Browsing by Subject "Catalytic domain"

Now showing 1 - 4 of 4
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    Active Site Characterization of a Campylobacter jejuni Nitrate Reductase Variant Provides Insight into the Enzyme Mechanism
    (ACS, 2024) Yang, Jing; Mintmier, Breeanna; KC, Khadanand; Metzger, Mikayla C.; Radhakrishnan, Manohar; McGarry, Jennifer; Wilcoxen, Jarett; Basu, Partha; Kirk, Martin L.; Chemistry and Chemical Biology, School of Science
    Mo K-edge X-ray absorption spectroscopy (XAS) is used to probe the structure of wild-type Campylobacter jejuni nitrate reductase NapA and the C176A variant. The results of extended X-ray absorption fine structure (EXAFS) experiments on wt NapA support an oxidized Mo(VI) hexacoordinate active site coordinated by a single terminal oxo donor, four sulfur atoms from two separate pyranopterin dithiolene ligands, and an additional S atom from a conserved cysteine amino acid residue. We found no evidence of a terminal sulfido ligand in wt NapA. EXAFS analysis shows the C176A active site to be a 6-coordinate structure, and this is supported by EPR studies on C176A and small molecule analogs of Mo(V) enzyme forms. The SCys is replaced by a hydroxide or water ligand in C176A, and we find no evidence of a coordinated sulfhydryl (SH) ligand. Kinetic studies show that this variant has completely lost its catalytic activity toward nitrate. Taken together, the results support a critical role for the conserved C176 in catalysis and an oxygen atom transfer mechanism for the catalytic reduction of nitrate to nitrite that does not employ a terminal sulfido ligand in the catalytic cycle.
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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.
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    Structural analysis of bengamide derivatives as inhibitors of methionine aminopeptidases
    (ACS, 2012) Xu, Wei; Lu, Jing-Ping; Ye, Qi-Zhuang; Biochemistry and Molecular Biology, School of Medicine
    Natural-product-derived bengamides possess potent antiproliferative activity and target human methionine aminopeptidases (MetAPs) for their cellular effects. Several derivatives were designed, synthesized, and evaluated as MetAP inhibitors. Here, we present four new X-ray structures of human MetAP1 in complex with the inhibitors. Together with the previous structures of bengamide derivatives with human MetAP2 and tubercular MtMetAP1c, analysis of the interactions of these inhibitors at the active site provides structural basis for further modification of these bengamide inhibitors for improved potency and selectivity as anticancer and antibacterial therapeutics.
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    Structure-Based Design of Active-Site-Directed, Highly Potent, Selective, and Orally Bioavailable Low-Molecular-Weight Protein Tyrosine Phosphatase Inhibitors
    (American Chemical Society, 2022) He, Rongjun; Wang, Jifeng; Yu, Zhi-Hong; Moyers, Julie S.; Michael, M. Dodson; Durham, Timothy B.; Cramer, Jeff W.; Qian, Yuewei; Lin, Amy; Wu, Li; Noinaj, Nicholas; Barrett, David G.; Zhang, Zhong-Yin; Biochemistry and Molecular Biology, School of Medicine
    Protein tyrosine phosphatases constitute an important class of drug targets whose potential has been limited by the paucity of drug-like small-molecule inhibitors. We recently described a class of active-site-directed, moderately selective, and potent inhibitors of the low-molecular-weight protein tyrosine phosphatase (LMW-PTP). Here, we report our extensive structure-based design and optimization effort that afforded inhibitors with vastly improved potency and specificity. The leading compound inhibits LMW-PTP potently and selectively (Ki = 1.2 nM, >8000-fold selectivity). Many compounds exhibit favorable drug-like properties, such as low molecular weight, weak cytochrome P450 inhibition, high metabolic stability, moderate to high cell permeability (Papp > 0.2 nm/s), and moderate to good oral bioavailability (% F from 23 to 50% in mice), and therefore can be used as in vivo chemical probes to further dissect the complex biological as well as pathophysiological roles of LMW-PTP and for the development of therapeutics targeting LMW-PTP.