title: Maximum-likelihood density modification using pattern recognition of structural motifs.
abstract: The likelihood-based approach to density modification [Terwilliger (2000), Acta Cryst. D56, 965-972] is extended to include the recognition of patterns of electron density. Once a region of electron density in a map is recognized as corresponding to a known structural element, the likelihood of the map is reformulated to include a term that reflects how closely the map agrees with the expected density for that structural element. This likelihood is combined with other aspects of the likelihood of the map, including the presence of a flat solvent region and the electron-density distribution in the protein region. This likelihood-based pattern-recognition approach was tested using the recognition of helical segments in a largely helical protein. The pattern-recognition method yields a substantial phase improvement over both conventional and likelihood-based solvent-flattening and histogram-matching methods. The method can potentially be used to recognize any common structural motif and incorporate prior knowledge about that motif into density modification.
authors.affiliation: Bioscience Division, Mail Stop M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. terwilliger@lanl.gov
authors.forename: T C
authors.initials: TC
authors.lastname: Terwilliger
mesh_terms: D020816:Amino Acid Motifs; D004583:Electrons; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008958:Models, Molecular; D010363:Pattern Recognition, Automated; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins