ArticleBiophysical journal2017
Bridging Microscopic and Macroscopic Mechanisms of p53-MDM2 Binding with Kinetic Network Models.
Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
38 citing papers in PubMed, 93 citations in OpenAlex.
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- Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides.Journal of chemical theory and computation · 2024Article
- Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides.bioRxiv : the preprint server for biology · 2024Article
- Markov State Model of Solvent Features Reveals Water Dynamics in Protein-Peptide Binding.The journal of physical chemistry. B · 2023Article
- A Kinetic Transition Network Model Reveals the Diversity of Protein Dimer Formation Mechanisms.Biomolecules · 2023Article
- Folding@home: Achievements from over 20 years of citizen science herald the exascale era.Biophysical journal · 2023Review
- Binding-and-Folding Recognition of an Intrinsically Disordered Protein Using Online Learning Molecular Dynamics.Journal of chemical theory and computation · 2023Article
- Predicting Biomolecular Binding Kinetics: A Review.Journal of chemical theory and computation · 2023Review
- Structure Determination of Challenging Protein-Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2023Article
- Folding@home: achievements from over twenty years of citizen science herald the exascale era.ArXiv · 2023Article
- Computational approaches for the design of modulators targeting protein-protein interactions.Expert opinion on drug discovery · 2023Review
- Markov State Study of Electrostatic Channeling within the Tricarboxylic Acid Cycle Supercomplex.ACS nanoscience Au · 2022Article
- A litmus test for classifying recognition mechanisms of transiently binding proteins.Nature communications · 2022Article
- Estimation of binding rates and affinities from multiensemble Markov models and ligand decoupling.The Journal of chemical physics · 2022Article
- Potential of rescue and reactivation of tumor suppressor p53 for cancer therapy.Biophysical reviews · 2022Review
- Unprotected peptide macrocyclization and stapling via a fluorine-thiol displacement reaction.Nature communications · 2022Article
- Modelling peptide-protein complexes: docking, simulations and machine learning.QRB discovery · 2022Article
- Towards rational computational peptide design.Frontiers in bioinformatics · 2022Article
- Induced fit versus conformational selection: From rate constants to fluxes… and back to rate constants.Pharmacology research & perspectives · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Under normal cellular conditions, the tumor suppressor protein p53 is kept at low levels in part due to ubiquitination by MDM2, a process initiated by binding of MDM2 to the intrinsically disordered transactivation domain (TAD) of p53. Many experimental and simulation studies suggest that disordered domains such as p53 TAD bind their targets nonspecifically before folding to a tightly associated conformation, but the microscopic details are unclear. Toward a detailed prediction of binding mechanisms, pathways, and rates, we have performed large-scale unbiased all-atom simulations of p53-MDM2 binding. Markov state models (MSMs) constructed from the trajectory data predict p53 TAD binding pathways and on-rates in good agreement with experiment. The MSM reveals that two key bound intermediates, each with a nonnative arrangement of hydrophobic residues in the MDM2 binding cleft, control the overall on-rate. Using microscopic rate information from the MSM, we parameterize a simple four-state kinetic model to 1) determine that induced-fit pathways dominate the binding flux over a large range of concentrations, and 2) predict how modulation of residual p53 helicity affects binding, in good agreement with experiment. These results suggest new ways in which microscopic models of peptide binding, coupled with simple few-state binding flux models, can be used to understand biological function in physiological contexts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.