–protein interactions (PPIs). In this study, we utilized structure-based modeling integrated with deep learning approaches, including AlphaFold, to accurately predict peptide structures. The anticancer peptides were designed as conjugates with cell-penetrating peptides (CPPs) to enhance cellular uptake.YASARA was used for structural inspection, and its FoldX plugin enabled the introduction of rational mutations and energy minimization. ChimeraX and AlphaFold were further employed for visualization and structural modeling of the anticancer peptides. The designed peptides were thoroughly evaluated using molecular docking, molecular dynamics (MD) simulations with GROMACS, and binding energy calculations via gmx_MMPBSA. Notably, the mutated peptide demonstrated a significantly improved binding affinity and structural stability compared to the nonmutated peptide, underscoring its potential therapeutic value. Despite the inherent computational complexity, our approach highlights the effectiveness of in silico peptide engineering for developing targeted anticancer therapeutics. Specifically, the designed peptide targets survivin, a member of the inhibitor of apoptosis (IAP) family, which is frequently overexpressed in cancer cells and plays a critical role in tumor progression and therapy resistance. 

Keywords

  • Computational peptide design,
  • Anticancer peptide optimization,
  • Molecular dynamics simulation,
  • Protein-protein docking,
  • Deep learning in structural biology,
  • Mathematical modeling of survivin inhibition,
  • Free energy calculations (MM/PBSA)
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References

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