research papers\(\def\hfill{\hskip 5em}\def\hfil{\hskip 3em}\def\eqno#1{\hfil {#1}}\)

IUCrJ
Volume 8| Part 6| November 2021| Pages 905-920
ISSN: 2052-2525

Pink-beam serial femtosecond crystallography for accurate structure-factor determination at an X-ray free-electron laser

crossmark logo

aPaul Scherrer Institut, Forschungstrasse 111, Villigen 5232, Switzerland, and bCenter for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron DESY, Notkestrasse 85, Hamburg 22607, Germany
*Correspondence e-mail: karol.nass@gmail.com, christopher.milne@xfel.eu

Edited by F. Maia, Uppsala University, Sweden (Received 1 June 2021; accepted 5 August 2021; online 23 September 2021)

Serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs) enables essentially radiation-damage-free macromolecular structure determination using microcrystals that are too small for synchrotron studies. However, SFX experiments often require large amounts of sample in order to collect highly redundant data where some of the many stochastic errors can be averaged out to determine accurate structure-factor amplitudes. In this work, the capability of the Swiss X-ray free-electron laser (SwissFEL) was used to generate large-bandwidth X-ray pulses [Δλ/λ = 2.2% full width at half-maximum (FWHM)], which were applied in SFX with the aim of improving the partiality of Bragg spots and thus decreasing sample consumption while maintaining the data quality. Sensitive data-quality indicators such as anomalous signal from native thaumatin micro-crystals and de novo phasing results were used to quantify the benefits of using pink X-ray pulses to obtain accurate structure-factor amplitudes. Compared with data measured using the same setup but using X-ray pulses with typical quasi-monochromatic XFEL bandwidth (Δλ/λ = 0.17% FWHM), up to fourfold reduction in the number of indexed diffraction patterns required to obtain similar data quality was achieved. This novel approach, pink-beam SFX, facilitates the yet underutilized de novo structure determination of challenging proteins at XFELs, thereby opening the door to more scientific breakthroughs.

