bims-tricox 2024-05-12 papers

Issue of 2024–05–12
three papers selected by
Yash Verma, University of Zurich

Annu Rev Cell Dev Biol. 2024 May 09.

Ribosomes synthesize protein in all cells. Maintaining both the correct number and composition of ribosomes is critical for protein homeostasis. To address this challenge, cells have evolved intricate quality control mechanisms during assembly to ensure that only correctly matured ribosomes are released into the translating pool. However, these assembly-associated quality control mechanisms do not deal with damage that arises during the ribosomes' exceptionally long lifetimes and might equally compromise their function or lead to reduced ribosome numbers. Recent research has revealed that ribosomes with damaged ribosomal proteins can be repaired by the release of the damaged protein, thereby ensuring ribosome integrity at a fraction of the energetic cost of producing new ribosomes, appropriate for stress conditions. In this article, we cover the types of ribosome damage known so far, and then we review the known repair mechanisms before surveying the literature for possible additional instances of repair.

DOI: https://doi.org/10.1146/annurev-cellbio-111822-113326

Sci Data. 2024 May 06. 11(1): 458

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures.

Nabin Giri, Liguo Wang, Jianlin Cheng.

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

DOI: https://doi.org/10.1038/s41597-024-03299-9

Biochim Biophys Acta Mol Cell Res. 2024 May 06. pii: S0167-4889(24)00089-2. [Epub ahead of print] 119746

Genetic dissection of the bacterial FeS protein biogenesis machineries.

Mathieu Sourice, Charlotte Oriol, Corinne Aubert, Pierre Mandin, Béatrice Py.

Iron‑sulfur (FeS) clusters are one of the most ancient and versatile inorganic cofactors present in the three domains of life. FeS clusters are essential cofactors for the activity of a large variety of metalloproteins that play crucial physiological roles. FeS protein biogenesis is a complex process that starts with the acquisition of the elements (iron and sulfur atoms) and their assembly into an FeS cluster that is subsequently inserted into the target proteins. The FeS protein biogenesis is ensured by multiproteic systems conserved across all domains of life. Here, we provide an overview on how bacterial genetics approaches have permitted to reveal and dissect the FeS protein biogenesis process in vivo.

Keywords: Bacterial genetics; ISC; MIS; NIF; SMS; SUF

DOI: https://doi.org/10.1016/j.bbamcr.2024.119746