title: Self-organizing neural networks bridge the biomolecular resolution gap.
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.
authors.affiliation: Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla 92093-0365, USA. wriggers@ucsd.edu
authors.forename: W
authors.initials: W
authors.lastname: Wriggers
authors.forename: R A
authors.initials: RA
authors.lastname: Milligan
authors.forename: K
authors.initials: K
authors.lastname: Schulten
authors.forename: J A
authors.initials: JA
authors.lastname: McCammon
mesh_terms: D000199:Actins / Q000737:chemistry*; D000465:Algorithms; D007091:Image Processing, Computer-Assisted; D008854:Microscopy, Electron; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
chemical_list: D000199:Actins; D011506:Proteins