1. Introduction

Serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs) offers unique opportunities for structural biologists (Chapman et al., 2011[Chapman, H. N., Fromme, P., Barty, A., White, T. A., Kirian, R. A., Aquila, A., Hunter, M. S., Schulz, J., DePonte, D. P., Weierstall, U., Doak, R. B., Maia, F. R., Martin, A. V., Schlichting, I., Lomb, L., Coppola, N., Shoeman, R. L., Epp, S. W., Hartmann, R., Rolles, D., Rudenko, A., Foucar, L., Kimmel, N., Weidenspointner, G., Holl, P., Liang, M., Barthelmess, M., Caleman, C., Boutet, S., Bogan, M. J., Krzywinski, J., Bostedt, C., Bajt, S., Gumprecht, L., Rudek, B., Erk, B., Schmidt, C., Hömke, A., Reich, C., Pietschner, D., Strüder, L., Hauser, G., Gorke, H., Ullrich, J., Herrmann, S., Schaller, G., Schopper, F., Soltau, H., Kühnel, K. U., Messerschmidt, M., Bozek, J. D., Hau-Riege, S. P., Frank, M., Hampton, C. Y., Sierra, R. G., Starodub, D., Williams, G. J., Hajdu, J., Timneanu, N., Seibert, M. M., Andreasson, J., Rocker, A., Jönsson, O., Svenda, M., Stern, S., Nass, K., Andritschke, R., Schröter, C. D., Krasniqi, F., Bott, M., Schmidt, K. E., Wang, X., Grotjohann, I., Holton, J. M., Barends, T. R., Neutze, R., Marchesini, S., Fromme, R., Schorb, S., Rupp, D., Adolph, M., Gorkhover, T., Andersson, I., Hirsemann, H., Potdevin, G., Graafsma, H., Nilsson, B. & Spence, J. C. (2011). Nature, 470, 73-77.]; Martin-Garcia et al., 2016[Martin-Garcia, J. M., Conrad, C. E., Coe, J., Roy-Chowdhury, S. & Fromme, P. (2016). Arch. Biochem. Biophys. 602, 32-47.]; Orville, 2020[Orville, A. M. (2020). Curr. Opin. Struct. Biol. 65, 193-208.]). Owing to ultra-short and extremely intense X-ray pulses with a peak brilliance which exceeds that of third-generation synchrotron sources by nine orders of magnitude, this method allows obtaining essentially damage-free X-ray diffraction data from systems prone to radiation damage such as micrometre-sized protein crystals or metalloproteins (Barty et al., 2012[Barty, A., Caleman, C., Aquila, A., Timneanu, N., Lomb, L., White, T. A., Andreasson, J., Arnlund, D., Bajt, S., Barends, T. R., Barthelmess, M., Bogan, M. J., Bostedt, C., Bozek, J. D., Coffee, R., Coppola, N., Davidsson, J., Deponte, D. P., Doak, R. B., Ekeberg, T., Elser, V., Epp, S. W., Erk, B., Fleckenstein, H., Foucar, L., Fromme, P., Graafsma, H., Gumprecht, L., Hajdu, J., Hampton, C. Y., Hartmann, R., Hartmann, A., Hauser, G., Hirsemann, H., Holl, P., Hunter, M. S., Johansson, L., Kassemeyer, S., Kimmel, N., Kirian, R. A., Liang, M., Maia, F. R., Malmerberg, E., Marchesini, S., Martin, A. V., Nass, K., Neutze, R., Reich, C., Rolles, D., Rudek, B., Rudenko, A., Scott, H., Schlichting, I., Schulz, J., Seibert, M. M., Shoeman, R. L., Sierra, R. G., Soltau, H., Spence, J. C., Stellato, F., Stern, S., Struder, L., Ullrich, J., Wang, X., Weidenspointner, G., Weierstall, U., Wunderer, C. B. & Chapman, H. N. (2012). Nat. Photonics, 6, 35-40.]; Lomb et al., 2011[Lomb, L., Barends, T. R., Kassemeyer, S., Aquila, A., Epp, S. W., Erk, B., Foucar, L., Hartmann, R., Rudek, B., Rolles, D., Rudenko, A., Shoeman, R. L., Andreasson, J., Bajt, S., Barthelmess, M., Barty, A., Bogan, M. J., Bostedt, C., Bozek, J. D., Caleman, C., Coffee, R., Coppola, N., DePonte, D. P., Doak, R. B., Ekeberg, T., Fleckenstein, H., Fromme, P., Gebhardt, M., Graafsma, H., Gumprecht, L., Hampton, C. Y., Hartmann, A., Hauser, G., Hirsemann, H., Holl, P., Holton, J. M., Hunter, M. S., Kabsch, W., Kimmel, N., Kirian, R. A., Liang, M., Maia, F. R., Meinhart, A., Marchesini, S., Martin, A. V., Nass, K., Reich, C., Schulz, J., Seibert, M. M., Sierra, R., Soltau, H., Spence, J. C., Steinbrener, J., Stellato, F., Stern, S., Timneanu, N., Wang, X., Weidenspointner, G., Weierstall, U., White, T. A., Wunderer, C., Chapman, H. N., Ullrich, J., Strüder, L. & Schlichting, I. (2011). Phys. Rev. B, 84, 214111.]; Nass, 2019[Nass, K. (2019). Acta Cryst. D75, 211-218.]). Examples include, but are not limited to, crystals grown in vivo (Nass, Redecke et al., 2020[Nass, K., Redecke, L., Perbandt, M., Yefanov, O., Klinge, M., Koopmann, R., Stellato, F., Gabdulkhakov, A., Schönherr, R., Rehders, D., Lahey-Rudolph, J. M., Aquila, A., Barty, A., Basu, S., Doak, R. B., Duden, R., Frank, M., Fromme, R., Kassemeyer, S., Katona, G., Kirian, R., Liu, H., Majoul, I., Martin-Garcia, J. M., Messerschmidt, M., Shoeman, R. L., Weierstall, U., Westenhoff, S., White, T. A., Williams, G. J., Yoon, C. H., Zatsepin, N., Fromme, P., Duszenko, M., Chapman, H. N. & Betzel, C. (2020). Nat. Commun. 11, 620.], 2013[Redecke, L., Nass, K., DePonte, D. P., White, T. A., Rehders, D., Barty, A., Stellato, F., Liang, M., Barends, T. R. M., Boutet, S., Williams, G. J., Messerschmidt, M., Seibert, M. M., Aquila, A., Arnlund, D., Bajt, S., Barth, T., Bogan, M. J., Caleman, C., Chao, T. C., Doak, R. B., Fleckenstein, H., Frank, M., Fromme, R., Galli, L., Grotjohann, I., Hunter, M. S., Johansson, L. C., Kassemeyer, S., Katona, G., Kirian, R. A., Koopmann, R., Kupitz, C., Lomb, L., Martin, A. V., Mogk, S., Neutze, R., Shoeman, R. L., Steinbrener, J., Timneanu, N., Wang, D., Weierstall, U., Zatsepin, N. A., Spence, J. C. H., Fromme, P., Schlichting, I., Duszenko, M., Betzel, C. & Chapman, H. N. (2013). Science, 339, 227-230.]; Sawaya et al., 2014[Sawaya, M. R., Cascio, D., Gingery, M., Rodriguez, J., Goldschmidt, L., Colletier, J. P., Messerschmidt, M. M., Boutet, S., Koglin, J. E., Williams, G. J., Brewster, A. S., Nass, K., Hattne, J., Botha, S., Doak, R. B., Shoeman, R. L., DePonte, D. P., Park, H. W., Federici, B. A., Sauter, N. K., Schlichting, I. & Eisenberg, D. S. (2014). Proc. Natl Acad. Sci. USA, 111, 12769-12774.]); membrane proteins crystallized in lipidic cubic phase (LCP), such as G-protein-coupled receptors (GPCRs) (Im et al., 2020[Im, D., Inoue, A., Fujiwara, T., Nakane, T., Yamanaka, Y., Uemura, T., Mori, C., Shiimura, Y., Kimura, K. T., Asada, H., Nomura, N., Tanaka, T., Yamashita, A., Nango, E., Tono, K., Kadji, F. M. N., Aoki, J., Iwata, S. & Shimamura, T. (2020). Nat. Commun. 11, 6442.]; Johansson et al., 2019[Johansson, L. C., Stauch, B., McCorvy, J. D., Han, G. W., Patel, N., Huang, X. P., Batyuk, A., Gati, C., Slocum, S. T., Li, C., Grandner, J. M., Hao, S., Olsen, R. H. J., Tribo, A. R., Zaare, S., Zhu, L., Zatsepin, N. A., Weierstall, U., Yous, S., Stevens, R. C., Liu, W., Roth, B. L., Katritch, V. & Cherezov, V. (2019). Nature, 569, 289-292.]; Kang et al., 2015[Kang, Y., Zhou, X. E., Gao, X., He, Y., Liu, W., Ishchenko, A., Barty, A., White, T. A., Yefanov, O., Han, G. W., Xu, Q., de Waal, P. W., Ke, J., Tan, M. H., Zhang, C., Moeller, A., West, G. M., Pascal, B. D., Van Eps, N., Caro, L. N., Vishnivetskiy, S. A., Lee, R. J., Suino-Powell, K. M., Gu, X., Pal, K., Ma, J., Zhi, X., Boutet, S., Williams, G. J., Messerschmidt, M., Gati, C., Zatsepin, N. A., Wang, D., James, D., Basu, S., Roy-Chowdhury, S., Conrad, C. E., Coe, J., Liu, H., Lisova, S., Kupitz, C., Grotjohann, I., Fromme, R., Jiang, Y., Tan, M., Yang, H., Li, J., Wang, M., Zheng, Z., Li, D., Howe, N., Zhao, Y., Standfuss, J., Diederichs, K., Dong, Y., Potter, C. S., Carragher, B., Caffrey, M., Jiang, H., Chapman, H. N., Spence, J. C., Fromme, P., Weierstall, U., Ernst, O. P., Katritch, V., Gurevich, V. V., Griffin, P. R., Hubbell, W. L., Stevens, R. C., Cherezov, V., Melcher, K. & Xu, H. E. (2015). Nature, 523, 561-567.]; Lee et al., 2020[Lee, M.