<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Yang W</submitter><funding>Key Research and Development Project of the Shandong Province</funding><funding>Shandong Jiaotong University</funding><funding>Natural Science Foundation of Shandong Province</funding><pagination>3377</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11279683</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>29(14)</volume><pubmed_abstract>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 intera</pubmed_abstract><journal>Molecules (Basel, Switzerland)</journal><pubmed_title>Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.</pubmed_title><pmcid>PMC11279683</pmcid><funding_grant_id>2019GGX102050</funding_grant_id><funding_grant_id>ZR2021MA069</funding_grant_id><funding_grant_id>TJXZ202204</funding_grant_id><pubmed_authors>Yang W</pubmed_authors><pubmed_authors>Zhao L</pubmed_authors><pubmed_authors>Chen J</pubmed_authors><pubmed_authors>Wang J</pubmed_authors></additional><is_claimable>false</is_claimable><name>Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.</name><description>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 intera</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Jul</publication><modification>2026-04-21T03:30:23.375Z</modification><creation>2025-04-19T13:13:08.68Z</creation></dates><accession>S-EPMC11279683</accession><cross_references><pubmed>39064955</pubmed><doi>10.3390/molecules29143377</doi></cross_references></HashMap>