bims-lances Biomed News
on Landscapes from Cryo-EM and Simulations
Issue of 2025–03–30
nine papers selected by
James M. Krieger, National Centre for Biotechnology



  1. J Chem Inf Model. 2025 Mar 28.
      With the breakthroughs in protein structure prediction technology, constructing atomic structures from cryo-electron microscopy (cryo-EM) density maps through structural fitting has become increasingly critical. However, the accuracy of the constructed models heavily relies on the precision of the structure-to-map fitting. In this study, we introduce DEMO-EMfit, a progressive method that integrates deep learning-based backbone map extraction with a global-local structural pose search to fit atomic structures into density maps. DEMO-EMfit was extensively evaluated on a benchmark data set comprising both cryo-electron tomography (cryo-ET) and cryo-EM maps of protein and nucleic acid complexes. The results demonstrate that DEMO-EMfit outperforms state-of-the-art approaches, offering an efficient and accurate tool for fitting atomic structures into density maps.
    DOI:  https://doi.org/10.1021/acs.jcim.5c00004
  2. Acta Crystallogr D Struct Biol. 2025 Apr 01.
      Despite the parallels between problems in computer vision and cryo-electron microscopy (cryo-EM), many state-of-the-art approaches from computer vision have yet to be adapted for cryo-EM. Within the computer-vision research community, implicits such as neural radiance fields (NeRFs) have enabled the detailed reconstruction of 3D objects from few images at different camera-viewing angles. While other neural implicits, specifically density fields, have been used to map conformational heterogeneity from noisy cryo-EM projection images, most approaches represent volume with an implicit function in Fourier space, which has disadvantages compared with solving the problem in real space, complicating, for instance, masking, constraining physics or geometry, and assessing local resolution. In this work, we build on a recent development in neural implicits, a multi-resolution hash-encoding framework called instant-NGP, that we use to represent the scalar volume directly in real space and apply it to the cryo-EM density-map reconstruction problem (InstaMap). We demonstrate that for both synthetic and real data, InstaMap for homogeneous reconstruction achieves higher resolution at shorter training stages than five other real-spaced representations. We propose a solution to noise overfitting, demonstrate that InstaMap is both lightweight and fast to train, implement masking from a user-provided input mask and extend it to molecular-shape heterogeneity via bending space using a per-image vector field.
    Keywords:  cryo-EM; density maps; end-to-end gradient-based learning; heterogeneity
    DOI:  https://doi.org/10.1107/S2059798325002025
  3. Protein Sci. 2025 Apr;34(4): e70107
      Cells function through dynamic interactions between macromolecules. Detailed characterization of the dynamics of large biomolecular systems is often not feasible by individual biophysical methods. In such cases, it may be possible to compute useful models by integrating multiple sources of information. We have previously developed an integrative method to model dynamic processes by computing biomolecular heterogeneity at fixed time points, then generating static integrative structural modes for each of these heterogeneity models, and finally connecting these static models to produce a scored trajectory model that depicts the process. Here, we demonstrate how to compute, score, and assess these integrative spatiotemporal models using our open-source Integrative Modeling Platform (IMP) program (https://integrativemodeling.org/).
    Keywords:  biomolecular processes; integrative structure modeling; molecular dynamics; structural biology
    DOI:  https://doi.org/10.1002/pro.70107
  4. Methods Enzymol. 2025 ;pii: S0076-6879(25)00004-7. [Epub ahead of print]712 41-53
      CRISPR-Cas9 has transformed genome editing through its programmability and versatility. Its DNA cleavage activity involves dynamic conformational changes during gRNA binding, DNA recognition, R-loop formation, and endonuclease activation. Understanding these molecular transitions is critical for improving the specificity and efficiency of Cas9, but this remains challenging precisely due to these rapid structural rearrangements. Early structural studies provided foundational insights but were limited to static states under catalytically inactive conditions. Cryo-EM has since enabled visualization of the dynamic nature of active Cas9, by enriching for specific conformations. This chapter introduces a kinetics-informed cryo-EM approach to capture the stepwise activation of Cas9 in real time. With thorough kinetic analyses, such as stopped-flow measurements of R-loop formation, we describe how to identify optimal timepoints to visualize key conformational states with cryo-EM. Integration of kinetic and structural data enables precise mapping of the conformational landscape of Cas9 and other dynamic enzymes, advancing our understanding of their molecular mechanisms and providing a framework for engineering enhanced variants.
