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  Originally published by Justin Jackson , Phys.org, on May 19, 2025 Edited by Sadie Harley , reviewed by Robert Egan Working model revealing the role of ALDH4A1 in maintaining an active MPC complex for mitochondrial pyruvate import and TCA cycle entry. Credit: Nature Cell Biology (2025). DOI: 10.1038/s41556-025-01651-8 Researchers at Duke University Medical Center and Wake Forest University School of Medicine have identified ALDH4A 1 , a mitochondrial proline-metabolizing enzyme , as a third structural component of the mitochondrial pyruvate carrier (MPC) complex . Forming a trimeric assembly with MPC1 and MPC2 , ALDH4A1 maintains MPC integrity and facilitates pyruvate import into mitochondria . M itochondrial pyruvate import serves as a c ritical step in cellular energy metabolism , linking cytosolic glycolysis to mitochondrial oxidative phosphorylation. Disruptions in this pathway can promote cancer cell proliferation by increasing cytosolic pyruvate and driving ...

Originally published by Ian Haydon, University of Washington , February 22,2023   An artist's imaginative conception of the idea of light-emitting enzymes. Credit: Ian Haydon / Institute for Protein Design For the first time, scientists have used machine learning to create brand-new enzymes , which are proteins that accelerate chemical reactions. This is an important step in the field of protein design , as new enzymes could have many uses across medicine and industrial manufacturing . "Living organisms are remarkable chemists. Rather than relying on toxic compounds or extreme heat, they use enzymes to break down or build up whatever they need under gentle conditions. New enzymes could put renewable chemicals and biofuels within reach," said senior author David Baker, professor of biochemistry at the University of Washington School of Medicine and recipient of the 2021 Breakthrough Prize in Life Sciences. Original article 

Model learns how individual amino acids determine protein function Technique could improve machine-learning tasks in protein design, drug testing, and other applications. Rob Matheson | MIT News Office Publication Date: March 22, 2019   A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein’s function, which could help researchers design and test new proteins for drug development or biological research. Proteins are linear chains of amino acids, connected by peptide bonds, that fold into exceedingly complex three-dimensional structures, depending on the sequence and physical interactions within the chain. That structure, in turn, determines the protein’s biological function. Knowing a protein’s 3-D structure, therefore, is valuable for, say, predicting how proteins may respond to certain drugs. Original article