Scaling and merging macromolecular diffuse scattering with mdx2

Abstract

Diffuse scattering is a promising method to gain additional insight into protein dynamics from macro-molecular crystallography (MX) experiments. Bragg intensities yield the average electron density, while the diffuse scattering can be processed to obtain a three-dimensional reciprocal space map, that is further analyzed to determine correlated motion. To make diffuse scattering techniques more accessible, we have created software for data processing called mdx2 that is both convenient to use and simple to extend and modify. Mdx2 is written in Python, and it interfaces with DIALS to implement self-contained data reduction workflows. Data are stored in NeXusformat for software interchange and convenient visualization. Mdx2 can be run on the command line or imported as a package, for instance to encapsulate a complete workflow in a Jupyter notebook for reproducible computing and education. Here, we describe mdx2 version 1.0, a new release incorporating state-of-the-art techniques for data reduction. We describe the implementation of a complete multi-crystal scaling and merging workflow, and test the methods using a high-redundancy dataset from cubic insulin. We show that redundancy can be leveraged during scaling to correct systematic errors, and obtain accurate and reproducible measurements of weak diffuse signals. SynopsisMdx2 is a Python toolkit for processing diffuse scattering data from macromolecular crystals. We describe multi-crystal scaling and merging procedures implemented in the latest version of mdx2. A high-redundancy dataset from cubic insulin is processed to reveal weak scattering features.