    Keywords:  CRISPR-Cas enzymes; Cas9; Cryo-electron microscopy; Kinetics; Structural changes
    DOI:  https://doi.org/10.1016/bs.mie.2025.01.004
  5. J Mol Biol. 2025 Feb 13. pii: S0022-2836(25)00069-5. [Epub ahead of print] 169003
      Intrinsically disordered proteins and regions (IDPs/IDRs) leverage their structural flexibility to fulfill essential cellular functions, with dysfunctions often linked to severe diseases. However, the relationships between their sequences, structural dynamics and functional roles remain poorly understood. Understanding these complex relationships is crucial for therapeutic development, highlighting the need for methods to generate plausible IDP/IDR conformational ensembles. While AlphaFold (AF) excels at modeling structured domains, it fails to accurately represent disordered regions, leaving a significant portion of proteomes inaccurately modeled. We present AFflecto, a user-friendly web server for generating large conformational ensembles of proteins that include both structured domains and IDRs from AF structural models. AFflecto identifies IDRs as tails, linkers or loops by analyzing their structural context. Additionally, it incorporates a method to identify conditionally folded IDRs that AF may incorrectly predict as natively folded elements. The conformational space is globally explored using efficient stochastic sampling algorithms. AFflecto's web interface allows users to customize the modeling, by modifying boundaries between ordered and disordered regions, and selecting among several sampling strategies. The web server is freely available at https://moma.laas.fr/applications/AFflecto/.
    DOI:  https://doi.org/10.1016/j.jmb.2025.169003
  6. Protein Sci. 2025 Apr;34(4): e70099
      During mitosis, unattached kinetochores trigger the spindle assembly checkpoint by promoting the assembly of the mitotic checkpoint complex, a heterotetramer comprising Mad2, Cdc20, BubR1, and Bub3. Critical to this process is the kinetochore-mediated catalysis of an intrinsically slow conformational conversion of Mad2 from an open (O-Mad2) inactive state to a closed (C-Mad2) active state bound to Cdc20. These Mad2 conformational changes involve substantial remodeling of the N-terminal β1 strand and C-terminal β7/β8 hairpin. In vitro, the Mad2-interaction motif (MIM) of Cdc20 (Cdc20MIM) triggers the rapid conversion of O-Mad2 to C-Mad2, effectively removing the kinetic barrier for MCC assembly. How Cdc20MIM directly induces Mad2 conversion remains unclear. In this study, we demonstrate that the Cdc20MIM-binding site is inaccessible in O-Mad2. Time-resolved NMR and molecular dynamics simulations show how Mad2 conversion involves sequential conformational changes of flexible structural elements in O-Mad2, orchestrated by Cdc20MIM. Conversion is initiated by the β7/β8 hairpin of O-Mad2 transiently unfolding to expose a nascent Cdc20MIM-binding site. Engagement of Cdc20MIM to this site promotes the release of the β1 strand. We propose that initial conformational changes of the β7/β8 hairpin allow binding of Cdc20MIM to a transient intermediate state of Mad2, thereby lowering the kinetic barrier to Mad2 conversion.
    Keywords:  Cdc20; HORMA domain; Mad2; NMR; cell cycle; metamorphic proteins; molecular dynamic simulations; spindle assembly checkpoint
