Bridging microscopic and macroscopic mechanisms of p53-MDM2 binding using molecular simulations and kinetic network models

Abstract

Under normal cellular conditions, the tumor suppressor protein p53 is kept at a 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. Although many experimental and simulation studies suggest that disordered domains such as p53 TAD bind their targets nonspecifically before folding to a tightly-associated conformation, the molecular details are unclear. Toward a detailed prediction of binding mechanism, 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 bound-state ensembles can be used to understand biological function on a macroscopic scale. AUTHOR SUMMARYMany cell signaling pathways involve protein-protein interactions in which an intrinsically disordered peptide folds upon binding its target. Determining the molecular mechanisms that control these binding rates is important for understanding how such systems are regulated. In this paper, we show how extensive all-atom simulations combined with kinetic network models provide a detailed mechanistic understanding of how tumor suppressor protein p53 binds to MDM2, an important target of new cancer therapeutics. A simple fourstate model parameterized from the simulations shows a binding-then-folding mechanism, and recapitulates experiments in which residual helicity boosts binding. This work goes beyond previous simulations of small-molecule binding, to achieve pathways and binding rates for a large peptide, in good agreement with experiment.

Computational and Experimental Evaluation of Designed β-Cap Hairpins Using Molecular Simulations and Kinetic Network Models

“…While for the moment MSM approaches are more expensive (especially when used with explicit solvent), we expect that in the future MSM adaptive sampling techniques, , ever-faster molecular simulation hardware (e.g., GPUs), and improved force field potentials will make these approaches accurate and efficient for the purpose of design. With the recent emergence of MSMs studies that explicitly model protein–ligand binding, − another exciting application of MSMs will be to use models of coupled folding and binding to guide the design of high-affinity peptidomimetics. New methods to construct MSMs from biased ensembles and efficiently predict how mutations and chemical modifications will perturb binding kinetics , will likely make these approaches possible in the next few years.…”

Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules

“…29,[109][110][111] In TPT, once the initial and final states are defined, net fluxes between all pairs of states are computed based on transition probabilities, committor probabilities as well as equilibrium populations of states, and transition pathways can then be identified from the net flux matrix. TPT has been implemented to investigate various binding processes, 43,112 polymerase systems, 2 selfassembly processes, 113 and so forth. MFPT, which quantifies the averaged time for a state to first make a transition to another state, can also be used to characterize the kinetics of the system.…”