Structural Basis for Binding of Potassium-Competitive Acid Blockers to the Gastric Proton Pump

The role of the methoxy group in approved drugs

The methoxy substituent is prevalent in natural products and, consequently, is present in many natural product-derived drugs. It has also been installed in modern drug molecules with no remnant of natural product features because medicinal chemists have been taking advantage of the benefits that this small functional group can bestow on ligand-target binding, physicochemical properties, and ADME parameters. Herein, over 230 methoxy-containing small-molecule drugs, as well as several fluoromethoxy-containing drugs, are presented from the vantage point of the methoxy group. Biochemical mechanisms of action, medicinal chemistry SAR studies, and numerous X-ray cocrystal structures are analyzed to identify the precise role of the methoxy group for many of the drugs and drug classes. Although the methoxy substituent can be considered as the hybridization of a hydroxy and a methyl group, the combination of these functionalities often results in unique effects that can amount to more than the sum of the individual parts.

Accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and specialized U-Net

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence (AI)-based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape, and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.32 Å, 7% better than 3.57 Å of Topaz and 14% better than 3.85 Å of crYOLO.

Brasiliensic acid: in vitro cytotoxic and genotoxic, in vivo acute toxicity and in silico pharmacological prediction of a new promising molecule

Brasiliensic acid (Bras) is a chromanone isolated from Calophyllum brasiliense Cambèss. bark extracts with confirmed potential activity on gastric ulcer and Helicobacter pylori infection. This study aimed to investigate the in vitro and in vivo toxicity of Bras and molecular docking studies on its interactions with the H. pylori virulence factors and selected gastric cancer-related proteins. Cytotoxicity was evaluated by alamarBlue© assay, genotoxicity by micronucleus and comet assays, and on cell cycle by flow cytometry, using Chinese hamster epithelial ovary cells. Bras was not cytotoxic to CHO-K1 cells, and caused no chromosomal aberrations, nor altered DNA integrity. Furthermore, Bras inhibited damages to DNA by H2O2 at 1.16 µM. No cell cycle arrest was observed, but apoptosis accounted for 31.2% of the cell death observed in the CHO-K1 at 24 h incubation of the IC50. Oral acute toxicity by Hippocratic screening test in mice showed no relevant behavioral change/mortality seen up to 1,000 mg/kg. The molecular docking approach indicated potential interactions between Bras and the various targets for peptic ulcer and gastric cancer, notably CagA virulence factor of H. pylori and VEGFR-2. In conclusion, Bras is apparently safe and an optimization for Bras can be considered for gastric ulcer and cancer.Communicated by Ramaswamy H. Sarma.

CryoTransformer: A Transformer Model for Picking Protein Particles from Cryo-EM Micrographs

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise (SNR) ratios. To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labelled cryo-EM protein particle dataset - CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score and is poised to facilitate the automation of the cryo-EM protein particle picking.

Deep learning driven de novo drug design based on gastric proton pump structures

Existing drugs often suffer in their effectiveness due to detrimental side effects, low binding affinity or pharmacokinetic problems. This may be overcome by the development of distinct compounds. Here, we exploit the rich structural basis of drug-bound gastric proton pump to develop compounds with strong inhibitory potency, employing a combinatorial approach utilizing deep generative models for de novo drug design with organic synthesis and cryo-EM structural analysis. Candidate compounds that satisfy pharmacophores defined in the drug-bound proton pump structures, were designed in silico utilizing our deep generative models, a workflow termed Deep Quartet. Several candidates were synthesized and screened according to their inhibition potencies in vitro, and their binding poses were in turn identified by cryo-EM. Structures reaching up to 2.10 Å resolution allowed us to evaluate and re-design compound structures, heralding the most potent compound in this study, DQ-18 (N-methyl-4-((2-(benzyloxy)-5-chlorobenzyl)oxy)benzylamine), which shows a Ki value of 47.6 nM. Further high-resolution cryo-EM analysis at 2.08 Å resolution unambiguously determined the DQ-18 binding pose. Our integrated approach offers a framework for structure-based de novo drug development based on the desired pharmacophores within the protein structure.

A large expert-curated cryo-EM image dataset for machine learning protein particle picking

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.