A deep learning-driven discovery of berberine derivatives as novel antibacterial against multidrug-resistant Helicobacter pylori through targeting outer membrane protein transport and assembling

ABSTRACT: Helicobacter pylori (H. pylori) is currently recognized as the primary carcinogenic pathogen associated with gastric tumorigenesis, and its high prevalence and resistance make it difficult to tackle. A graph neural network-based deep learning model, employing different training sets of 13,638 molecules for pre-training and fine-tuning through rigorous iterative learning processes, was aided in predicting and exploring novel molecules against H. pylori. A positively predicted novel berberine (BBR) derivative 8 with 3,13-disubstituted alkene exhibited a potency against all tested drug-susceptible and resistant H. pylori strains with minimum inhibitory concentrations (MICs) of 0.25–0.5 μg/mL. Strikingly, pharmacokinetic studies demonstrated an ideal gastric retention of 8, with the stomach concentration significantly higher than its MIC value at 24 h post dose. Oral administration of 8 and proton pump inhibitor omeprazole (OPZ) showed a 2.2-log reduction in the gastric bacterial burden compared with the control group, which is comparable to the triple-therapy, namely OPZ + amoxicillin (AMX) + clarithromycin (CLA), and partially restored the diversity of the intestinal flora as well as the abundance of probiotics. A combination of OPZ + AMX + CLA + 8 could further decrease the bacteria load (2.8-log reduction). More importantly, the mono-therapy of 8 showed comparable eradicative efficacies compared with both triple-therapy (OPZ + AMX + CLA) and the quadruple-therapy (OPZ + AMX + CLA + bismuth citrate) groups. SecA and BamD, playing a major role in outer membrane protein (OMP) transport and assembling, were identified and verified as the direct targets of 8 by employing the chemoproteomics technique. The treatment of 8 induced the death of H. pylori caused by OMP deficiency, and the subsequent reduced adhesion to gastric epithelial cells. In summary, by targeting the relatively conserved OMPs transport and assembling system, 8 has the potential to be developed as a novel anti-H. pylori candidate, especially for the eradication of drug-resistant strains. The deep learning model established in this study might provide a reliable prediction tool for future anti-H. pylori candidate discovery.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Helicobacter Pylori Nctc 11637

TISSUE(S): Thallus

SUBMITTER: Sissi Guo  

LAB HEAD: Wang yanxiang

PROVIDER: PXD052333 | Pride | 2024-07-03

REPOSITORIES: Pride