Willy Wriggers, Ronald A. Milligan, Klaus Schulten, and J. Andrew McCammon. Self-Organizing Neural Networks Bridge the Biomolecular Resolution Gap. Journal of Molecular Biology. 1998, 284:1247-1254.

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 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.

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