-Y., Geiger, J., Ishchenko, A., Han, G. W., Barty, A., White, T. A., Gati, C., Batyuk, A., Hunter, M. S., Aquila, A., Boutet, S., Weierstall, U., Cherezov, V. & Liu, W. (2020). IUCrJ, 7, 976-984.]; Liu et al., 2013[Liu, W., Wacker, D., Gati, C., Han, G. W., James, D., Wang, D., Nelson, G., Weierstall, U., Katritch, V., Barty, A., Zatsepin, N. A., Li, D., Messerschmidt, M., Boutet, S., Williams, G. J., Koglin, J. E., Seibert, M. M., Wang, C., Shah, S. T., Basu, S., Fromme, R., Kupitz, C., Rendek, K. N., Grotjohann, I., Fromme, P., Kirian, R. A., Beyerlein, K. R., White, T. A., Chapman, H. N., Caffrey, M., Spence, J. C., Stevens, R. C. & Cherezov, V. (2013). Science, 342, 1521-1524.]; Stauch et al., 2019[Stauch, B., Johansson, L. C., McCorvy, J. D., Patel, N., Han, G. W., Huang, X. P., Gati, C., Batyuk, A., Slocum, S. T., Ishchenko, A., Brehm, W., White, T. A., Michaelian, N., Madsen, C., Zhu, L., Grant, T. D., Grandner, J. M., Shiriaeva, A., Olsen, R. H. J., Tribo, A. R., Yous, S., Stevens, R. C., Weierstall, U., Katritch, V., Roth, B. L., Liu, W. & Cherezov, V. (2019). Nature, 569, 284-288.]; Zhang et al., 2015[Zhang, H., Unal, H., Gati, C., Han, G. W., Liu, W., Zatsepin, N. A., James, D., Wang, D., Nelson, G., Weierstall, U., Sawaya, M. R., Xu, Q., Messerschmidt, M., Williams, G. J., Boutet, S., Yefanov, O. M., White, T. A., Wang, C., Ishchenko, A., Tirupula, K. C., Desnoyer, R., Coe, J., Conrad, C. E., Fromme, P., Stevens, R. C., Katritch, V., Karnik, S. S. & Cherezov, V. (2015). Cell, 161, 833-844.]); and radiation-sensitive metalloproteins, such as photosystem II (Suga et al., 2015[Suga, M., Akita, F., Hirata, K., Ueno, G., Murakami, H., Nakajima, Y., Shimizu, T., Yamashita, K., Yamamoto, M., Ago, H. & Shen, J. R. (2015). Nature, 517, 99-103.]), cytochrome c oxidase (Hirata et al., 2014[Hirata, K., Shinzawa-Itoh, K., Yano, N., Takemura, S., Kato, K., Hatanaka, M., Muramoto, K., Kawahara, T., Tsukihara, T., Yamashita, E., Tono, K., Ueno, G., Hikima, T., Murakami, H., Inubushi, Y., Yabashi, M., Ishikawa, T., Yamamoto, M., Ogura, T., Sugimoto, H., Shen, J. R., Yoshikawa, S. & Ago, H. (2014). Nat. Methods, 11, 734-736.]) and copper nitrite reductase (Halsted et al., 2019[Halsted, T. P., Yamashita, K., Gopalasingam, C. C., Shenoy, R. T., Hirata, K., Ago, H., Ueno, G., Blakeley, M. P., Eady, R. R., Antonyuk, S. V., Yamamoto, M. & Hasnain, S. S. (2019). IUCrJ, 6, 761-772.]; Rose et al., 2021[Rose, S. L., Antonyuk, S. V., Sasaki, D., Yamashita, K., Hirata, K., Ueno, G., Ago, H., Eady, R. R., Tosha, T., Yamamoto, M. & Hasnain, S. S. (2021). Sci. Adv. 7, eabd8523.]). Additionally, it opened the possibility to observe proteins in action via time-resolved crystallography on ultra-fast time scales (Barends et al., 2015[Barends, T. R., Foucar, L., Ardevol, A., Nass, K., Aquila, A., Botha, S., Doak, R. B., Falahati, K., Hartmann, E., Hilpert, M., Heinz, M., Hoffmann, M. C., Kofinger, J., Koglin, J. E., Kovacsova, G., Liang, M., Milathianaki, D., Lemke, H. T., Reinstein, J., Roome, C. M., Shoeman, R. L., Williams, G. J., Burghardt, I., Hummer, G., Boutet, S. & Schlichting, I. (2015). Science, 350, 445-450.]; Coquelle et al., 2018[Coquelle, N., Sliwa, M., Woodhouse, J., Schirò, G., Adam, V., Aquila, A., Barends, T. R. M., Boutet, S., Byrdin, M., Carbajo, S., De la Mora, E., Doak, R. B., Feliks, M., Fieschi, F., Foucar, L., Guillon, V., Hilpert, M., Hunter, M. S., Jakobs, S., Koglin, J. E., Kovacsova, G., Lane, T. J., Lévy, B., Liang, M., Nass, K., Ridard, J., Robinson, J. S., Roome, C. M., Ruckebusch, C., Seaberg, M., Thepaut, M., Cammarata, M., Demachy, I., Field, M., Shoeman, R. L., Bourgeois, D., Colletier, J. P., Schlichting, I. & Weik, M. (2018). Nat. Chem. 10, 31-37.]; Dods et al., 2021[Dods, R., Båth, P., Morozov, D., Gagnér, V. A., Arnlund, D., Luk, H. L., Kübel, J., Maj, M., Vallejos, A., Wickstrand, C., Bosman, R., Beyerlein, K. R., Nelson, G., Liang, M., Milathianaki, D., Robinson, J., Harimoorthy, R., Berntsen, P., Malmerberg, E., Johansson, L., Andersson, R., Carbajo, S., Claesson, E., Conrad, C. E., Dahl, P., Hammarin, G., Hunter, M. S., Li, C., Lisova, S., Royant, A., Safari, C., Sharma, A., Williams, G. J., Yefanov, O., Westenhoff, S., Davidsson, J., DePonte, D. P., Boutet, S., Barty, A., Katona, G., Groenhof, G., Brändén, G. & Neutze, R. (2021). Nature, 589, 310-314.]; Nango et al., 2016[Nango, E., Royant, A., Kubo, M., Nakane, T., Wickstrand, C., Kimura, T., Tanaka, T., Tono, K., Song, C., Tanaka, R., Arima, T., Yamashita, A., Kobayashi, J., Hosaka, T., Mizohata, E., Nogly, P., Sugahara, M., Nam, D., Nomura, T., Shimamura, T., Im, D., Fujiwara, T., Yamanaka, Y., Jeon, B., Nishizawa, T., Oda, K., Fukuda, M., Andersson, R., Båth, P., Dods, R., Davidsson, J., Matsuoka, S., Kawatake, S., Murata, M., Nureki, O., Owada, S., Kameshima, T., Hatsui, T., Joti, Y., Schertler, G., Yabashi, M., Bondar, A. N., Standfuss, J., Neutze, R. & Iwata, S. (2016). Science, 354, 1552-1557.]; Nass, Gorel et al., 2020[Nass, K., Gorel, A., Abdullah, M. M. V., Martin, A., Kloos, M., Marinelli, A., Aquila, A., Barends, T. R. M., Decker, F. J., Bruce Doak, R., Foucar, L., Hartmann, E., Hilpert, M., Hunter, M. S., Jurek, Z., Koglin, J. E., Kozlov, A., Lutman, A. A., Kovacs, G. N., Roome, C. M., Shoeman, R. L., Santra, R., Quiney, H. M., Ziaja, B., Boutet, S. & Schlichting, I. (2020). Nat. Commun. 11, 1814.]; Nass Kovacs et al., 2019[Nass Kovacs, G., Colletier, J. P., Grünbein, M. L., Yang, Y., Stensitzki, T., Batyuk, A., Carbajo, S., Doak, R. B., Ehrenberg, D., Foucar, L., Gasper, R., Gorel, A., Hilpert, M., Kloos, M., Koglin, J. E., Reinstein, J., Roome, C. M., Schlesinger, R., Seaberg, M., Shoeman, R. L., Stricker, M., Boutet, S., Haacke, S., Heberle, J., Heyne, K., Domratcheva, T., Barends, T. R. M. & Schlichting, I. (2019). Nat. Commun. 10, 3177.]; Pande et al., 2016[Pande, K., Hutchison, C. D., Groenhof, G., Aquila, A., Robinson, J. S., Tenboer, J., Basu, S., Boutet, S., DePonte, D. P., Liang, M., White, T. A., Zatsepin, N. A., Yefanov, O., Morozov, D., Oberthuer, D., Gati, C., Subramanian, G., James, D., Zhao, Y., Koralek, J., Brayshaw, J., Kupitz, C., Conrad, C., Roy-Chowdhury, S., Coe, J. D., Metz, M., Xavier, P. L., Grant, T. D., Koglin, J. E., Ketawala, G., Fromme, R., rajer, V., Henning, R., Spence, J. C., Ourmazd, A., Schwander, P., Weierstall, U., Frank, M., Fromme, P., Barty, A., Chapman, H. N., Moffat, K., van Thor, J. J. & Schmidt, M. (2016). Science, 352, 725-729.]; Skopintsev et al., 2020[Skopintsev, P., Ehrenberg, D., Weinert, T., James, D., Kar, R. K., Johnson, P. J. M., Ozerov, D., Furrer, A., Martiel, I., Dworkowski, F., Nass, K., Knopp, G., Cirelli, C., Arrell, C., Gashi, D., Mous, S., Wranik, M., Gruhl, T., Kekilli, D., Brünle, S., Deupi, X., Schertler, G. F. X., Benoit, R. M., Panneels, V., Nogly, P., Schapiro, I., Milne, C., Heberle, J. & Standfuss, J. (2020). Nature, 583, 314-318.]; Sorigué et al., 2021[Sorigué, D., Hadjidemetriou, K., Blangy, S., Gotthard, G., Bonvalet, A., Coquelle, N., Samire, P., Aleksandrov, A., Antonucci, L., Benachir, A., Boutet, S., Byrdin, M., Cammarata, M., Carbajo, S., Cuine, S., Doak, R. B., Foucar, L., Gorel, A., Grünbein, M., Hartmann, E., Hienerwadel, R., Hilpert, M., Kloos, M., Lane, T. J., Legeret, B., Legrand, P., Li-Beisson, Y., Moulin, S. L. Y., Nurizzo, D., Peltier, G., Schiro, G., Shoeman, R. L., Sliwa, M., Solinas, X., Zhuang, B., Barends, T. R. M., Colletier, J. P., Joffre, M., Royant, A., Berthomieu, C., Weik, M., Domratcheva, T., Brettel, K., Vos, M. H., Schlichting, I., Arnoux, P., Muller, P. & Beisson, F. (2021). Science, 372, eabd5687.]; Woodhouse et al., 2020[Woodhouse, J., Nass Kovacs, G., Coquelle, N., Uriarte, L. M., Adam, V., Barends, T. R. M., Byrdin, M., de la Mora, E., Bruce Doak, R., Feliks, M., Field, M., Fieschi, F., Guillon, V., Jakobs, S., Joti, Y., Macheboeuf, P., Motomura, K., Nass, K., Owada, S., Roome, C. M., Ruckebusch, C., Schirò, G., Shoeman, R. L., Thepaut, M., Togashi, T., Tono, K., Yabashi, M., Cammarata, M., Foucar, L., Bourgeois, D., Sliwa, M., Colletier, J. P., Schlichting, I. & Weik, M. (2020). Nat. Commun. 11, 741.]; Yun et al., 2021[Yun, J. H., Li, X., Yue, J., Park, J. H., Jin, Z., Li, C., Hu, H., Shi, Y., Pandey, S., Carbajo, S., Boutet, S., Hunter, M. S., Liang, M., Sierra, R. G., Lane, T. J., Zhou, L., Weierstall, U., Zatsepin, N. A., Ohki, M., Tame, J. R. H., Park, S. Y., Spence, J. C. H., Zhang, W., Schmidt, M., Lee, W. & Liu, H. (2021). Proc. Natl Acad. Sci. USA, 118, e2020486118.]; Nogly et al., 2018[Nogly, P., Weinert, T., James, D., Carbajo, S., Ozerov, D., Furrer, A., Gashi, D., Borin, V., Skopintsev, P., Jaeger, K., Nass, K., Bath, P., Bosman, R., Koglin, J., Seaberg, M., Lane, T., Kekilli, D., Brunle, S., Tanaka, T., Wu, W., Milne, C., White, T., Barty, A., Weierstall, U., Panneels, V., Nango, E., Iwata, S., Hunter, M., Schapiro, I., Schertler, G., Neutze, R. & Standfuss, J. (2018). Science, 361, eaat0094.]).

