New York Structural Biology Center

Insulin degrading enzyme (IDE) is a dimeric M16A zinc metalloprotease that degrades amyloidogenic peptides diverse in shape and sequence, including insulin and amyloid-β, to prevent toxic amyloid fibril formation. IDE has a hollow catalytic chamber formed by two ∼55 kDa N- and C-domains (IDE-N and IDE-C, respectively), in which peptides bind, unfold, and are repositioned for proteolysis. IDE is known to transition between a closed state, poised for catalysis, and an open state, able to release cleavage products and bind a new substrate. Here, we present six cryo-EM structures of the IDE dimer at 3.0-5.1 Å resolution, obtained in the presence of a sub-saturating concentration of insulin. Combining cryo-EM heterogeneity analysis with all-atom molecular dynamics (MD) simulations, we identified the structural basis and key residues for IDE conformational dynamics that were not previously revealed by IDE static structures. Notably R668 serves as a molecular latch mediating the open-close transition and facilitates key protein motions through charge-swapping interactions at the IDE-N/C interface. Our small-angle X-ray scattering analysis and enzymatic assays of an R668A mutant indicate a profound alteration of conformational dynamics and catalytic activity. By integrating coarse-grained MD simulations, our analysis reveals that IDE unfolds its substrates through the coordinated motion between IDE-N and IDE-C, as well as β-sheet formation between IDE and insulin. Additionally, our time-resolved cryo-EM analysis uncovers IDE allostery within the IDE dimer. Collectively, our findings demonstrate the strength of combining experimental and computational approaches to probe protein dynamics and pave the way for developing substrate-specific modulators of IDE activity.

Cryo-electron microscopy (cryo-EM) has transformed structural biology by enabling the determination of macromolecular structures at near-atomic resolution. The recent Nobel Prizes awarded to John Hopfield and Geoffrey Hinton in Physics, and to David Baker, Demis Hassabis, and John Jumper in Chemistry, highlight the growing convergence of artificial intelligence (AI) and computational innovation in advancing scientific discovery. Cryo-EM has emerged as a powerful tool for elucidating the architectures and dynamics of complex biological assemblies. Its ability to capture multiple conformational states provides mechanistic insights that are critical for understanding molecular function. However, traditional cryo-EM data processing pipelines face challenges in handling large, heterogeneous datasets. Manual particle selection, extensive computational demands, and uneven sampling of conformational states often limit resolution, especially for rare or transient conformations. Recent advances in AI-driven methods are reshaping this landscape. Deep neural networks now facilitate automated particle picking, denoising, classification, and three-dimensional reconstruction, substantially reducing human bias and computational bottlenecks. Machine learning models have also enhanced the accuracy of map interpretation and atomic modeling, accelerating the transition from raw micrographs to high-resolution structures. These developments represent a paradigm shift, positioning AI as an integral component of the “resolution revolution” in structural biology. This comprehensive article examines the state of the art in cryo-electron microscopy, highlighting how artificial intelligence is redefining every stage of the workflow-from automated particle picking and map reconstruction to structure modeling and high-resolution refinement. The integration of deep learning and neural network-based tools has transformed cryo-EM into a data-driven and highly precise discovery platform. By showcasing these innovations, we emphasize AI’s pivotal role in advancing next-generation cryo-EM research and accelerating molecular insights with profound implications for structural biology and drug discovery. Not applicable.

Structural robustness is vital to prevent disproportionate damage in a progressive collapse of a structure. Current assessment methods in building structures, such as the notional removal of structural members, are unable to capture cascading effects from debris impact loading and chained hazard scenarios. Vehicular impact with penetration into the building volume is an example of a plausible chained hazard scenario. In this study, the adequacy of notional damage methods was assessed for vehicular impact loading on building structures and a comprehensive framework was developed to address key limitations in conventional design methods against vehicular impact. The conventional approaches have relied on prescriptive forces and simplified models and have over-looked important phenomena, such as variable vehicle stiffness and energy dissipation mechanisms. The use of the vehicular impact framework has been exemplified using a model for progressive collapse to assess the consequences of successive time-delayed column loss on the structural response from the penetration of a lorry. The time-delay of successive column loss from vehicular impact was determined by considering the kinetic energy and energy dissipation mechanisms. Higher initial velocities lead to higher post-impact velocities after the initial impact on a column and to a reduction in time between successive failures, thus heightening the likelihood of cascading failures in structural columns and progressive collapse. Progressive collapse simulations revealed that a building structure may have sufficient robustness to prevent progressive collapse when subjected to the loss of single columns. However, successive column losses triggered an extensive progressive collapse.

