xia2.multiplex: a multi-crystal data-analysis pipeline

Area of Science:

• Structural Biology
• Crystallography
• Biophysics

Background:

• Radiation damage limits data collection from single crystals in macromolecular crystallography.
• Merging data from multiple crystals is often necessary, especially for micro-crystals or in situ experiments.
• Existing software faces challenges in merging data from small wedges, including symmetry identification and handling poor-quality datasets.

Purpose of the Study:

• To develop and present a new program, xia2.multiplex, for optimizing the scaling and merging of multi-crystal datasets.
• To address challenges in multi-crystal data merging, such as symmetry analysis and pathology detection.
• To demonstrate the utility of xia2.multiplex in facilitating complex crystallographic experiments.

Main Methods:

• The xia2.multiplex program integrates with DIALS for individual dataset integration.
• It performs automated symmetry analysis, scaling, and merging of multi-crystal data.
• The program analyzes common issues like non-isomorphism, radiation damage, and preferential orientation.

Main Results:

• xia2.multiplex successfully facilitates the scaling and merging of multi-crystal datasets.
• The program aids in identifying consensus symmetry and handling indexing ambiguities.
• It effectively analyzes and accounts for pathologies affecting data quality.

Conclusions:

• xia2.multiplex is a valuable tool for optimizing multi-crystal data merging in macromolecular crystallography.
• Its implementation within an autoprocessing framework enhances experimental efficiency.
• The program proved beneficial in fragment-screening experiments on SARS-CoV-2 main protease, demonstrating its practical application.