Despite over a decade of continuous improvements in beamline instrumentation, X-ray detection performance, data-processing software, sample delivery and structure-determination methods for SFX, elucidation of unknown protein structures at XFEL sources remains severely underutilized. Several SFX studies have demonstrated that de novo structure determination from SFX data is possible using well characterized and strongly diffracting model systems via single-wavelength anomalous diffraction (SAD) (Barends et al., 2014[Barends, T. R., Foucar, L., Botha, S., Doak, R. B., Shoeman, R. L., Nass, K., Koglin, J. E., Williams, G. J., Boutet, S., Messerschmidt, M. & Schlichting, I. (2014). Nature, 505, 244-247.]; Hunter et al., 2016[Hunter, M. S., Yoon, C. H., DeMirci, H., Sierra, R. G., Dao, E. H., Ahmadi, R., Aksit, F., Aquila, A. L., Ciftci, H., Guillet, S., Hayes, M. J., Lane, T. J., Liang, M., Lundström, U., Koglin, J. E., Mgbam, P., Rao, Y., Zhang, L., Wakatsuki, S., Holton, J. M. & Boutet, S. (2016). Nat. Commun. 7, 13388.]; Nakane et al., 2016[Nakane, T., Hanashima, S., Suzuki, M., Saiki, H., Hayashi, T., Kakinouchi, K., Sugiyama, S., Kawatake, S., Matsuoka, S., Matsumori, N., Nango, E., Kobayashi, J., Shimamura, T., Kimura, K., Mori, C., Kunishima, N., Sugahara, M., Takakyu, Y., Inoue, S., Masuda, T., Hosaka, T., Tono, K., Joti, Y., Kameshima, T., Hatsui, T., Yabashi, M., Inoue, T., Nureki, O., Iwata, S., Murata, M. & Mizohata, E. (2016). Proc. Natl Acad. Sci. USA, 113, 13039-13044.]; Sugahara et al., 2017[Sugahara, M., Nakane, T., Masuda, T., Suzuki, M., Inoue, S., Song, C., Tanaka, R., Nakatsu, T., Mizohata, E., Yumoto, F., Tono, K., Joti, Y., Kameshima, T., Hatsui, T., Yabashi, M., Nureki, O., Numata, K., Nango, E. & Iwata, S. (2017). Sci. Rep. 7, 703.]; Yamashita et al., 2017[Yamashita, K., Kuwabara, N., Nakane, T., Murai, T., Mizohata, E., Sugahara, M., Pan, D., Masuda, T., Suzuki, M., Sato, T., Kodan, A., Yamaguchi, T., Nango, E., Tanaka, T., Tono, K., Joti, Y., Kameshima, T., Hatsui, T., Yabashi, M., Manya, H., Endo, T., Kato, R., Senda, T., Kato, H., Iwata, S., Ago, H., Yamamoto, M., Yumoto, F. & Nakatsu, T. (2017). IUCrJ, 4, 639-647.]), native SAD (Batyuk et al., 2016[Batyuk, A., Galli, L., Ishchenko, A., Han, G. W., Gati, C., Popov, P. A., Lee, M. Y., Stauch, B., White, T. A., Barty, A., Aquila, A., Hunter, M. S., Liang, M., Boutet, S., Pu, M., Liu, Z. J., Nelson, G., James, D., Li, C., Zhao, Y., Spence, J. C., Liu, W., Fromme, P., Katritch, V., Weierstall, U., Stevens, R. C. & Cherezov, V. (2016). Sci. Adv. 2, e1600292.]; Nakane et al., 2015[Nakane, T., Song, C., Suzuki, M., Nango, E., Kobayashi, J., Masuda, T., Inoue, S., Mizohata, E., Nakatsu, T., Tanaka, T., Tanaka, R., Shimamura, T., Tono, K., Joti, Y., Kameshima, T., Hatsui, T., Yabashi, M., Nureki, O., Iwata, S. & Sugahara, M. (2015). Acta Cryst. D71, 2519-2525.]; Nass et al., 2016[Nass, K., Meinhart, A., Barends, T. R. M., Foucar, L., Gorel, A., Aquila, A., Botha, S., Doak, R. B., Koglin, J., Liang, M., Shoeman, R. L., Williams, G., Boutet, S. & Schlichting, I. (2016). IUCrJ, 3, 180-191.]), isomorphous replacement (Yamashita et al., 2015[Yamashita, K., Pan, D., Okuda, T., Sugahara, M., Kodan, A., Yamaguchi, T., Murai, T., Gomi, K., Kajiyama, N., Mizohata, E., Suzuki, M., Nango, E., Tono, K., Joti, Y., Kameshima, T., Park, J., Song, C., Hatsui, T., Yabashi, M., Iwata, S., Kato, H., Ago, H., Yamamoto, M. & Nakatsu, T. (2015). Sci. Rep. 5, 14017.]), and novel phasing methods such as simultaneous multi-wavelength anomalous diffraction (MAD) with two-color X-ray pulses (Gorel et al., 2017[Gorel, A., Motomura, K., Fukuzawa, H., Doak, R. B., Grünbein, M. L., Hilpert, M., Inoue, I., Kloos, M., Kovácsová, G., Nango, E., Nass, K., Roome, C. M., Shoeman, R. L., Tanaka, R., Tono, K., Joti, Y., Yabashi, M., Iwata, S., Foucar, L., Ueda, K., Barends, T. R. M. & Schlichting, I. (2017). Nat. Commun. 8, 1170.]) and high-intensity MAD (Son et al., 2011[Son, S. K., Chapman, H. N. & Santra, R. (2011). Phys. Rev. Lett. 107, 218102.]), but to date only one previously unknown structure of BinAB was determined at an XFEL (Colletier et al., 2016[Colletier, J. P., Sawaya, M. R., Gingery, M., Rodriguez, J. A., Cascio, D., Brewster, A. S., Michels-Clark, T., Hice, R. H., Coquelle, N., Boutet, S., Williams, G. J., Messerschmidt, M., DePonte, D. P., Sierra, R. G., Laksmono, H., Koglin, J. E., Hunter, M. S., Park, H. W., Uervirojnangkoorn, M., Bideshi, D. K., Brunger, A. T., Federici, B. A., Sauter, N. K. & Eisenberg, D. S. (2016). Nature, 539, 43-47.]).

