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Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
Published on: December 16, 2013
Investigation of the MDM2-binding potential of de novo designed peptides using enhanced sampling simulations
Olanrewaju Ayodeji Durojaye1, Abeeb Abiodun Yekeen2, Mukhtar Oluwaseun Idris3
1MOE Key Laboratory of Membraneless Organelle and Cellular Dynamics, Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230027, China; School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027, China; Department of Chemical Sciences, Coal City University, Emene, Enugu State, Nigeria.
Abstract:
The tumor suppressor p53 plays a crucial role in cellular responses to various stresses, regulating key processes such as apoptosis, senescence, and DNA repair. Dysfunctional p53, prevalent in approximately 50 % of human cancers, contributes to tumor development and resistance to treatment. This study employed deep learning-based protein design and structure prediction methods to identify novel high-affinity peptide binders (Pep1 and Pep2) targeting MDM2, with the aim of disrupting its interaction with p53. Extensive all-atom molecular dynamics simulations highlighted the stability of the designed peptide in complex with the target, supported by several structural analyses, including RMSD, RMSF, Rg, SASA, PCA, and free energy landscapes. Using the steered molecular dynamics and umbrella sampling simulations, we elucidate the dissociation dynamics of p53, Pep1, and Pep2 from MDM2. Notable differences in interaction profiles were observed, emphasizing the distinct dissociation patterns of each peptide. In conclusion, the results of our umbrella sampling simulations suggest Pep1 as a higher-affinity MDM2 binder compared to p53 and Pep2, positioning it as a potential inhibitor of the MDM2-p53 interaction. Using state-of-the-art protein design tools and advanced MD simulations, this study provides a comprehensive framework for rational in silico design of peptide binders with therapeutic implications in disrupting MDM2-p53 interactions for anticancer interventions.
Insights
Researchers designed novel peptides, Pep1 and Pep2, to disrupt the MDM2-p53 interaction, a key target in cancer therapy. Pep1 demonstrated higher affinity, showing potential as an anticancer agent by inhibiting this crucial protein binding.
Area of Science:
- Computational biology
- Structural biology
- Drug discovery
Background:
- The p53 tumor suppressor is vital for cellular stress response, but its dysfunction in ~50% of human cancers drives tumor growth and treatment resistance.
- The MDM2-p53 interaction inhibits p53 activity, making it a critical target for cancer therapies.
Purpose of the Study:
- To identify novel peptide inhibitors targeting the MDM2-p53 interaction using computational methods.
- To evaluate the binding affinity and dissociation dynamics of designed peptides (Pep1, Pep2) with MDM2.
Main Methods:
- Deep learning-based protein design and structure prediction.
- All-atom molecular dynamics (MD) simulations, including steered MD and umbrella sampling.
- Structural analyses (RMSD, RMSF, Rg, SASA, PCA) and free energy landscape calculations.
Main Results:
- Designed peptides Pep1 and Pep2 formed stable complexes with MDM2.
- Molecular dynamics simulations revealed distinct dissociation pathways for p53, Pep1, and Pep2 from MDM2.
- Umbrella sampling simulations indicated that Pep1 exhibits higher binding affinity to MDM2 compared to p53 and Pep2.
Conclusions:
- Pep1 is a promising high-affinity inhibitor of the MDM2-p53 interaction.
- This study presents a computational framework for designing peptide-based inhibitors targeting protein-protein interactions for cancer therapy.