    DOI:  https://doi.org/10.1002/pro.70099
  7. J Proteome Res. 2025 Mar 24.
      Many proteins can exist in multiple conformational states in vivo to achieve distinct functional roles. These states include alternative conformations, variable post-translational modifications (PTMs), and associations with interacting protein, nucleotide, and ligand partners. Quantitative chemical cross-linking of live cells, organelles, or tissues together with mass spectrometry provides the relative abundance of cross-link levels formed in two or more compared samples, which depends both on the relative levels of existent protein conformational states in the compared samples and on the relative likelihood of the cross-link originating from each. Because cross-link conformational state preferences can vary widely, one expects intraprotein cross-link levels from proteins with high conformational plasticity to display divergent quantitation among samples with differing conformational ensembles. Here we use the large volume of quantitative cross-linking data available on the public XLinkDB database to cluster intraprotein cross-links according to their quantitation in many diverse compared samples to provide the first widescale glimpse of cross-links grouped according to the protein conformational state(s) from which they predominantly originate. We further demonstrate how cluster cross-links can be aligned with any protein structure to assess the likelihood that they were derived from it.
    Keywords:  chemical cross-linking; cross-link database; mass spectrometry; protein complexes; protein structures; quantitative XL-MS
    DOI:  https://doi.org/10.1021/acs.jproteome.4c01030
  8. J Am Chem Soc. 2025 Mar 27.
      Small heat shock proteins (sHSPs), including HSPB1, are essential regulators of cellular proteostasis that interact with unfolded and partially folded proteins to prevent aberrant misfolding and aggregation. These proteins fulfill a similar role in biological condensates, where they interact with intrinsically disordered proteins to modulate their liquid-liquid and liquid-to-solid phase transitions. Characterizing the sHSP structure, dynamics, and client interactions is challenging due to their partially disordered nature, their tendency to form polydisperse oligomers, and their diverse range of clients. In this work, we leverage various biophysical methods, including fast 1H-based magic angle spinning (MAS) NMR spectroscopy, molecular dynamics (MD) simulations, and modeling, to shed new light on the structure and dynamics of HSPB1 oligomers. Using split-intein-mediated segmental labeling, we provide unambiguous evidence that in the oligomer context, the N-terminal domain (NTD) of HSPB1 is rigid and adopts an ensemble of heterogeneous conformations, the α-Crystallin domain (ACD) forms dimers and experiences multiple distinct local environments, while the C-terminal domain (CTD) remains highly dynamic. Our computational models suggest that the NTDs participate in extensive NTD-NTD and NTD-ACD interactions and are sequestered within the oligomer interior. We further demonstrate that HSPB1 higher order oligomers disassemble into smaller oligomeric species in the presence of a client protein and that an accessible NTD is essential for HSPB1 partitioning into condensates and interactions with client proteins. Our integrated approach provides a high-resolution view of the complex oligomeric landscape of HSPB1 and sheds light on the elusive network of interactions that underlies the function of HSPB1 in biological condensates.
    DOI:  https://doi.org/10.1021/jacs.4c18668
  9. J Mol Biol. 2025 Mar 12. pii: S0022-2836(25)00151-2. [Epub ahead of print] 169085
      The environment inside biological cells is densely populated by macromolecules and other cellular components. The crowding has a significant impact on folding and stability of macromolecules, and on kinetics of molecular interactions. Computational approaches to cell modeling, such as molecular dynamics, provide details of macromolecular behavior in concentrated solutions. However, such simulations are either slow, when carried out at atomic resolution, or significantly coarse-grained. Protein docking has been widely used for predicting structures of protein complexes. Systematic docking approaches, such as those based on Fast Fourier Transform (FFT), map the entire intermolecular energy landscape by determining the position and depth of the energy minima. The GRAMMCell web server implements docking-based approach for simulating cell crowded environment by sampling the intermolecular energy landscape generated by GRAMM (Global RAnge Molecular Matching). GRAMM systematically maps the landscape by a spectrum of docking poses corresponding to stable (deep energy minima) and transient (shallow minima) protein interactions. The sampling of these energy landscapes of a large system of proteins is performed in GRAMMCell using highly optimized Markov Chain Monte Carlo protocol. The procedure allows simulation of extra-long trajectories of large, crowded protein systems with atomic resolution accuracy. GRAMMCell is available at https://grammcell.compbio.ku.edu.
    Keywords:  cell-modelling; energy landscape; protein docking; protein–protein interactions; web-based resource
    DOI:  https://doi.org/10.1016/j.jmb.2025.169085