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Estimation of the quality of refined protein crystal structures
Author(s) -
Wang Jimin
Publication year - 2015
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.2639
Subject(s) - metric (unit) , quality (philosophy) , resolution (logic) , protein data bank , computer science , goodness of fit , work (physics) , algorithm , data mining , statistics , mathematics , statistical physics , physics , protein structure , artificial intelligence , engineering , thermodynamics , operations management , nuclear magnetic resonance , quantum mechanics
Crystallographic R work and R free values, which are measures of the ability of the models of macromolecular structures to explain the crystallographic data on which they are based, are often used to assess structure quality. It is widely known, and confirmed here that both are sensitive to the methods used to compute them, and can be manipulated to improve the apparent quality of the model. As an alternative it is proposed here that the quality of crystallographic models should be assessed using a global goodness‐of‐fit metric R O2A / R work where R O2A is the number of reflections used for refinement divided by the number of nonhydrogen atoms in the structure, and R work is the working R ‐factor of the refined structure. Also, analysis of structures in the Protein Data Bank suggests that many data sets have been truncated at high resolution, thereby improving the R ‐factor statistics. To discourage this practice, it is proposed that the resolution of a dataset be defined as the resolution of the shell of data where < I /σ I > falls to 1. The proposed goodness‐of‐fit metric encourages investigators to use all the data available rather than a truncated subset.