High-confidence Template Matching for Precise Macromolecule Localization in Cellular Tomograms

High-confidence template matching for in situ macromolecule localization has been successfully tested on various species, including Dictyostelium discoideum, Schizosaccharomyces pombe, and human tissue culture cells. The template matching pipeline, implemented using the STOPGAP software framework, accurately identifies macromolecular complexes in tomograms, such as ribosomes, nuclear pore complexes, microtubules, and vaults.

By using a library of templates obtained from various sources, including subtomogram averaging, homology modeling, and molecular dynamics simulations, the template matching process can detect low-density and low-abundance particles with high fidelity. The optimization of search parameters, such as bandpass filters, mask sizes, voxel sizes, and angular sampling, plays a crucial role in the performance of template matching.

The high-confidence template matching approach has been validated for the quantitative localization of ribosomes, identification of subunits and conformer subpopulations, and detection of membrane compartments. Additionally, the method has successfully identified ribosome-loaded vaults in situ, providing evidence for cargo encapsulation within vault particles.

Comparative analysis with state-of-the-art deep-learning tools, such as DeePiCt and DeepFinder, on a tomographic dataset of Schizosaccharomyces pombe shows that high-confidence template matching performs comparably for ribosome localization and outperforms these tools for fatty acid synthase localization and nuclear pore complex identification.

Overall, high-confidence template matching for in situ macromolecule localization demonstrates its versatility, accuracy, and potential for visual proteomics analysis in various biological systems. The method offers a comprehensive and reliable approach for studying macromolecular complexes in their native cellular environment.