Open AccessFast Fourier feature recognition
Author(s) -
Cowtan Kevin
Publication year - 2001
Publication title -
acta crystallographica section d
Language(s) - English
Resource type - Journals
ISSN - 1399-0047
DOI - 10.1107/s0907444901010812
Subject(s) - weighting , fourier transform , computer science , pattern recognition (psychology) , feature (linguistics) , function (biology) , task (project management) , artificial intelligence , identification (biology) , algorithm , interpretation (philosophy) , data mining , physics , mathematics , engineering , mathematical analysis , programming language , linguistics , philosophy , botany , systems engineering , evolutionary biology , acoustics , biology
Various approaches have been demonstrated for the automatic interpretation of crystallographic data in terms of atomic models. The use of a masked Fourier‐based search function has some benefits for this task. The application and optimization of this procedure is discussed in detail. The search function also acquires a statistical significance when used with an appropriate electron‐density target and weighting, giving rise to improved results at low resolutions. Methods are discussed for building a library of protein fragments suitable for use with this procedure. These methods are demonstrated with the construction of a statistical target for the identification of short helical fragments in the electron density.