bims-tricox Biomed News
on Translation, ribosomes and COX
Issue of 2024–05–19
two papers selected by
Yash Verma, University of Zurich
  1. ZNF574 is a Quality Control Factor For Defective Ribosome Biogenesis Intermediates.
  2. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.
  1. bioRxiv. 2024 Apr 29. pii: 2024.04.26.591394. [Epub ahead of print]
    ZNF574 is a Quality Control Factor For Defective Ribosome Biogenesis Intermediates.
    Jared Akers, Adrian Bothe, Hanna Suh, Carmen Jung, Zachary Stolp, Tanushree Ghosh, Liewei Yan, Yuming Wang, Tarabryn Grismer, Andreas Reyes, Tianyi Hu, Shouling Xu, Nenad Ban, Kamena Kostova.
      Eukaryotic ribosome assembly is an intricate process that involves four ribosomal RNAs, 80 ribosomal proteins, and over 200 biogenesis factors that take part in numerous interdependent steps. This complexity creates a large genetic space in which pathogenic mutations can occur. Dead-end ribosome intermediates that result from biogenesis errors are rapidly degraded, affirming the existence of quality control pathway(s) that monitor ribosome assembly. However, the factors that differentiate between on-path and dead-end intermediates are unknown. We engineered a system to perturb ribosome assembly in human cells and discovered that faulty ribosomes are degraded via the ubiquitin proteasome system. We identified ZNF574 as a key component of a novel quality control pathway, which we term the Ribosome Assembly Surveillance Pathway (RASP). Loss of ZNF574 results in the accumulation of faulty biogenesis intermediates that interfere with global ribosome production, further emphasizing the role of RASP in protein homeostasis and cellular health.
    DOI:  https://doi.org/10.1101/2024.04.26.591394
  2. Nat Methods. 2024 May 14.
    OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.
    Gustaf Ahdritz, Nazim Bouatta, Christina Floristean, Sachin Kadyan, Qinghui Xia, William Gerecke, Timothy J O'Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M Stepniewska-Dziubinska, Shang Zhang, Adegoke Ojewole, Murat Efe Guney, Stella Biderman, Andrew M Watkins, Stephen Ra, Pablo Ribalta Lorenzo, Lucas Nivon, Brian Weitzner, Yih-En Andrew Ban, Shiyang Chen, Minjia Zhang, Conglong Li, Shuaiwen Leon Song, Yuxiong He, Peter K Sorger, Emad Mostaque, Zhao Zhang, Richard Bonneau, Mohammed AlQuraishi.
      AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.
    DOI:  https://doi.org/10.1038/s41592-024-02272-z
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