Proteasomal degradation of ubiquitylated proteins is tightly regulated at multiple levels^1–3. A primary regulatory checkpoint is the removal of ubiquitin chains from substrates by the deubiquitylating enzyme ubiquitin-specific protease 14 (USP14), which reversibly binds the proteasome and confers the ability to edit and reject substrates. How USP14 is activated and regulates proteasome function remain unknown^4–7. Here we present high-resolution cryo-electron microscopy structures of human USP14 in complex with the 26S proteasome in 13 distinct conformational states captured during degradation of polyubiquitylated proteins. Time-resolved cryo-electron microscopy analysis of the conformational continuum revealed two parallel pathways of proteasome state transitions induced by USP14, and captured transient conversion of substrate-engaged intermediates into substrate-inhibited intermediates. On the substrate-engaged pathway, ubiquitin-dependent activation of USP14 allosterically reprograms the conformational landscape of the AAA-ATPase motor and stimulates opening of the core particle gate^8–10, enabling observation of a near-complete cycle of asymmetric ATP hydrolysis around the ATPase ring during processive substrate unfolding. Dynamic USP14–ATPase interactions decouple the ATPase activity from RPN11-catalysed deubiquitylation^11–13 and kinetically introduce three regulatory checkpoints on the proteasome, at the steps of ubiquitin recognition, substrate translocation initiation and ubiquitin chain recycling. These findings provide insights into the complete functional cycle of the USP14-regulated proteasome and establish mechanistic foundations for the discovery of USP14-targeted therapies.

The cellular functions are executed by biological macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving the conformational continuum of important biomolecular complexes at the atomic level is essential to understanding their functional mechanisms and guiding structure-based drug discovery. Here, we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-EM) reconstructions that approximately visualize the conformational space of biomolecular complexes of interest. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of pseudo-energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. In blind assessments using simulated heterogeneous datasets, AlphaCryo4D achieved 3D classification accuracy three times those of alternative methods and reconstructed continuous conformational changes of a 130-kDa protein at sub-3 Å resolution. By applying this approach to analyze several experimental datasets of the proteasome, ribosome and spliceosome, we demonstrate its potential generality in exploring hidden conformational space or transient states of macromolecular complexes that remain hitherto invisible. Integration of this approach with time-resolved cryo-EM further allows visualization of conformational continuum in a nonequilibrium regime at the atomic level, thus potentially enabling therapeutic discovery against highly dynamic biomolecular targets.

Biological processes are typically actuated by dynamic multi-subunit molecular complexes. However, interactions between subunits, which govern the functions of these complexes, are hard to measure directly. Here, we develop a general approach combining cryo-EM imaging technology and statistical modeling and apply it to study the hexameric clock protein KaiC in Cyanobacteria. By clustering millions of KaiC monomer images, we identify two major conformational states of KaiC monomers. We then classify the conformational states of (>160,000) KaiC hexamers by the thirteen distinct spatial arrangements of these two subunit states in the hexamer ring. We find that distributions of the thirteen hexamer conformational patterns for two KaiC phosphorylation mutants can be fitted quantitatively by an Ising model, which reveals a significant cooperativity between neighboring subunits with phosphorylation shifting the probability of subunit conformation. Our results show that a KaiC hexamer can respond in a switch-like manner to changes in its phosphorylation level.

Small angle x-ray scattering (SAXS) is a widely recognized solution for measuring complex nanostructures. With the increasing demand for accurately assessing structural characteristics and optimizing manufacturing processes, uncertainty quantification in SAXS inverse problems has become a critical issue. However, traditional methods face challenges such as slow computation speed and inaccurate estimation of multidimensional parameters. To overcome these issues, we propose an uncertainty quantification approach suitable for SAXS measurement that approximates the posterior using Bayesian deep learning. The effectiveness and reliability of our method are illustrated by assessing structural parameters of synthetic 2D Si grating samples. The uncertainty quantification takes only about 2.3 s, thousands of times faster than the conventional Markov Chain Monte Carlo (MCMC) methods. Also, our method has superior repeatability for parameter measurement compared to the MCMC approaches. It provides the potential of efficient and reliable SAXS measurement in increasingly intricate semiconductor manufacturing.

The circadian clock of cyanobacteria is based on an approximately 24h rhythm in the phosphorylation level of KaiC, a hexameric ATPase. This oscillation can be reconstituted in vitro by incubating three proteins together with ATP. Like all chemical oscillators, this system must include a nonlinear, or switch-like, feedback loop, whose nature has been unclear. Here, by using single particle cryo-EM at near-atomic resolution we identified two major conformational states of KaiC subunits, denoted as the exposed state and the buried state, which may provide a structural basis of how the KaiC hexamer changes its conformation during the (day-night) phosphorylation-dephosphorylation cycle. We classify the abundance and pattern of exposed and buried states within hexamers for more than 160,000 KaiC particles. The statistics of the spatial arrangement of the two states in hexamers can be quantitatively fit by a simple statistical physics model with an interaction energy between neighboring subunits and a local field that depends on phosphorylation state. Our study shows that phosphorylation shifts the probability of each conformation and reveals that there is substantial cooperativity between neighboring subunits, which can allow a KaiC hexamer to respond in an ultrasensitive, switch-like manner to changes in the phosphorylation level.

Ryanodine receptors (RyRs) are Ca ²⁺ -regulated Ca ²⁺ channels of 2.2-megadalton in muscles and neurons for calcium signaling. How Ca ²⁺ regulates ion conduction in the RyR channels remains elusive. We determined a 2.6-Å cryo-EM structure of rabbit skeletal muscle RyR1, and used multiscale dynamics simulations to elucidate cation interactions with RyR1. We investigated 21 potential cation-binding sites that may together rationalize biphasic Ca ²⁺ response of RyR1. The selectivity filter captures a cation hydration complex by hydrogen-bonding with both the inner and outer hydration shells of water molecules. Molecular dynamics simulations suggest that adjacent Ca ²⁺ ions moving in concert along ion-permeation pathway are separated by at least two cation-binding sites. Our analysis reveals that RyR1 has been evolved to favor its interactions with two hydration shells of cations.