One of the most significant reasons for this underutilization of SFX for de novo structure determination, in addition to the scarcity of available beam time, are various sources of experimental errors that make it challenging to obtain accurate measurements of small signals required for phasing. They arise from stochastic fluctuations of X-ray pulse properties, background scattering from media used to deliver hydrated crystals to the interaction region with X-ray pulses and difficulties in analyzing still diffraction patterns from crystals in random orientations with unknown partiality of individual reflections. As an example of efforts to overcome these limitations, partiality correction was introduced into SFX data-processing software (Ginn et al., 2015[Ginn, H. M., Brewster, A. S., Hattne, J., Evans, G., Wagner, A., Grimes, J. M., Sauter, N. K., Sutton, G. & Stuart, D. I. (2015). Acta Cryst. D71, 1400-1410.], 2016[Ginn, H. M., Evans, G., Sauter, N. K. & Stuart, D. I. (2016). J. Appl. Cryst. 49, 1065-1072.]; Kabsch, 2014[Kabsch, W. (2014). Acta Cryst. D70, 2204-2216.]; Sauter, 2015[Sauter, N. K. (2015). J. Synchrotron Rad. 22, 239-248.]; White, 2014[White, T. A. (2014). Phil. Trans. R. Soc. B, 369, 20130330.]; White et al., 2016[White, T. A., Mariani, V., Brehm, W., Yefanov, O., Barty, A., Beyerlein, K. R., Chervinskii, F., Galli, L., Gati, C., Nakane, T., Tolstikova, A., Yamashita, K., Yoon, C. H., Diederichs, K. & Chapman, H. N. (2016). J. Appl. Cryst. 49, 680-689.]); however, in practice, this procedure often fails when other experimental errors are large. Currently, the most popular approach in SFX to decrease the experimental errors and obtain full reflection intensities from still diffraction patterns is Monte Carlo averaging (Kirian et al., 2011[Kirian, R. A., White, T. A., Holton, J. M., Chapman, H. N., Fromme, P., Barty, A., Lomb, L., Aquila, A., Maia, F. R. N. C., Martin, A. V., Fromme, R., Wang, X., Hunter, M. S., Schmidt, K. E. & Spence, J. C. H. (2011). Acta Cryst. A67, 131-140.]) but it requires a large number of measurements, especially for de novo structure determination. This translates into larger sample quantities and longer measurement times required.

