A log‐likelihood‐gain intensity target for crystallographic phasing that accounts for experimental error. Issue 3 (1st March 2016)

• Record Type: Journal Article
• Authors: Read, Randy J.; McCoy, Airlie J.
• Is Part Of: Acta crystallographica. Volume 72:Issue 3(2016)
• Page Start: 375
• Page End: 387
• Publication Date: 2016-03-01
• Subjects: intensity‐measurement errors -- likelihood; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; Molecular biology -- Periodicals; Molecular structure -- Periodicals; Biomolecules -- Structure -- Periodicals; Cytology -- Periodicals; Biomolecules -- Structure; Crystallography; Cytology; Molecular biology; Molecular structure; X-ray crystallography; Periodicals; 548
• DOI: 10.1107/S2059798315013236
• Languages: English
• ISSNs: 2059-7983

Abstract

Abstract : A new Rice‐function approximation for the effect of intensity‐measurement errors improves the treatment of weak intensity data in calculating log‐likelihood‐gain scores in crystallographic applications including experimental phasing, molecular replacement and refinement. Abstract : The crystallographic diffraction experiment measures Bragg intensities; crystallographic electron‐density maps and other crystallographic calculations in phasing require structure‐factor amplitudes. If data were measured with no errors, the structure‐factor amplitudes would be trivially proportional to the square roots of the intensities. When the experimental errors are large, and especially when random errors yield negative net intensities, the conversion of intensities and their error estimates into amplitudes and associated error estimates becomes nontrivial. Although this problem has been addressed intermittently in the history of crystallographic phasing, current approaches to accounting for experimental errors in macromolecular crystallography have numerous significant defects. These have been addressed with the formulation of LLGI, a log‐likelihood‐gain function in terms of the Bragg intensities and their associated experimental error estimates. LLGI has the correct asymptotic behaviour for data with large experimental error, appropriately downweighting these reflections without introducing bias. LLGI abrogates the need for the conversion of intensity data to amplitudes, which is usually performed with the French and Wilson method [French & Wilson (1978), Acta Cryst. A35, 517–525], wherever likelihood target functions are required. It has general applicability for a wide variety of algorithms in macromolecular crystallography, including scaling, characterizing anisotropy and translational noncrystallographic symmetry, detecting outliers, experimental phasing, molecular replacement and refinement. Because it is impossible to reliably recover the original intensity data from amplitudes, it is suggested that crystallographers should always deposit the intensity data in the Protein Data Bank.