bims-lances Biomed News
on Landscapes from Cryo-EM and Simulations
Issue of 2025–02–16
three papers selected by
James M. Krieger, National Centre for Biotechnology



  1. Curr Opin Struct Biol. 2025 Feb 07. pii: S0959-440X(25)00006-5. [Epub ahead of print]91 102988
      Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. Simulation-based inference (SBI) is a powerful framework for analyzing complex experimental data, integrating statistical inference, physics-based simulators, and machine learning. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep learning-based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force spectroscopy and cryo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.
    Keywords:  Bayesian inference; Cryo-electron microscopy; Data analysis; Likelihood-free inference; Simulation-based inference; Single-molecule data analysis
    DOI:  https://doi.org/10.1016/j.sbi.2025.102988
  2. QRB Discov. 2025 ;6 e3
      Integrative modeling enables structure determination for large macromolecular assemblies by combining data from multiple experiments with theoretical and computational predictions. Recent advancements in AI-based structure prediction and cryo electron-microscopy have sparked renewed enthusiasm for integrative modeling; structures from AI-based methods can be integrated with in situ maps to characterize large assemblies. This approach previously allowed us and others to determine the architectures of diverse macromolecular assemblies, such as nuclear pore complexes, chromatin remodelers, and cell-cell junctions. Experimental data spanning several scales was used in these studies, ranging from high-resolution data, such as X-ray crystallography and AlphaFold structure, to low-resolution data, such as cryo-electron tomography maps and data from co-immunoprecipitation experiments. Two recurrent modeling challenges emerged across a range of studies. First, these assemblies contained significant fractions of disordered regions, necessitating the development of new methods for modeling disordered regions in the context of ordered regions. Second, methods needed to be developed to utilize the information from cryo-electron tomography, a timely challenge as structural biology is increasingly moving towards in situ characterization. Here, we recapitulate recent developments in the modeling of disordered proteins and the analysis of cryo-electron tomography data and highlight other opportunities for method development in the context of integrative modeling.
    Keywords:  Conformational ensembles; Electron cryo-tomography; Generative modeling; Integrative modeling; Intrinsically disordered proteins; Macromolecular assemblies; Protein language models
    DOI:  https://doi.org/10.1017/qrd.2024.15
  3. Curr Opin Struct Biol. 2025 Feb 08. pii: S0959-440X(25)00018-1. [Epub ahead of print]91 103000
      This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2, there has been a shift toward more accurate and efficient sampling of structural ensembles. The review discusses the integration of AI with traditional molecular dynamics techniques as well as experiments, the challenges of conformational sampling, and future directions for AI-driven research in structural biology, particularly in drug discovery and protein dynamics.
    DOI:  https://doi.org/10.1016/j.sbi.2025.103000