Recently, an improvement in native-SAD phasing of SFX data was demonstrated by utilizing longer wavelengths that increased the strength of the anomalous signal (Nass, Cheng et al., 2020[Nass, K., Cheng, R., Vera, L., Mozzanica, A., Redford, S., Ozerov, D., Basu, S., James, D., Knopp, G., Cirelli, C., Martiel, I., Casadei, C., Weinert, T., Nogly, P., Skopintsev, P., Usov, I., Leonarski, F., Geng, T., Rappas, M., Doré, A. S., Cooke, R., Nasrollahi Shirazi, S., Dworkowski, F., Sharpe, M., Olieric, N., Bacellar, C., Bohinc, R., Steinmetz, M. O., Schertler, G., Abela, R., Patthey, L., Schmitt, B., Hennig, M., Standfuss, J., Wang, M. & Milne, C. J. (2020). IUCrJ, 7, 965-975.]). This reduced up to tenfold the number of indexed images needed for successful de novo structure determination. Another approach to reduce the number of indexed images, applicable not only to de novo phasing but also to molecular replacement strategies, is to use polychromatic (pink) X-ray pulses for SFX. Theoretically, faster convergence rates of the Monte Carlo approach can be achieved by increasing the bandwidth (BW) or divergence of the X-ray pulses (Dejoie et al., 2013[Dejoie, C., McCusker, L. B., Baerlocher, C., Abela, R., Patterson, B. D., Kunz, M. & Tamura, N. (2013). J. Appl. Cryst. 46, 791-794.]; White et al., 2013[White, T. A., Barty, A., Stellato, F., Holton, J. M., Kirian, R. A., Zatsepin, N. A. & Chapman, H. N. (2013). Acta Cryst. D69, 1231-1240.]). This is due to improvement of partiality of individual Bragg spot intensities and increase of the number of Bragg spots per pattern owing to a larger coverage of the reciprocal lattice points located between the limiting Ewald spheres. Hence, the large-BW XFEL pulses might allow obtaining a complete set of full structure-factor amplitudes with fewer number of indexed diffraction patterns than typical self-amplified spontaneous emission (SASE) BW XFEL pulses.

Initial pink-beam serial crystallography experiments at synchrotrons were performed using a spectral BW of 2.5% (Tolstikova et al., 2019[Tolstikova, A., Levantino, M., Yefanov, O., Hennicke, V., Fischer, P., Meyer, J., Mozzanica, A., Redford, S., Crosas, E., Opara, N. L., Barthelmess, M., Lieske, J., Oberthuer, D., Wator, E., Mohacsi, I., Wulff, M., Schmitt, B., Chapman, H. N. & Meents, A. (2019). IUCrJ, 6, 927-937.]) and a full harmonic of the undulator spectrum with a BW of 5.7% [full width at half-maximum (FWHM)] (Martin-Garcia et al., 2019[Martin-Garcia, J. M., Zhu, L., Mendez, D., Lee, M.-Y., Chun, E., Li, C., Hu, H., Subramanian, G., Kissick, D., Ogata, C., Henning, R., Ishchenko, A., Dobson, Z., Zhang, S., Weierstall, U., Spence, J. C. H., Fromme, P., Zatsepin, N. A., Fischetti, R. F., Cherezov, V. & Liu, W. (2019). IUCrJ, 6, 412-425.]; Meents et al., 2017[Meents, A., Wiedorn, M. O., Srajer, V., Henning, R., Sarrou, I., Bergtholdt, J., Barthelmess, M., Reinke, P. Y. A., Dierksmeyer, D., Tolstikova, A., Schaible, S., Messerschmidt, M., Ogata, C. M., Kissick, D. J., Taft, M. H., Manstein, D. J., Lieske, J., Oberthuer, D., Fischetti, R. F. & Chapman, H. N. (2017). Nat. Commun. 8, 1281.]), and show a reduction in the number of images required for a set of usable structure factors. However, data collected at synchrotrons at room temperature from micro-crystals, especially metalloproteins, are particularly affected by radiation damage. With the upcoming upgrades of synchrotron sources, brighter and smaller X-ray beams resulting in approximately two orders of magnitude larger average photon flux densities are expected to be used for (pink beam) serial crystallography experiments. As an example, serial synchrotron crystallography experiments have shown that a decay of the diffracting power by half is observed at D1/2 = 150 kGy (Roedig et al., 2016[Roedig, P., Duman, R., Sanchez-Weatherby, J., Vartiainen, I., Burkhardt, A., Warmer, M., David, C., Wagner, A. & Meents, A. (2016). J. Appl. Cryst. 49, 968-975.]), and an elongation of the iron–water bond in DtpAa, indicative of FeIII to FeII reduction, is observed at a dose of 33 kGy but not in the SFX structure (Ebrahim et al., 2019[Ebrahim, A., Moreno-Chicano, T., Appleby, M. V., Chaplin, A. K., Beale, J. H., Sherrell, D. A., Duyvesteyn, H. M. E., Owada, S., Tono, K., Sugimoto, H., Strange, R. W., Worrall, J. A. R., Axford, D., Owen, R. L. & Hough, M. A. (2019). IUCrJ, 6, 543-551.]).

