Open AccessMaximum‐likelihood density modification using pattern recognition of structural motifs
Author(s) -
Terwilliger Thomas C.
Publication year - 2001
Publication title -
acta crystallographica section d
Language(s) - English
Resource type - Journals
ISSN - 1399-0047
DOI - 10.1107/s0907444901013737
Subject(s) - pattern recognition (psychology) , flattening , artificial intelligence , structural motif , histogram , maximum likelihood , computer science , motif (music) , mathematics , chemistry , materials science , statistics , physics , image (mathematics) , biochemistry , acoustics , composite material
The likelihood‐based approach to density modification [Terwilliger (2000), Acta Cryst. D 56 , 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.