Exploring the Conformational Landscape of Cryo-EM Using Energy-Aware Pathfinding Algorithm
Lin, T.-Y.; Chung, S.-C.
Show abstract
Cryo-electron microscopy (cryo-EM) is a powerful technique for studying macromolecules and holds the potential for identifying kinetically preferred transition sequences between conformational states. Typically, these sequences are explored within two-dimensional energy landscapes. However, due to the complexity of biomolecules, representing conformational changes in two dimensions can be challenging. Recent advancements in reconstruction models have successfully extracted structural heterogeneity from cryo-EM images using higher-dimension latent space. Nonetheless, creating high-dimensional conformational landscapes in the latent space and then searching for preferred paths continues to be a formidable task. This study introduces an innovative framework for identifying preferred trajectories within high-dimensional conformational landscapes. Our method encompasses the search for the shortest path in the graph, where edge weights are determined based on the energy estimation at each node using local density. The effectiveness of this approach is demonstrated by identifying accurate transition states in both synthetic and real-world datasets featuring continuous conformational changes.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Localization of Macromolecules in Crowded Cellular Cryo-electron Tomograms from Extremely Sparse Labels 95%
- Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net 95%
- Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps 95%
Similar papers in this journal
- A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics 95%
- Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins 95%
- Revisiting the \"satisfaction of spatial restraints\" approach of MODELLER for protein homology modeling 95%
Similar papers in this journal
- Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN 97%
- De Novo Atomic Protein Structure Modeling for Cryo-EMDensity Maps Using 3D Transformer and Hidden MarkovModel 96%
- A robust normalized local filter to estimate compositionalheterogeneity directly from cryo-EM maps 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.