Here we demonstrate that large-BW X-ray pulses with Δλ/λ = 2.2% (FWHM) from an XFEL significantly improve SFX data quality when compared with nominal SASE BW with Δλ/λ = 0.17% (FWHM). We used microcrystals of a well characterized system (thaumatin) and X-ray pulses with a photon energy of 6 keV (2.066 Å) to perform a detailed comparison of data quality between data sets measured with large and nominal SASE BW X-ray pulses from SwissFEL (Prat, Abela et al., 2020[Prat, E., Abela, R., Aiba, M., Alarcon, A., Alex, J., Arbelo, Y., Arrell, C., Arsov, V., Bacellar, C., Beard, C., Beaud, P., Bettoni, S., Biffiger, R., Bopp, M., Braun, H.-H., Calvi, M., Cassar, A., Celcer, T., Chergui, M., Chevtsov, P., Cirelli, C., Citterio, A., Craievich, P., Divall, M. C., Dax, A., Dehler, M., Deng, Y., Dietrich, A., Dijkstal, P., Dinapoli, R., Dordevic, S., Ebner, S., Engeler, D., Erny, C., Esposito, V., Ferrari, E., Flechsig, U., Follath, R., Frei, F., Ganter, R., Garvey, T., Geng, Z., Gobbo, A., Gough, C., Hauff, A., Hauri, C. P., Hiller, N., Hunziker, S., Huppert, M., Ingold, G., Ischebeck, R., Janousch, M., Johnson, P. J. M., Johnson, S. L., Juranić, P., Jurcevic, M., Kaiser, M., Kalt, R., Keil, B., Kiselev, D., Kittel, C., Knopp, G., Koprek, W., Laznovsky, M., Lemke, H. T., Sancho, D. L., Löhl, F., Malyzhenkov, A., Mancini, G. F., Mankowsky, R., Marcellini, F., Marinkovic, G., Martiel, I., Märki, F., Milne, C. J., Mozzanica, A., Nass, K., Orlandi, G. L., Loch, C. O., Paraliev, M., Patterson, B., Patthey, L., Pedrini, B., Pedrozzi, M., Pradervand, C., Radi, P., Raguin, J.-Y., Redford, S., Rehanek, J., Reiche, S., Rivkin, L., Romann, A., Sala, L., Sander, M., Schietinger, T., Schilcher, T., Schlott, V., Schmidt, T., Seidel, M., Stadler, M., Stingelin, L., Svetina, C., Treyer, D. M., Trisorio, A., Vicario, C., Voulot, D., Wrulich, A., Zerdane, S. & Zimoch, E. (2020). Nat. Photonics, 14, 748-754.]). We present up to fourfold reduction of the number of images required for obtaining similar data quality with large-BW XFEL pulses as compared with SASE using the same experimental setup and similar pulse properties. As a sensitive data-quality indicator, we used the weak anomalous signal from sulfur atoms and results from an automatic de novo structure-determination pipeline. The improvement offered by increased spectral BW of XFEL pulses significantly reduces the data-collection time and sample quantity needed for obtaining a high-quality SFX data set, not only for de novo phasing applications but also for molecular replacement strategies used, for example, in time-resolved SFX.

2. Methods

2.1. Preparation of thaumatin microcrystals and embedding in LCP

Preparation and embedding of thaumatin microcrystals in LCP were carried out as described (Nass, Cheng et al., 2020[Nass, K., Cheng, R., Vera, L., Mozzanica, A., Redford, S., Ozerov, D., Basu, S., James, D., Knopp, G., Cirelli, C., Martiel, I., Casadei, C., Weinert, T., Nogly, P., Skopintsev, P., Usov, I., Leonarski, F., Geng, T., Rappas, M., Doré, A. S., Cooke, R., Nasrollahi Shirazi, S., Dworkowski, F., Sharpe, M., Olieric, N., Bacellar, C., Bohinc, R., Steinmetz, M. O., Schertler, G., Abela, R., Patthey, L., Schmitt, B., Hennig, M., Standfuss, J., Wang, M. & Milne, C. J. (2020). IUCrJ, 7, 965-975.]). The average size of the microcrystals was ∼20 µm in length and ∼10 µm in width.

2.2. Diffraction data collection at SwissFEL

SFX data from thaumatin microcrystals were collected in May 2019 during a regular user beam time at SwissFEL (proposal No. 20182149, `Large bandwidth X-ray FEL pulses for reducing partiality in SFX'). Thaumatin microcrystals embedded in LCP were delivered to the interaction region with X-ray pulses using an LCP injector (Weierstall et al., 2014[Weierstall, U., James, D., Wang, C., White, T. A., Wang, D., Liu, W., Spence, J. C., Bruce Doak, R., Nelson, G., Fromme, P., Fromme, R., Grotjohann, I., Kupitz, C., Zatsepin, N. A., Liu, H., Basu, S., Wacker, D., Won Han, G., Katritch, V., Boutet, S., Messerschmidt, M., Williams, G. J., Koglin, J. E., Marvin Seibert, M., Klinker, M., Gati, C., Shoeman, R. L., Barty, A., Chapman, H. N., Kirian, R. A., Beyerlein, K. R., Stevens, R. C., Li, D., Shah, S. T., Howe, N., Caffrey, M. & Cherezov, V. (2014). Nat. Commun. 5, 3309.]). The injector was mounted vertically in the Prime chamber of the Alvra experimental hutch with a distance between the tip of the nozzle and the detector surface of ∼95 mm. The diameter of the capillary used to extrude the sample was 50 µm. Pressure to drive the injector was supplied by a remotely controlled high-performance liquid chromatography (HPLC) system (Shimadzu). The flow rate set on the HPLC pump ranged from 0.002 to 0.005 ml min−1 resulting in a sample consumption rate of 0.15 to 0.37 µl min−1. An estimated distance of 50 and 125 µm between the exposed areas from two consecutive X-ray exposures ensured sufficient separation to avoid radiation damage from the previous shot. To reduce the background scattering from the residual air in the chamber, the pressure was decreased to 100 mbar, filled up to 800 mbar with He gas and then reduced again to 100 mbar prior to measurements. The temperature in the hutch was kept constant at 24°C. The X-ray pulses were attenuated to achieve both stable flow of the LCP jet and to avoid saturated Bragg spot intensities on the detector. The attenuation was adjusted when switching between the large-BW and SASE modes to keep the fluence on the sample constant. Single-shot diffraction patterns were recorded at the 25 Hz repetition rate of the X-ray laser using the low-noise automatic gain switching JUNGFRAU 16M detector (Mozzanica et al., 2016[Mozzanica, A., Bergamaschi, A., Brueckner, M., Cartier, S., Dinapoli, R., Greiffenberg, D., Jungmann-Smith, J., Maliakal, D., Mezza, D., Ramilli, M., Ruder, C., Schaedler, L., Schmitt, B., Shi, X. & Tinti, G. (2016). J. Instrum. 11, C02047-C02047.]) and saved on disk as HDF5 files in raw format as read out directly from the detector without any modifications in runs of 10 000 consecutive shots. A Kapton foil of 75 µm thickness was installed ∼20 mm in front of the detector to separate the detector compartment filled with N2 gas from He gas in the main part of the chamber, and a regulation system maintained the pressure in the two compartments at equilibrium.

