Database of articles cited by EMDB/PDB/SASBDB data

Keywords
Structure methods	All X-ray diffraction NMR electron microscopy small angle scattering neutron diffraction fiber diffraction powder diffraction infrared spectroscopy EPR fluorescence transfer theoretical model Hybrid
Author
Journal
IF	>30 >20 >10 >5 all

Title	Deep learning driven de novo drug design based on gastric proton pump structures.
Journal, issue, pages	Commun Biol, Vol. 6, Issue 1, Page 956, Year 2023
Publish date	Sep 19, 2023
Authors	Kazuhiro Abe / Mami Ozako / Miki Inukai / Yoe Matsuyuki / Shinnosuke Kitayama / Chisato Kanai / Chiaki Nagai / Chai C Gopalasingam / Christoph Gerle / Hideki Shigematsu / Nariyoshi Umekubo / Satoshi Yokoshima / Atsushi Yoshimori /
PubMed Abstract	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. ...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 K 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.
External links	Commun Biol / PubMed:37726448 / PubMed Central
Methods	EM (single particle)
Resolution	2.08 - 2.26 Å
Structure data	35500 8ijv EMDB-35500, PDB-8ijv: Cryo-EM structure of the gastric proton pump with bound DQ-02 Method: EM (single particle) / Resolution: 2.1 Å 35501 8ijw EMDB-35501, PDB-8ijw: Cryo-EM structure of the gastric proton pump with bound DQ-06 Method: EM (single particle) / Resolution: 2.19 Å 35502 8ijx EMDB-35502, PDB-8ijx: Cryo-EM structure of the gastric proton pump with bound DQ-18 Method: EM (single particle) / Resolution: 2.08 Å 36424 8jmn EMDB-36424, PDB-8jmn: Cryo-EM structure of the gastric proton pump with bound DQ-21 Method: EM (single particle) / Resolution: 2.26 Å
Chemicals	ChemComp-PCW: 1,2-DIOLEOYL-SN-GLYCERO-3-PHOSPHOCHOLINE / DOPC, phospholipidYM ChemComp-MG: Unknown entry ChemComp-PWI: 4-[[5-chloranyl-2-(4-chlorophenyl)phenyl]methoxy]-N-methyl-but-2-yn-1-amine ChemComp-NAG: 2-acetamido-2-deoxy-beta-D-glucopyranose / N-Acetylglucosamine ChemComp-CLR: CHOLESTEROL / Cholesterol ChemComp-HOH: WATER / Water ChemComp-PXR: N-methyl-1-[4-[(2-phenylmethoxycyclohexa-1,3-dien-1-yl)methoxy]cyclohexa-1,3-dien-1-yl]methanamine ChemComp-PZ0: 1-[4-[(5-chloranyl-2-phenylmethoxy-phenyl)methoxy]phenyl]-N-methyl-methanamine ChemComp-UOU*: 1-[4-[[2-[(4-chlorophenyl)methoxy]phenyl]methoxy]phenyl]-N-methyl-methanamine
Source	sus scrofa (pig)
Keywords	MEMBRANE PROTEIN / P-type ATPase / P2-type ATPase / proton pump / gastric