ArticleJournal of molecular biology2025
Properties Governing Native State Entanglements and Relationships to Protein Function.
Article in Journal of molecular biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Protein entanglement misfolding influences whether proteins undergo proteasomal degradation or persist in near-native misfolded states.Nature communications · 2026Article
- Detecting misfolded non-covalent lasso entanglements in protein structures, simulation trajectories, and mass spectrometry data.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Non-covalent lasso entanglements are structural motifs found in a majority of globular proteins, and their misfolding has been linked to a range of biological consequences. Here, we characterize these motifs' structural and physicochemical properties, sequence biases, functional site correlations, and universal features across E. coli, S. cerevisiae, and H. sapiens. We find that the crossing residues, which pierce the plane of the entanglement loop, are 11-times more likely to be a β-strand than an α-helix or random coil, and that around this position the protein sequence is 2.5-times more likely to be composed of a stretch of all hydrophobic residues (most often Val, Ile, or Phe) compared to other sequence motifs. Functionally, crossing residues are enriched at enzyme active sites in S. cerevisiae and small molecule binding residues across all species to degrees greater than expected by random chance. Metal binding residues are enriched in these entanglements in H. sapiens. Increasing statistical power by pooling together these species data, we find RNA-binding residues are enriched in these entanglement components. On the other hand, there is a spatial depletion of crossing residues at sites involved in protein binding. Using machine learning, we identified eight robust features predictive of these entanglements, achieving AUROC scores of 0.8 across species. These results are significant because they suggest a direct role for components of native entanglements in particular protein functions, as well as identifying strong secondary structure and sequence preferences in native entanglements.
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Registered trials
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