bims-lances Biomed News
on Landscapes from Cryo-EM and Simulations
Issue of 2024–09–15
two papers selected by
James M. Krieger, National Centre for Biotechnology
  1. DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map.
  2. Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.
  1. IEEE Trans Vis Comput Graph. 2024 Sep 10. PP
    DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map.
    Deng Luo, Zainab Alsuwaykit, Dawar Khan, Ondrej Strnad, Tobias Isenberg, Ivan Viola.
      We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, this process is necessary to semi-automatically composite large mesoscale models of complex protein assemblies and complete cellular structures that are based on measured cryo-EM data. The current approaches require manual fitting in three dimensions to start, resulting in approximately aligned structures followed by an automated fine-tuning of the alignment. The DiffFit approach enables domain scientists to fit new structures automatically and visualize the results for inspection and interactive revision. The fitting begins with differentiable three-dimensional (3D) rigid transformations of the protein atom coordinates followed by sampling the density values at the atom coordinates from the target cryo-EM volume. To ensure a meaningful correlation between the sampled densities and the protein structure, we proposed a novel loss function based on a multi-resolution volume-array approach and the exploitation of the negative space. This loss function serves as a critical metric for assessing the fitting quality, ensuring the fitting accuracy and an improved visualization of the results. We assessed the placement quality of DiffFit with several large, realistic datasets and found it to be superior to that of previous methods. We further evaluated our method in two use cases: automating the integration of known composite structures into larger protein complexes and facilitating the fitting of predicted protein domains into volume densities to aid researchers in identifying unknown proteins. We implemented our algorithm as an open-source plugin (github.com/nanovis/DiffFit) in ChimeraX, a leading visualization software in the field. All supplemental materials are available at osf.io/5tx4q.
    DOI:  https://doi.org/10.1109/TVCG.2024.3456404
  2. Int J Mol Sci. 2024 Sep 08. pii: 9725. [Epub ahead of print]25(17):
    Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.
    Ahrum Son, Woojin Kim, Jongham Park, Wonseok Lee, Yerim Lee, Seongyun Choi, Hyunsoo Kim.
      Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence have revolutionized our understanding of protein dynamics. Nuclear magnetic resonance spectroscopy provides atomic-resolution insights, while molecular dynamics simulations offer detailed trajectories of protein motions. Computational methods applied to X-ray crystallography and cryo-electron microscopy (cryo-EM) have enabled the exploration of protein dynamics, capturing conformational ensembles that were previously unattainable. The integration of machine learning, exemplified by AlphaFold2, has accelerated structure prediction and dynamics analysis. These approaches have revealed the importance of protein dynamics in allosteric regulation, enzyme catalysis, and intrinsically disordered proteins. The shift towards ensemble representations of protein structures and the application of single-molecule techniques have further enhanced our ability to capture the dynamic nature of proteins. Understanding protein dynamics is essential for elucidating biological mechanisms, designing drugs, and developing novel biocatalysts, marking a significant paradigm shift in structural biology and drug discovery.
    Keywords:  allosteric regulation; artificial intelligence; conformational changes; enzyme catalysis; molecular dynamics; protein dynamics; protein folding; structural biology
    DOI:  https://doi.org/10.3390/ijms25179725
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