Crystal diffraction prediction and partiality estimation using Gaussian basis functions

The recent diversification of macromolecular crystallographic experiments including the use of pink beams, convergent electron diffraction and serial snapshot crystallography has shown the limitations of using the Laue equations for diffraction prediction. This article gives a computationally efficient way of calculating approximate crystal diffraction patterns given varying distributions of the incoming beam, crystal shapes and other potentially hidden parameters. This approach models each pixel of a diffraction pattern and improves data processing of integrated peak intensities by enabling the correction of partially recorded reflections. The fundamental idea is to express the distributions as weighted sums of Gaussian functions. The approach is demonstrated on serial femtosecond crystallography data sets, showing a significant decrease in the required number of patterns to refine a structure to a given error.

Automated summary

The recent diversification of macromolecular crystallographic experiments has exposed the limitations of using the Laue equations for diffraction prediction. This article presents a computationally efficient method for calculating approximate crystal diffraction patterns based on different distributions of the incoming beam, crystal shapes, and other hidden parameters. The method models each pixel of a diffraction pattern, improving data processing by enabling the correction of partially recorded reflections. By expressing distributions as weighted sums of Gaussian functions, the approach reduces the number of patterns needed for refining a structure to a given error.