Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 100964009 Publication Model: Electronic Cited Medium: Internet ISSN: 1420-3049 (Electronic) Linking ISSN: 14203049 NLM ISO Abbreviation: Molecules Subsets: MEDLINE

Original Publication: Basel, Switzerland : MDPI, c1995-

Proto-Oncogene Proteins c-mdm2*/antagonists & inhibitors ; Proto-Oncogene Proteins c-mdm2*/chemistry ; Proto-Oncogene Proteins c-mdm2*/metabolism ; Molecular Dynamics Simulation* ; Peptides*/chemistry ; Peptides*/pharmacology ; Protein Binding* ; Deep Learning*; Humans ; Thermodynamics ; Binding Sites ; Normal Distribution

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and binding free energy calculations were combined together to probe the binding mechanism of non-peptide inhibitors K23 and 0Y7 and peptide ones PDI6W and PDI to MDM2. The GaMD trajectory-based DL approach successfully identified significant functional domains, predominantly located at the helixes α2 and α2', as well as the β-strands and loops between α2 and α2'. The post-processing analysis of the GaMD simulations indicated that inhibitor binding highly influences the structural flexibility and collective motions of MDM2. Calculations of molecular mechanics-generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) not only suggest that the ranking of the calculated binding free energies is in agreement with that of the experimental results, but also verify that van der Walls interactions are the primary forces responsible for inhibitor-MDM2 binding. Our findings also indicate that peptide inhibitors yield more interaction contacts with MDM2 compared to non-peptide inhibitors. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated that the piperidinone inhibitor 0Y7 shows the most pronounced impact on the free energy profiles of MDM2, with the piperidinone inhibitor demonstrating higher fluctuation amplitudes along primary eigenvectors. The hot spots of MDM2 revealed by residue-based free energy estimation provide target sites for drug design toward MDM2. This study is expected to provide useful theoretical aid for the development of selective inhibitors of MDM2 family members.

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Keywords: Gaussian-accelerated dynamics simulations; MDM2; binding free energy; deep learning; peptide inhibitors

EC 2.3.2.27 (Proto-Oncogene Proteins c-mdm2)
0 (Peptides)
EC 2.3.2.27 (MDM2 protein, human)

Date Created: 20240727 Date Completed: 20240728 Latest Revision: 20240729

20240729

PMC11279683

10.3390/molecules29143377

39064955