Massive Compression for High Data Rate Macromolecular Crystallography (HDRMX): Impact on Diffraction Data and Subsequent Structural Analysis

Overview

Journal J Synchrotron Radiat

Date 2025 Feb 6

PMID 39913307

Authors

Herbert J Bernstein

Alexei S Soares

Kimberly Horvat

Jean Jakoncic

Affiliations

Soon will be listed here.

Abstract

New higher-count-rate, integrating, large-area X-ray detectors with framing rates as high as 17400 images per second are beginning to be available. These will soon be used for specialized macromolecular crystallography experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some information may be lost. Can we minimize this loss with acceptable impact on structural information? To explore this question, we have considered several approaches: summing short sequences of images, binning to create the effect of larger pixels, use of JPEG-2000 lossy wavelet-based compression, and use of Hcompress, which is a Haar-wavelet-based lossy compression borrowed from astronomy. We also explore the effect of the combination of summing, binning, and Hcompress or JPEG-2000. In each of these last two methods one can specify approximately how much one wants the result to be compressed from the starting file size. These provide particularly effective lossy compressions that retain essential information for structure solution from Bragg reflections.

References

Bernstein H, Andrews L, Diaz Jr J, Jakoncic J, Nguyen T, Sauter N . Best practices for high data-rate macromolecular crystallography (HDRMX). Struct Dyn. 2020; 7(1):014302. PMC: 6952294. DOI: 10.1063/1.5128498. View

Vagin A, Steiner R, Lebedev A, Potterton L, McNicholas S, Long F . REFMAC5 dictionary: organization of prior chemical knowledge and guidelines for its use. Acta Crystallogr D Biol Crystallogr. 2004; 60(Pt 12 Pt 1):2184-95. DOI: 10.1107/S0907444904023510. View

Kroon-Batenburg L, Helliwell J . Experiences with making diffraction image data available: what metadata do we need to archive?. Acta Crystallogr D Biol Crystallogr. 2014; 70(Pt 10):2502-9. PMC: 4187998. DOI: 10.1107/S1399004713029817. View

Prucha G, Henry S, Hollander K, Carter Z, Spasov K, Jorgensen W . Covalent and noncovalent strategies for targeting Lys102 in HIV-1 reverse transcriptase. Eur J Med Chem. 2023; 262:115894. PMC: 10872499. DOI: 10.1016/j.ejmech.2023.115894. View

Langer G, Cohen S, Lamzin V, Perrakis A . Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7. Nat Protoc. 2008; 3(7):1171-9. PMC: 2582149. DOI: 10.1038/nprot.2008.91. View

Adams P, Afonine P, Bunkoczi G, Chen V, Davis I, Echols N . PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr D Biol Crystallogr. 2010; 66(Pt 2):213-21. PMC: 2815670. DOI: 10.1107/S0907444909052925. View

Kabsch W . XDS. Acta Crystallogr D Biol Crystallogr. 2010; 66(Pt 2):125-32. PMC: 2815665. DOI: 10.1107/S0907444909047337. View

Evans P, Murshudov G . How good are my data and what is the resolution?. Acta Crystallogr D Biol Crystallogr. 2013; 69(Pt 7):1204-14. PMC: 3689523. DOI: 10.1107/S0907444913000061. View

Wang B . Resolution of phase ambiguity in macromolecular crystallography. Methods Enzymol. 1985; 115:90-112. DOI: 10.1016/0076-6879(85)15009-3. View

10.

Duong V, Ippolito J, Chan A, Lee W, Spasov K, Jorgensen W . Structural investigation of 2-naphthyl phenyl ether inhibitors bound to WT and Y181C reverse transcriptase highlights key features of the NNRTI binding site. Protein Sci. 2020; 29(9):1902-1910. PMC: 7454559. DOI: 10.1002/pro.3910. View

11.

Berman H, Henrick K, Nakamura H . Announcing the worldwide Protein Data Bank. Nat Struct Biol. 2003; 10(12):980. DOI: 10.1038/nsb1203-980. View

12.

Schneider D, Soares A, Lazo E, Kreitler D, Qian K, Fuchs M . AMX - the highly automated macromolecular crystallography (17-ID-1) beamline at the NSLS-II. J Synchrotron Radiat. 2022; 29(Pt 6):1480-1494. PMC: 9641562. DOI: 10.1107/S1600577522009377. View

13.

Ferrer J, Roth M, Antoniadis A . Data compression for diffraction patterns. Acta Crystallogr D Biol Crystallogr. 1998; 54(Pt 2):184-99. DOI: 10.1107/s0907444997007257. View

14.

Murshudov G, Vagin A, Dodson E . Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallogr D Biol Crystallogr. 1997; 53(Pt 3):240-55. DOI: 10.1107/S0907444996012255. View

15.

Hollander K, Chan A, Frey K, Hunker O, Ippolito J, Spasov K . Exploring novel HIV-1 reverse transcriptase inhibitors with drug-resistant mutants: A double mutant surprise. Protein Sci. 2023; 32(12):e4814. PMC: 10659932. DOI: 10.1002/pro.4814. View

16.

Bernstein H, Jakoncic J . Investigation of fast and efficient lossless compression algorithms for macromolecular crystallography experiments. J Synchrotron Radiat. 2024; 31(Pt 4):647-654. PMC: 11226158. DOI: 10.1107/S160057752400359X. View

17.

Galchenkova M, Tolstikova A, Klopprogge B, Sprenger J, Oberthuer D, Brehm W . Data reduction in protein serial crystallography. IUCrJ. 2024; 11(Pt 2):190-201. PMC: 10916297. DOI: 10.1107/S205225252400054X. View

18.

Bernstein F, Koetzle T, Williams G, Meyer Jr E, Brice M, Rodgers J . The Protein Data Bank: a computer-based archival file for macromolecular structures. J Mol Biol. 1977; 112(3):535-42. DOI: 10.1016/s0022-2836(77)80200-3. View

19.

Winter G, McAuley K . Automated data collection for macromolecular crystallography. Methods. 2011; 55(1):81-93. DOI: 10.1016/j.ymeth.2011.06.010. View

20.

Emsley P, Cowtan K . Coot: model-building tools for molecular graphics. Acta Crystallogr D Biol Crystallogr. 2004; 60(Pt 12 Pt 1):2126-32. DOI: 10.1107/S0907444904019158. View