Browsing by Subject "Enzyme mechanisms"

Now showing 1 - 3 of 3
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    3D variability analysis reveals a hidden conformational change controlling ammonia transport in human asparagine synthetase
    (Springer Nature, 2024-12-03) Coricello, Adriana; Nardone, Alanya J.; Lupia, Antonio; Gratteri, Carmen; Vos, Matthijn; Chaptal, Vincent; Alcaro, Stefano; Zhu, Wen; Takagi, Yuichiro; Richards, Nigel G. J.; Biochemistry and Molecular Biology, School of Medicine
    Advances in X-ray crystallography and cryogenic electron microscopy (cryo-EM) offer the promise of elucidating functionally relevant conformational changes that are not easily studied by other biophysical methods. Here we show that 3D variability analysis (3DVA) of the cryo-EM map for wild-type (WT) human asparagine synthetase (ASNS) identifies a functional role for the Arg-142 side chain and test this hypothesis experimentally by characterizing the R142I variant in which Arg-142 is replaced by isoleucine. Support for Arg-142 playing a role in the intramolecular translocation of ammonia between the active site of the enzyme is provided by the glutamine-dependent synthetase activity of the R142 variant relative to WT ASNS, and MD simulations provide a possible molecular mechanism for these findings. Combining 3DVA with MD simulations is a generally applicable approach to generate testable hypotheses of how conformational changes in buried side chains might regulate function in enzymes.
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    Metal Fluorides: Tools for Structural and Computational Analysis of Phosphoryl Transfer Enzymes
    (Springer, 2017-04) Jin, Yi; Molt, Robert W., Jr.; Blackburn, G. Michael; Chemistry and Chemical Biology, School of Science
    The phosphoryl group, PO3-, is the dynamic structural unit in the biological chemistry of phosphorus. Its transfer from a donor to an acceptor atom, with oxygen much more prevalent than nitrogen, carbon, or sulfur, is at the core of a great majority of enzyme-catalyzed reactions involving phosphate esters, anhydrides, amidates, and phosphorothioates. The serendipitous discovery that the phosphoryl group could be labeled by "nuclear mutation," by substitution of PO3- by MgF3- or AlF4-, has underpinned the application of metal fluoride (MF x ) complexes to mimic transition states for enzymatic phosphoryl transfer reactions, with sufficient stability for experimental analysis. Protein crystallography in the solid state and 19F NMR in solution have enabled direct observation of ternary and quaternary protein complexes embracing MF x transition state models with precision. These studies have underpinned a radically new mechanistic approach to enzyme catalysis for a huge range of phosphoryl transfer processes, as varied as kinases, phosphatases, phosphomutases, and phosphohydrolases. The results, without exception, have endorsed trigonal bipyramidal geometry (tbp) for concerted, "in-line" stereochemistry of phosphoryl transfer. QM computations have established the validity of tbp MF x complexes as reliable models for true transition states, delivering similar bond lengths, coordination to essential metal ions, and virtually identical hydrogen bond networks. The emergence of protein control of reactant orbital overlap between bond-forming species within enzyme transition states is a new challenging theme for wider exploration.
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    Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations
    (Springer Nature, 2022-03-29) Zhu, Jingxuan; Wang, Juexin; Han, Weiwei; Xu, Dong; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Protein allostery is a biological process facilitated by spatially long-range intra-protein communication, whereby ligand binding or amino acid change at a distant site affects the active site remotely. Molecular dynamics (MD) simulation provides a powerful computational approach to probe the allosteric effect. However, current MD simulations cannot reach the time scales of whole allosteric processes. The advent of deep learning made it possible to evaluate both spatially short and long-range communications for understanding allostery. For this purpose, we applied a neural relational inference model based on a graph neural network, which adopts an encoder-decoder architecture to simultaneously infer latent interactions for probing protein allosteric processes as dynamic networks of interacting residues. From the MD trajectories, this model successfully learned the long-range interactions and pathways that can mediate the allosteric communications between distant sites in the Pin1, SOD1, and MEK1 systems. Furthermore, the model can discover allostery-related interactions earlier in the MD simulation trajectories and predict relative free energy changes upon mutations more accurately than other methods.