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Can I solve my structure by SAD phasing? Planning an experiment, scaling data and evaluating the useful anomalous correlation and anomalous signal
Author(s) -
Terwilliger Thomas C.,
Bunkóczi Gábor,
Hung Li-Wei,
Zwart Peter H.,
Smith Janet L.,
Akey David L.,
Adams Paul D.
Publication year - 2016
Publication title -
acta crystallographica section d
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.374
H-Index - 138
ISSN - 2059-7983
DOI - 10.1107/s2059798315019403
Subject(s) - scaling , phaser , correlation , substructure , algorithm , data mining , signal (programming language) , data set , computer science , merge (version control) , set (abstract data type) , pattern recognition (psychology) , mathematics , artificial intelligence , physics , information retrieval , engineering , optics , geometry , structural engineering , programming language
A key challenge in the SAD phasing method is solving a structure when the anomalous signal‐to‐noise ratio is low. Here, algorithms and tools for evaluating and optimizing the useful anomalous correlation and the anomalous signal in a SAD experiment are described. A simple theoretical framework [Terwilliger et al. (2016), Acta Cryst. D 72 , 346–358] is used to develop methods for planning a SAD experiment, scaling SAD data sets and estimating the useful anomalous correlation and anomalous signal in a SAD data set. The phenix.plan_sad_experiment tool uses a database of solved and unsolved SAD data sets and the expected characteristics of a SAD data set to estimate the probability that the anomalous substructure will be found in the SAD experiment and the expected map quality that would be obtained if the substructure were found. The phenix.scale_and_merge tool scales unmerged SAD data from one or more crystals using local scaling and optimizes the anomalous signal by identifying the systematic differences among data sets, and the phenix.anomalous_signal tool estimates the useful anomalous correlation and anomalous signal after collecting SAD data and estimates the probability that the data set can be solved and the likely figure of merit of phasing.