Effect of crystal-to-detector distance shift on data processing in serial crystallography.
Serial crystallography (SX) is an emerging technique to determine the room-temperature structure of macromolecules while minimizing any radiation damage. In SX, many crystals are continuously delivered to the X-ray interaction points. Crystal-to-detector distance (CTDD), an important data processing...
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327019 |
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| Summary: | Serial crystallography (SX) is an emerging technique to determine the room-temperature structure of macromolecules while minimizing any radiation damage. In SX, many crystals are continuously delivered to the X-ray interaction points. Crystal-to-detector distance (CTDD), an important data processing parameter, changes during sample delivery. However, the ways in which these variations affect the data quality remain unclear. Therefore, this study aimed to assess the impact of CTDD accuracy on SX data quality. Serial synchrotron crystallographic data of hen egg white lysozyme (HEWL) and glucose isomerase (GI) were processed at various CTDDs using three indexing algorithms. The CTDD that yielded the maximum number of indexed images differed from the optimized CTDD, and the resulting indexing trends varied depending on the algorithm used. The indexed data near the actual CTDD exhibited a Gaussian unit cell distribution pattern; however, this pattern became distorted as the CTDD deviated from the actual CTDD value, resulting in changes in the indexed unit cell dimensions. The phase problem was successfully solved even when HEWL and GI data deviated by ±10 mm from the actual CTDD, excluding one dataset. For HEWL processed via MOSFLM, structure refinement statistics maintained acceptable Rfree values despite a ± 10 mm deviation; however, data quality deteriorated with increasing deviation from the actual CTDD. Collectively, these results provide valuable insights for efficient SX data processing and interpretation. |
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| ISSN: | 1932-6203 |