Self-organizing neural networks bridge the biomolecular resolution gap

Willy Wriggers, Ronald A. Milligan, Klaus Schulten, J. Andrew McCammon

Research output: Contribution to journalArticlepeer-review

Abstract

Topology-representing neural networks are employed to generate pseudo-atomic structures of large-scale protein assemblies by combining high-resolution data with volumetric data at lower resolution. As an application example, actin monomers and structural subdomains are located in a three-dimensional (3D) image reconstruction from electron micrographs. To test the reliability of the method, the resolution of the atomic model of an actin polymer is lowered to a level typically encountered in electron microscopic reconstructions. The atomic model is restored with a precision nine times the nominal resolution of the corresponding low-resolution density. The presented self-organizing computing method may be used as an information-processing tool for the synthesis of structural data from a variety of biophysical sources.

Original languageEnglish (US)
Pages (from-to)1247-1254
Number of pages8
JournalJournal of Molecular Biology
Volume284
Issue number5
DOIs
StatePublished - Dec 18 1998

Keywords

  • Actin
  • Electron microscopy
  • Macromolecular assemblies
  • Multi-resolution
  • Representing networks
  • Topology

ASJC Scopus subject areas

  • Virology

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