Diatoms are central to the global carbon cycle. At the heart of diatom carbon fixation is an overlooked organelle called the pyrenoid, where concentrated CO is delivered to densely packed Rubisco. Diatom pyrenoids fix approximately one-fifth of global CO, but the protein composition of this organelle is largely unknown. Using fluorescence protein tagging and affinity purification-mass spectrometry, we generate a high-confidence spatially defined protein-protein interaction network for the diatom pyrenoid. Within our pyrenoid interaction network are 10 proteins with previously unknown functions. We show that six of these form a shell that encapsulates the Rubisco matrix and is critical for pyrenoid structural integrity, shape, and function. Although not conserved at a sequence or structural level, the diatom pyrenoid shares some architectural similarities to prokaryotic carboxysomes. Collectively, our results support the convergent evolution of pyrenoids across the two main plastid lineages and uncover a major structural and functional component of global CO fixation.
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http://dx.doi.org/10.1016/j.cell.2024.09.025 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Institute of Science and Technology Austria, AT-3400 Klosterneuburg, Austria.
Biophysical constraints limit the specificity with which transcription factors (TFs) can target regulatory DNA. While individual nontarget binding events may be low affinity, the sheer number of such interactions could present a challenge for gene regulation by degrading its precision or possibly leading to an erroneous induction state. Chromatin can prevent nontarget binding by rendering DNA physically inaccessible to TFs, at the cost of energy-consuming remodeling orchestrated by pioneer factors (PFs).
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Physiology and Biophysical Sciences, State University of New York at Buffalo, Buffalo, NY 14214.
Ion channels are generally allosteric proteins, involving specialized stimulus sensor domains conformationally linked to the gate to drive channel opening. Temperature receptors are a group of ion channels from the transient receptor potential family. They exhibit an unprecedentedly strong temperature dependence and are responsible for temperature sensing in mammals.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH 43210.
The homo-dodecameric ring-shaped RNA binding attenuation protein (TRAP) from binds up to twelve tryptophan ligands (Trp) and becomes activated to bind a specific sequence in the 5' leader region of the operon mRNA, thereby downregulating biosynthesis of Trp. Thermodynamic measurements of Trp binding have revealed a range of cooperative behavior for different TRAP variants, even if the averaged apparent affinities for Trp have been found to be similar. Proximity between the ligand binding sites, and the ligand-coupled disorder-to-order transition has implicated nearest-neighbor interactions in cooperativity.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Traditional Chinese Medicine, Ruijin Hospital, Shanghai Jiao Tong University Medical College, Shanghai, China.
Mycobacterium abscessus is a rapidly growing nontuberculous mycobacterium that causes severe pulmonary infections. Recent studies indicate that ferroptosis may play a critical role in the pathogenesis of M. abscessus pulmonary disease.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
BIFOLD─Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany.
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data.
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