Glycoprotein hormones (GpHs) produced in the human pituitary act through receptors (GpHRs) in the gonads to support reproduction and in the thyroid for metabolism. GpHs are heterodimeric cystine-knot proteins; their receptors bind cognate hormones at an extracellular domain and signal through a transmembrane domain to heterotrimeric G proteins. GpHs and GpHRs have co-evolved from invertebrate counterparts. Structures of the human receptors as isolated for cryogenic electron microscopy (cryo-EM) are all monomeric despite compelling evidence for their functioning as dimers. Here we characterize the homologous receptor from Caenorhabditis elegans. Its biochemical properties are notably similar to those of the thyroid stimulating hormone receptor (TSHR) of humans. Structurally, it is an asymmetric dimer (protomers screw-transformed by 142°/4.1 Å), composed such that only one hormone could bind. This is compatible with the 1:2 asymmetry of negatively cooperative TSH:TSHR complexes and for the transactivation evident from functional complementation of binding-deficient and signaling-deficient GpHRs. By modeling, a symmetrized dimer can bind two hormones as in the 2:2 complexes that support TSHR switches in G-protein usage.

Despite the rise of single particle cryo-electron microscopy (cryo-EM) as a premier method for resolving macromolecular structures at atomic resolution, methods to address molecular heterogeneity in vitrified samples have yet to reach maturity. With an increasing number of new methods to analyze the multitude of heterogeneous states captured in single particle images, a systematic approach to validation in this field is needed. With this motivation, we issued a challenge to the community to analyze two cryo-EM particle image sets of thyroglobulin that exhibit continuous conformational heterogeneity. The first dataset was experimental and the second was generated with a simulator, allowing control over the distribution of molecular structures and enabled direct comparison between participants’ submissions and the ground truth molecular structures and distributions. Participants were asked to submit 80 volumes representing the heterogeneous ensemble and estimate their respective populations in the image sets provided. Participation of the research community in the challenge was strong, with submissions from nearly all developers of heterogeneity methods, resulting in 41 submissions across both datasets. Submissions qualitatively exceeded expectations, with the molecular motions identified by methods resembling both each other and the ground truth motion. However, quantitatively assessing these similarities was a challenge in and of itself. In the process of assessing the submissions, we developed several validation metrics, most of which require reference to the underlying ground truth volumes. However, we have also explored the use of metrics that do not necessarily reference ground truth. This is particularly apt for experimental datasets where ground truth is inaccessible. These approaches allowed us to assess the similarity and accuracy in volume quality, molecular motions, and conformational distribution of di!erent submissions. These metrics and the e!orts of all participants help chart a path forward for the improvements of heterogeneity methods for cryo-EM and for future challenges to validate these new methods as they continue to be developed by the community.

Virus‐like particles (VLPs) find applications across many different fields, aided by their stability, capacity for large‐scale production, and presumed structural homogeneity. These attributes are a result of their highly efficient self‐assembly, which stems from the evolutionary pressures on the natural viruses from which they are derived. It is found that VLPs based on the Leviphage PP7 assemble in an unexpectedly wide range of morphologies. The relative abundance of these structures is sensitive to small changes in the coat protein sequence. These results raise the possibility that structural plasticity may be a general property of such self‐assembling structures rather than an exception.

Phosphatidylserine externalization on the surface of dying cells is a key signal for their recognition and clearance by macrophages and is mediated by members of the X-Kell related (Xkr) protein family. Defective Xkr-mediated scrambling impairs clearance, leading to inflammation. It was proposed that activation of the Xkr4 apoptotic scramblase requires caspase cleavage, followed by dimerization and ligand binding. Here, using a combination of biochemical approaches we show that purified monomeric, full-length human Xkr4 (hXkr4) scrambles lipids. CryoEM imaging shows that hXkr4 adopts a novel conformation, where three conserved acidic residues create a negative electrostatic surface embedded in the membrane. Molecular dynamics simulations show this conformation induces membrane thinning, which could promote scrambling. Thinning is ablated or reduced in conditions where scrambling is abolished or reduced. Our work provides insights into the molecular mechanisms of hXkr4 scrambling and suggests the ability to thin membranes might be a general property of active scramblases.