Protein phosphatase 2A containing the B56δ regulatory subunit (PP2A-B56δ) is a critical signaling enzyme whose dysregulation is associated with cancer, neurodegenerative disorders, and Jordan’s syndrome, a severe intellectual disability disorder caused by mutations in B56δ. Unlike other PP2A holoenzymes, PP2A-B56δ is regulated through a unique dual autoinhibition mechanism in which the N- and C-arms occlude the catalytic site while a substrate-mimicking short linear motif (SLiM) blocks the substrate-binding pocket. Although disease-associated mutations have been shown to alter enzyme activity, the molecular mechanism underlying activation of PP2A-B56δ and the effects of pathogenic mutations remain poorly understood. Here, we combined cryo-electron microscopy (cryo-EM), enhanced-sampling molecular dynamics (MD) simulations, Markov state model (MSM) construction, and transition-state analysis using Transition State identification via Dispersion and vAriational principle Regularized neural networks (TS-DAR) to characterize the conformational landscape of the disease variant E198K. Our cryo-EM analysis identified two distinct structures of E198K: an inactive closed-form with the N/C-arms resolved and an active loose-form in which the N/C-arms become highly flexible and could not be fully resolved. These structures therefore established that activation is governed by conformational changes of the N/C-arms but did not reveal the underlying mechanism. Starting from the inactive closed-form, we generated over 1,600 trajectories with an average length of 1,260 ns combined for E198K and wild-type (WT) PP2A-B56δ. TS-DAR identified four metastable states and two major activation pathways connecting inactive and active conformations. We found that activation occurs through progressive loosening of the N/C-arm interface while maintaining the overall holoenzyme architecture, rather than a complete opening of the interface. This mechanism exposes both the catalytic site and substrate-binding pocket. Comparison of E198K and WT revealed that the disease-associated mutation shifts the conformational equilibrium toward active states while leaving the transition-state ensemble largely unchanged. Mechanistically, E198K disrupts a salt-bridge network and weakens interactions between the internal loop and the C-arm that normally stabilize active-site occlusion. The resulting increase in C-arm mobility promotes active-site exposure and explains the elevated catalytic activity of the mutant. Together, these findings establish a previously uncharacterized activation mechanism for PP2A-B56δ and provide an atomic-level explanation for how the pathogenic E198K mutation allosterically promotes holoenzyme activation.
Michael S O'Connor, Cheng-Guo Wu, Yichong Lao et al.· bioRxiv· 0 citations
Many biomolecular processes are governed by rare transitions between metastable states on complex free-energy landscapes. The committor function, the probability that a trajectory initiated from a given configuration reaches one metastable state before another, is regarded as the optimal reaction coordinate for describing such transitions. Committor functions are often computed using Transition Path Theory (TPT) combined with Markov State Models (MSMs), which discretize configuration space into states and, therefore, limit spatial resolution. Here, we introduce an analytical framework for computing continuous-space committor functions directly from molecular dynamics trajectories using a Liouville propagator formulated in a basis-function representation. The key insight is that the slow eigenmodes of the propagator can be represented as linear combinations of basis functions, yet the resulting committor equations do not depend explicitly on the unknown expansion coefficients. Consequently, the Liouville propagator and committor functions can be constructed directly from an arbitrary set of basis functions without explicitly solving for eigenfunctions. This formulation enables continuous committor functions, iso-committor surfaces, and other kinetic quantities such as mean first passage times to be computed without discretizing configuration space. Applications to a two-dimensional model potential, alanine dipeptide, and the FIP35 WW domain demonstrate that the resulting committor functions agree with MSM-TPT results while providing substantially higher spatial resolution, enabling precise identification of transition states. Because our theoretical framework accommodates general basis representations, including polynomial or Fourier expansions of physical coordinates, tensor-based collective variables (CVs), and machine-learning-derived CVs, it provides a versatile foundation for analyzing dynamics in biomolecular systems and other complex dynamical processes.
Siqin Cao, Xuhui Huang· Journal of Chemical Physics· 0 citations