Deep residual networks for crystallography trained on synthetic data

Mendez, D.; Holton, J.M.; Lyubimov, A.Y.; Hollatz, S.; Mathews, I.I.; Cichosz, A.; Martirosyan, V.; Zeng, T.; Stofer, R.; Liu, R.; Song, J.; McPhillips, S.; Soltis, M.; Cohen, A.E.

doi:10.1107/S2059798323010586

Acta Crystallographica Section D
Acta Crystallographica
Section D STRUCTURAL BIOLOGY

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Figure 13
Merging statistics for hydrogenase data collected at XPP. After indexing the images with dials.stills_process, they were grouped into two sets according to the probability of each image containing overlapping lattice diffraction (according to Resonet). Each set contained 198 images. (a) The CC_1/2 obtained after merging either set using cctbx.xfel.merge. CC_1/2 was computed five times per set with random half-data-set assignments. The markers represent the mean, and the shaded region indicates ±1 standard deviation from that mean. (b) The merged multiplicity in the asymmetric unit. Notably, Set B had a slightly higher overall multiplicity (7.83 versus 7.36), but a lower CC_1/2 at high resolution.

STRUCTURAL
BIOLOGY

ISSN: 2059-7983

Volume 80| Part 1| January 2024| Pages 26-43

https://doi.org/10.1107/S2059798323010586

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