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TitleParametrically guided design of beta barrels and transmembrane nanopores using deep learning.
Journal, issue, pagesProc. Natl. Acad. Sci. USA, Vol. 122, Page e2425459122-e2425459122, Year 2025
Publish dateAug 27, 2025 (structure data deposition date)
AuthorsKim, D.E. / Watson, J.L. / Juergens, D. / Majumder, S. / Sonigra, R. / Gerben, S.R. / Kang, A. / Bera, A.K. / Li, X. / Baker, D.
External linksProc. Natl. Acad. Sci. USA / PubMed:40953261
MethodsX-ray diffraction
Resolution2.03 Å
Structure data

PDB-9xzt:
Crystal structure of BBn6
Method: X-RAY DIFFRACTION / Resolution: 2.03 Å

Chemicals

ChemComp-PG6:
1-(2-METHOXY-ETHOXY)-2-{2-[2-(2-METHOXY-ETHOXY]-ETHOXY}-ETHANE

ChemComp-SO4:
SULFATE ION

ChemComp-PG4:
TETRAETHYLENE GLYCOL / precipitant*YM

ChemComp-HOH:
WATER

Source
  • synthetic construct (others)
KeywordsDE NOVO PROTEIN / de novo design / parametric / beta-barrel / sequence-structure relation ship / machine learning / nanopore

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