bims-lances 2025-04-27 papers

Nat Commun. 2025 Apr 22. 16(1): 3751

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN.

David Herreros, Carlos Perez Mata, Chari Noddings, Deli Irene, James Krieger, David A Agard, Ming-Daw Tsai, Carlos Oscar Sanchez Sorzano, Jose Maria Carazo.

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformations of macromolecules. Traditional methods assume discrete conformations, while newer algorithms estimate conformational landscapes representing the different structural states a biomolecule explores. This work presents HetSIREN, a deep learning-based method that can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. HetSIREN is defined as an accurate space-based method that allows spatially focused analysis and the introduction of sinusoidal hypernetworks with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the images' pose while conditioning the network with additional constraints to yield cleaner high-quality volumes, as well as addressing one of the most confusing issues in heterogeneity analysis, as it is the fact that structural heterogeneity estimations are entangled with pose estimation (and to a lesser extent with CTF estimation) thanks to its decoupling architecture.

DOI: https://doi.org/10.1038/s41467-025-59135-0

Struct Dyn. 2025 Mar;12(2): 020901

Unraveling atomic complexity from frozen samples.

Yasmeen N Ruma, Brent L Nannenga, Tamir Gonen.

Cryo-electron microscopy (cryo-EM) is a significant driver of recent advances in structural biology. Cryo-EM is comprised of several distinct and complementary methods, which include single particle analysis, cryo-electron tomography, and microcrystal electron diffraction. In this Perspective, we will briefly discuss the different branches of cryo-EM in structural biology and the current challenges in these areas.

DOI: https://doi.org/10.1063/4.0000303

Nat Chem Biol. 2025 Apr 24.

Advances in uncovering the mechanisms of macromolecular conformational entropy.

Stephanie A Wankowicz, James S Fraser.

During protein folding, proteins transition from a disordered polymer into a globular structure, markedly decreasing their conformational degrees of freedom, leading to a substantial reduction in entropy. Nonetheless, folded proteins retain substantial entropy as they fluctuate between the conformations that make up their native state. This residual entropy contributes to crucial functions like binding and catalysis, supported by growing evidence primarily from NMR and simulation studies. Here, we propose three major ways that macromolecules use conformational entropy to perform their functions; first, prepaying entropic cost through ordering of the ground state; second, spatially redistributing entropy, in which a decrease in entropy in one area is reciprocated by an increase in entropy elsewhere; third, populating catalytically competent ensembles, in which conformational entropy within the enzymatic scaffold aids in lowering transition state barriers. We also provide our perspective on how solving the current challenge of structurally defining the ensembles encoding conformational entropy will lead to new possibilities for controlling binding, catalysis and allostery.

DOI: https://doi.org/10.1038/s41589-025-01879-3

Annu Rev Phys Chem. 2025 Apr;76(1): 103-128

Resolving Conformational Plasticity in Mammalian Cells with High-Resolution Fluorescence Tools.

Hao Ruan, Edward A Lemke.

Investigating protein dynamic structural changes is fundamental for understanding protein function, drug discovery, and disease mechanisms. Traditional studies of protein dynamics often rely on investigations of purified systems, which fail to capture the complexity of the cellular environment. The intracellular milieu imposes distinct physicochemical constraints that affect macromolecular interactions and dynamics in ways not easily replicated in isolated experimental setups. We discuss the use of fluorescence resonance energy transfer, fluorescence anisotropy, and minimal photon flux imaging technologies to address these challenges and directly investigate protein conformational dynamics in mammalian cells. Key findings from the application of these techniques demonstrate their potential to reveal intricate details of protein conformational plasticity. By overcoming the limitations of traditional in vitro methods, these approaches offer a more accurate and comprehensive understanding of protein function and behavior within the complex environment of mammalian cells.

Keywords: MINFLUX; anisotropy; fluorescence lifetime; genetic code expansion; in situ conformational plasticity; single-molecule FRET

DOI: https://doi.org/10.1146/annurev-physchem-082423-030632

Front Mol Biosci. 2025 ;12 1542267

Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.

Souradeep Sil, Ishita Datta, Sankar Basu.

Intrinsically Disordered Proteins (IDPs) challenge traditional structure-function paradigms by existing as dynamic ensembles rather than stable tertiary structures. Capturing these ensembles is critical to understanding their biological roles, yet Molecular Dynamics (MD) simulations, though accurate and widely used, are computationally expensive and struggle to sample rare, transient states. Artificial intelligence (AI) offers a transformative alternative, with deep learning (DL) enabling efficient and scalable conformational sampling. They leverage large-scale datasets to learn complex, non-linear, sequence-to-structure relationships, allowing for the modeling of conformational ensembles in IDPs without the constraints of traditional physics-based approaches. Such DL approaches have been shown to outperform MD in generating diverse ensembles with comparable accuracy. Most models rely primarily on simulated data for training and experimental data serves a critical role in validation, aligning the generated conformational ensembles with observable physical and biochemical properties. However, challenges remain, including dependence on data quality, limited interpretability, and scalability for larger proteins. Hybrid approaches combining AI and MD can bridge the gaps by integrating statistical learning with thermodynamic feasibility. Future directions include incorporating physics-based constraints and learning experimental observables into DL frameworks to refine predictions and enhance applicability. AI-driven methods hold significant promise in IDP research, offering novel insights into protein dynamics and therapeutic targeting while overcoming the limitations of traditional MD simulations.

Keywords: MD simulations; artificial intelligence; conformational sampling; deep learning; intrinsically disordered proteins

DOI: https://doi.org/10.3389/fmolb.2025.1542267