This article was originally published here
Acta Crystallogr D Struct Biol. 2022 Jun 1;78(Pt 6):752-769. doi: 10.1107/S2059798322004399. Published online May 18, 2022.
In macromolecular crystallography, radiation damage limits the amount of data that can be collected from a single crystal. It is often necessary to merge data sets from multiple crystals; for example, small corner data collection from microcrystals, in situ data collection at room temperature, and data collection from membrane proteins in lipid mesophases. While indexing and integrating individual datasets can be relatively straightforward with existing software, merging multiple datasets from small corners presents new challenges. The identification of a consensual symmetry can be problematic, especially in the presence of a possible indexing ambiguity. Additionally, the presence of non-isomorphic or poor quality datasets can reduce the overall quality of the final merged dataset. To facilitate and help optimize the scaling and merging of multiple datasets, a new program, xia2.multiplex, has been developed which takes datasets integrated individually with DIALS and performs symmetry analysis, scaling and fusion of multi-crystal datasets. xia2.multiplex also performs analysis for various pathologies that commonly affect multicrystal datasets, including non-isomorphism, radiation damage, and preferential orientation. After describing a number of use cases, the benefit of xia2.multiplex is demonstrated in a broader self-processing framework to facilitate a multi-crystal experiment collected from in situ fragment screening experiments at room temperature on the SARS-CoV-2 main protease.
PMID:35647922 | DOI:10.1107/S2059798322004399