The nonheme iron dioxygenase 2-(trimethylammonio)-ethylphosphonate dioxygenase (TmpA) is an enzyme involved in the regio- and chemoselective hydroxylation at the C -position of the substrate as part of the biosynthesis of glycine betaine in bacteria and carnitine in humans. To understand how the enzyme avoids breaking the weak C -H bond in favor of C -hydroxylation, we set up a cluster model of 242 atoms representing the first and second coordination sphere of the metal center and substrate binding pocket, and investigated possible reaction mechanisms of substrate activation by an iron(IV)-oxo species by density functional theory methods. In agreement with experimental product distributions, the calculations predict a favorable C -hydroxylation pathway. The calculations show that the selectivity is guided through electrostatic perturbations inside the protein from charged residues, external electric fields and electric dipole moments. In particular, charged residues influence and perturb the homolytic bond strength of the C -H and C -H bonds of the substrate, and strongly strengthens the C -H bond in the substrate-bound orientation.
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http://dx.doi.org/10.1002/chem.202100791 | DOI Listing |
Alchemical free energy methods using molecular mechanics (MM) force fields are essential tools for predicting thermodynamic properties of small molecules, especially via free energy calculations that can estimate quantities relevant for drug discovery such as affinities, selectivities, the impact of target mutations, and ADMET properties. While traditional MM forcefields rely on hand-crafted, discrete atom types and parameters, modern approaches based on graph neural networks (GNNs) learn continuous embedding vectors that represent chemical environments from which MM parameters can be generated. Excitingly, GNN parameterization approaches provide a fully end-to-end differentiable model that offers the possibility of systematically improving these models using experimental data.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States.
Stacking interactions are a recurring motif in supramolecular chemistry and biochemistry, where a persistent theme is a preference for parallel-displaced aromatic rings rather than face-to-face π-stacking. This is typically explained in terms of quadrupole-quadrupole interactions between the arene moieties but that interpretation is inconsistent with accurate calculations, which reveal that the quadrupolar picture is qualitatively wrong. At typical π-stacking distances, quadrupolar electrostatics may differ in sign from an exact calculation based on charge densities of the interacting arenes.
View Article and Find Full Text PDFACS Nano
January 2025
Medical Research Center, The First Affiliated Hospital of Zhengzhou University, The Center of Infection and Immunity, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China.
Tumor-specific T cells play a vital role in potent antitumor immunity. However, their efficacy is severely affected by the spatiotemporal orchestration of antigen-presentation as well as the innate immune response in dendritic cells (DCs). Herein, we develop a minimalist nanovaccine that exploits a dual immunofunctional polymeric nanoplatform (DIPNP) to encapsulate ovalbumin (OVA) via electrostatic interaction when the nanocarrier serves as both STING agonist and immune adjuvant in DCs.
View Article and Find Full Text PDFNature
January 2025
Frontiers Medical Center, Tianfu Jincheng Laboratory, Chengdu, China.
Identifying phase-separated structures remains challenging, and effective intervention methods are currently lacking. Here we screened for phase-separated proteins in breast tumour cells and identified forkhead (FKH) box protein M1 (FOXM1) as the most prominent candidate. Oncogenic FOXM1 underwent liquid-liquid phase separation (LLPS) with FKH consensus DNA element, and compartmentalized the transcription apparatus in the nucleus, thereby sustaining chromatin accessibility and super-enhancer landscapes crucial for tumour metastatic outgrowth.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Physics, Shahid Beheshti University, Tehran, 1983969411, Iran.
Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces. We challenge the commonly accepted assumption that the contribution of an atom can be learned from the short-range local environment of that atom. We employ density functional theory calculations to quantify the decay of the induced electron density and electrostatic potential in response to local perturbations throughout insulating, semiconducting and metallic samples of different dimensionalities.
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