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pytom-match-pick: a tophat-transform constraint for automated classification in template matching

bioRxiv, ISSN: 2692-8205
2024
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Article Description

Template matching (TM) in cryo-electron tomography (cryo-ET) enables in situ detection and localization of known macromolecules. However, TM faces challenges such as interfering features with a high signal-to-noise ratio and the need for manual curation of results. To address these challenges, we introduce pytom-match-pick, a GPU-accelerated, open-source command line interface for enhanced TM in cryo-ET. Using pytom-match-pick, we first quantify the e=ects of point spread function (PSF) weighting and show that a tilt-weighted PSF outperforms a binary wedge with a single defocus estimate. We also assess previously introduced background normalization methods for classification performance. This indicates that phase randomization is more e=ective than spectrum whitening in reducing false positives. Furthermore, a novel application of the tophat transform on score maps, combined with a dual-constraint thresholding strategy, reduces false positives and improves precision. We benchmarked pytom-match-pick on public datasets, demonstrating improved classification and localization of macromolecules like ribosomal subunits and proteasomes that led to fewer artifacts in subtomogram averages. This tool promises to advance visual proteomics by improving the e=iciency and accuracy of macromolecule detection in cellular contexts.

Bibliographic Details

Marten L. Chaillet; Sander Roet; Friedrich Förster; Remco C. Veltkamp

Cold Spring Harbor Laboratory

Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences; Immunology and Microbiology; Neuroscience; Pharmacology, Toxicology and Pharmaceutics

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