Roet, S. | bioRxiv Science

bioRxiv2024

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

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 effects 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 effective 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 efficiency and accuracy of macromolecule detection in cellular contexts.

molecular biology↗

bioRxiv2020

Path sampling simulations reveal how the Q61L mutation restricts the dynamics of KRas

The GTPase KRas is a signaling protein in networks for cell differentiation, growth, and division. KRas mutations can prolong activation of these networks, resulting in tumor formation. When active, KRas tightly binds GTP. Several oncogenic mutations affect the conversion between this rigid state and inactive, more flexible states. Detailed understanding of these transitions may provide valuable insights into how mutations affect KRas. Path sampling simulations, which focus on transitions, show KRas visiting several states, which are the same for wild type and the oncogenic mutant Q61L. Large differences occur when converting between these states, indicating the dramatic effect of the Q61L mutation on KRas dynamics. For Q61L a route to the flexible state is inaccessible, thus shifting the equilibrium to more rigid states. Our methodology presents a novel way to predict dynamical effects of KRas mutations, which may aid in identifying potential therapeutic targets. Author summaryCancer cells frequently contain mutations in the protein KRas. However, KRas is a challenging target for anti-cancer drugs, in part because the dynamic behavior of flexible regions in the protein is difficult to characterize experimentally, and occurs on timescales that are too long for straightforward molecular dynamics simulations. We have used path sampling, an advanced simulation technique that overcomes long timescales, to obtain atomistic insight into the dynamics of KRas. Comparing the oncogenic mutant Q61L to the wild type revealed that the mutation closes off one transition channel for deactivating KRas. Our approach opens up the way for predicting the dynamical effects of mutations in KRas, which may aid in identifying potential therapeutic targets.

cancer biology↗