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Updated: May 28, 2026

Bimolecular Fluorescence Complementation

Bimolecular Fluorescence Complementation

Published on: April 15, 2011

Some B(eq) are more equivalent than others.

Ethan A Merritt¹

¹Department of Biochemistry, University of Washington, Seattle, WA 98195-7742, USA. merritt@u.washington.edu

Acta Crystallographica. Section A, Foundations of Crystallography

|October 21, 2011

Summary

A new crystallographic parameter, B(est), estimates isotropic displacement parameters from anisotropic models more accurately than B(eq). This improves comparisons between isotropic and anisotropic structural models, potentially enhancing multi-group TLS model selection.

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Last Updated: May 28, 2026

Bimolecular Fluorescence Complementation

Bimolecular Fluorescence Complementation

Published on: April 15, 2011

Area of Science:

Crystallography
Structural Biology
Computational Chemistry

Background:

Macromolecular crystallographic models traditionally use isotropic displacement parameters B(iso).
Anisotropic displacement parameters U(ij) can be used, with B(eq) derived for comparison.
B(eq) is an imperfect predictor of hypothetical B(iso) from isotropic refinement.

Purpose of the Study:

Introduce a new parameter, B(est), for estimating B(iso) from anisotropic models.
Improve the accuracy of comparing isotropic and anisotropic crystallographic models.
Enhance the selection of multi-group thermal motion models.

Main Methods:

Proposed B(est) parameter minimizes Kullback-Leibler divergence between isotropic and anisotropic models.
Compared B(est) and B(eq) as predictors of hypothetical B(iso).
Evaluated the impact on multi-group TLS model selection.

Main Results:

B(est) generally shows B(est)/B(eq) < 1.
The difference between B(est) and B(eq) increases with atomic anisotropy.
B(est) provides a more accurate estimate of B(iso) than B(eq).

Conclusions:

B(est) offers a superior estimation of isotropic displacement parameters from anisotropic models.
The findings are crucial for analyses comparing different crystallographic refinement models.
Improved model selection for multi-group TLS analysis is anticipated.