Optimized docking of models into cryo-EM maps requires exploiting an understanding of the signal expected in the data to minimize the calculation time while maintaining sufficient signal. The likelihood-based rotation function used in crystallography can be employed to establish plausible orientations in a docking search. A phased likelihood translation function yields scores for the placement and rigid-body refinement of oriented models. Optimized strategies for choices of the resolution of data from the cryo-EM maps to use in the calculations and the size of search volumes are based on expected log-likelihood-gain scores computed in advance of the search calculation. Tests demonstrate that the new procedure is fast, robust and effective at placing models into even challenging cryo-EM maps.
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http://dx.doi.org/10.1107/S2059798323001602 | DOI Listing |
Int J Mol Sci
December 2024
Department of Biochemistry, College of Natural Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea.
Recent advances in cryo-electron microscopy (cryo-EM) have facilitated the high-resolution structural determination of macromolecular complexes in their native states, providing valuable insights into their dynamic behaviors. However, insufficient understanding or experience with the cryo-EM image processing parameters can result in the loss of biological meaning. In this paper, we investigate the dihydrolipoyl acetyltransferase (E2) inner core complex of the pyruvate dehydrogenase complex (PDC) and reconstruct the 3D maps using five different symmetry parameters.
View Article and Find Full Text PDFSubcell Biochem
December 2024
School of Biomedical Sciences, The University of New South Wales, Sydney, NSW, Australia.
Electron microscopy (EM) techniques have been crucial for understanding the structure of biological specimens such as cells, tissues and macromolecular assemblies. Viruses and related viral assemblies are ideal targets for structural studies that help to define essential biological functions. Whereas conventional EM methods use chemical fixation, dehydration, and staining of the specimens, cryogenic electron microscopy (cryo-EM) preserves the native hydrated state.
View Article and Find Full Text PDFBiochemistry
January 2025
Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States.
Amyloid diseases feature pathologic deposition of normally soluble proteins and peptides as insoluble fibrils in vital organs. Amyloid fibrils co-deposit with various nonfibrillar components including heparan sulfate (HS), a glycosaminoglycan that promotes amyloid formation in vitro for many unrelated proteins. HS-amyloid interactions have been proposed as a therapeutic target for inflammation-linked amyloidosis wherein N-terminal fragments of serum amyloid A (SAA) protein deposit in the kidney and liver.
View Article and Find Full Text PDFNucleic Acids Res
January 2025
Institute of Medical Physics and Biophysics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117 Berlin, Germany.
Chemical modifications of ribosomal RNAs (rRNAs) and proteins expand their topological repertoire, and together with the plethora of bound ligands, fine-tune ribosomal function. Detailed knowledge of this natural composition provides important insights into ribosome genesis and function and clarifies some aspects of ribosomopathies. The discovery of new structural properties and functional aspects of ribosomes has gone hand in hand with cryo-electron microscopy (cryo-EM) and its technological development.
View Article and Find Full Text PDFBioinform Adv
November 2024
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, United States.
Summary: Although multiple neural networks have been proposed for detecting secondary structures from medium-resolution (5-10 Å) cryo-electron microscopy (cryo-EM) maps, the loss functions used in the existing deep learning networks are primarily based on cross-entropy loss, which is known to be sensitive to class imbalances. We investigated five loss functions: cross-entropy, Focal loss, Dice loss, and two combined loss functions. Using a U-Net architecture in our DeepSSETracer method and a dataset composed of 1355 box-cropped atomic-structure/density-map pairs, we found that a newly designed loss function that combines Focal loss and Dice loss provides the best overall detection accuracy for secondary structures.
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