Accurate assessment of ligand coordinate–density consistency across different resolutions remains challenging in macromolecular crystallography. We introduce the atomic Box Correlation Coefficient (aBCC), an atom-level metric for evaluating the consistency between ligand atomic coordinates and electron density in a resolution-standardized framework. To predict aBCC values from electron-density maps, we developed QAEmap, a machine-learning model based on three-dimensional convolutional neural networks (3D-CNNs). The model was trained using Fourier-truncated electron-density maps and corresponding ligand coordinates generated from high-resolution structures in the Protein Data Bank. It was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures. was evaluated using both Fourier-truncated electron-density maps and experimentally determined PDB structures.The prediction accuracy gradually decreased with decreasing resolution, but remained reliable up to ∼3.5 Å. These results demonstrate that aBCC enables resolution-standardized atom-wise evaluation of coordinate–density consistency across different resolutions and provide a foundation for further development and refinement of machine learning-based coordinate validation. Synopsis We introduce the atomic box correlation coefficient (aBCC), a machine learning-based metric for the resolution-standardized atom-level evaluation of ligand coordinate–density consistency in crystallographic structures. aBCC provides a common framework for assessing and communicating the local coordinate reliability between structural biologists and researchers in structure-based drug discovery.
I. Miyaguchi, H. Hata, Takaaki Kuribayashi et al.· bioRxiv· 0 citations
Flavonoids are polyphenolic natural products predominantly isolated from plants and exhibit a diverse array of biological activities. Because they serve as important pharmaceuticals and nutraceuticals, there is a strong demand for their sustainable supply. Chlorflavonin is a rare fungal flavonoid with potent antitubercular activity. While its biosynthetic enzymes are expected to be valuable tools for application in fungal production of flavonoids, the biosynthetic pathway remains unknown. Here, we elucidate the complete biosynthetic pathway for chlorflavonin through detailed functional analysis of each biosynthetic enzyme. Previously, stepwise and straightforward introduction of 3-, 7-, and 8-methoxy; 2'-hydroxy; and 3'-chloro functionalities have been proposed. In contrast to this proposal, we uncovered an intricate biosynthetic route involving a dynamic interconversion between the 6- and 8-methoxy forms of flavonoid skeletons mediated by the chalcone isomerase CfvF and the flavin-dependent oxygenase CfvI. CfvF interconverted the 6- and 8-methoxyflavanones, likely via a chalcone intermediate. CfvI oxidized the chemically inert 2,3-double bond of flavanone, giving the hemiacetal product. Although these enzymes generate products existing in equilibrium states, respective downstream enzymes catalyze selective conversion of one specific species, facilitating smooth progression to the final product. We also solved the crystal structure of CfvK, the dehydratase that selectively converts the 8-methoxy form of the CfvI product. Through analyzing the structure complexed with its substrate and product and site-directed mutagenesis, key residues determining the substrate selectivity were identified. Our comprehensive analysis established a rational framework for preparing 46 flavonoids, including unnatural ones, setting the stage for fungal production of structurally diverse flavonoids.
Sho Furumura, T. Ozaki, Kazuya Hasegawa et al.· Journal of the American Chem...· 0 citations
GHBfix-18Ab, an 18-component hydrogen-bond correction that distinguishes NH and NH□ donors, is developed, demonstrating that targeted refinement of hydrogen-bond interactions provides a practical strategy for systematic improvement of RNA force fields toward more accurate modeling of noncanonical RNA motifs.