2.3. Real-time and automatic SFX data-analysis and visualization pipeline

An automatic SFX data-analysis and visualization pipeline developed at SwissFEL was used during the experiment. The pipeline provided information about the amount and quality of collected data during the experiments in real time, which significantly reduced the workload of the data-processing team. The pipeline applied pedestal and gain corrections as well as bad pixel masking to each diffraction image `on the fly' in parallel to raw data saving, and performed peak search using a modified peakfinder8 algorithm from Cheetah (Barty et al., 2014[Barty, A., Kirian, R. A., Maia, F. R. N. C., Hantke, M., Yoon, C. H., White, T. A. & Chapman, H. (2014). J. Appl. Cryst. 47, 1118-1131.]). Diffraction patterns containing more than ten Bragg peaks were marked as crystal hits. The fraction of diffraction images with crystal hits was reported during data collection on a real-time `hit rate' plot. Diffraction patterns with marked Bragg spots and resolution rings were displayed at a refresh rate of 1 Hz for visual inspection of diffraction quality. After completion of each run, the pipeline applied corrections (pedestal subtraction, gain correction and bad pixel masking) to each raw diffraction image identified as crystal hit. Corrected diffraction images with pixel values in analog-to-digital units (ADU) (keV) were saved as separate HDF5 files for each run. The pipeline automatically invoked the indexamajig program from CrystFEL version 0.8.0 (White et al., 2012[White, T. A., Kirian, R. A., Martin, A. V., Aquila, A., Nass, K., Barty, A. & Chapman, H. N. (2012). J. Appl. Cryst. 45, 335-341.]) to index corrected diffraction patterns with crystal hits identified in the previous step. Indexing was performed with known unit-cell parameters using MOSFLM (Battye et al., 2011[Battye, T. G. G., Kontogiannis, L., Johnson, O., Powell, H. R. & Leslie, A. G. W. (2011). Acta Cryst. D67, 271-281.]) with peak-finding parameters, beam-center position and sample-to-detector distance optimized at the beginning of the experiment. The number of indexed patterns and the average resolution of found peaks for each run were automatically written to a shared spreadsheet. The Monte Carlo merging using process_hkl program from CrystFEL was initiated each time when an interval of 5000 indexed patterns was reached. Finally, merged Bragg spot intensities were converted to an MTZ file using create-mtz script from CrystFEL. Results from the well configured real-time SFX data-processing pipeline at SwissFEL were sufficient to judge the overall progress of the experiment. However, to obtain the best results, the data sets were re-processed after the experiment with carefully optimized parameters.

2.4. Off-line data processing, phasing and refinement

The CrystFEL software-suite version 0.9.1 (White et al., 2012[White, T. A., Kirian, R. A., Martin, A. V., Aquila, A., Nass, K., Barty, A. & Chapman, H. N. (2012). J. Appl. Cryst. 45, 335-341.]) was used to analyze corrected diffraction images identified as crystal hits by the real-time SFX data-processing pipeline. The indexamajig program performed peak finding using the peakfinder8 algorithm with threshold 70 for the nominal SASE and 170 ADU for the large-BW data sets with the minimal signal-to-noise ratio (SNR) = 5. Other peak-finding parameters were --min-pix-count = 2 and --local-bg-radius = 4. The geometry of the multi-segment JUNGFRAU 16M detector was refined using geoptimizer from CrystFEL (Yefanov et al., 2015[Yefanov, O., Mariani, V., Gati, C., White, T. A., Chapman, H. N. & Barty, A. (2015). Opt. Express, 23, 28459-28470.]). The detector-shift script from CrystFEL was used to optimize the beam-center position. The sample-to-detector distance was optimized by finding the distance for which the standard deviation of the distribution of unit-cell parameters was the smallest, as described by Nass, Cheng et al. (2020[Nass, K., Cheng, R., Vera, L., Mozzanica, A., Redford, S., Ozerov, D., Basu, S., James, D., Knopp, G., Cirelli, C., Martiel, I., Casadei, C., Weinert, T., Nogly, P., Skopintsev, P., Usov, I., Leonarski, F., Geng, T., Rappas, M., Doré, A. S., Cooke, R., Nasrollahi Shirazi, S., Dworkowski, F., Sharpe, M., Olieric, N., Bacellar, C., Bohinc, R., Steinmetz, M. O., Schertler, G., Abela, R., Patthey, L., Schmitt, B., Hennig, M., Standfuss, J., Wang, M. & Milne, C. J. (2020). IUCrJ, 7, 965-975.]) and Nass et al. (2016[Nass, K., Meinhart, A., Barends, T. R. M., Foucar, L., Gorel, A., Aquila, A., Botha, S., Doak, R. B., Koglin, J., Liang, M., Shoeman, R. L., Williams, G., Boutet, S. & Schlichting, I. (2016). IUCrJ, 3, 180-191.]).

Off-line indexing of the nominal SASE and large-BW data sets was performed with the XGANDALF algorithm (Gevorkov et al., 2019[Gevorkov, Y., Yefanov, O., Barty, A., White, T. A., Mariani, V., Brehm, W., Tolstikova, A., Grigat, R.-R. & Chapman, H. N. (2019). Acta Cryst. A75, 694-704.]) with known unit-cell parameters. This indexing algorithm does not consider BW. The large-BW data set was also indexed using the pinkIndexer algorithm recently developed for processing pink-beam X-ray and electron-diffraction snapshots (Gevorkov et al., 2020[Gevorkov, Y., Barty, A., Brehm, W., White, T. A., Tolstikova, A., Wiedorn, M. O., Meents, A., Grigat, R.-R., Chapman, H. N. & Yefanov, O. (2020). Acta Cryst. A76, 121-131.]). A modified CrystFEL 0.9.1 version was used to process the large-BW data set with pinkIndexer (removed lines 865–876 from integration.c source-code file) to enable correct handling of an increased number of predicted reflections in CrystFEL arising from enlarged spectral width of the X-ray pulses. In addition, CrystFEL considers BW during spot prediction but not during prediction refinement, therefore it is disabled by pinkIndexer which uses its own prediction-refinement protocol that considers BW. The option --no-check-peaks, which disables CrystFEL's check if indexing is successful, is automatically added when pinkIndexer is used. PinkIndexer performs this check internally considering BW. The indexamajig parameters used with this algorithm were --fix-profile-radius = 0.001e8, --pinkIndexer-tolerance = 0.04, --pinkIndexer-refinement-type = 4, --pinkIndexer-angle-resolution = 2, --pinkIndexer-considered-peaks-count = 4 and photon_energy_bandwidth = 0.015 defined in the geometry file.

Bragg peak intensity integration in indexamajig was performed using the rings-grad method for all data sets with the parameter --int-radius = 4, 5, 7. To avoid possible bias in the comparison of data-quality indicators between nominal SASE and large-BW data sets due to overlapping reflections from multiple crystals, we used indexamajig parameters --no-multi, --no-retry and --check-peaks for all data sets. Integrated reflection intensities were merged using partialator from the CrystFEL software-suite version 0.8.0 with partiality corrections and post-refinement (White et al., 2016[White, T. A., Mariani, V., Brehm, W., Yefanov, O., Barty, A., Beyerlein, K. R., Chervinskii, F., Galli, L., Gati, C., Nakane, T., Tolstikova, A., Yamashita, K., Yoon, C. H., Diederichs, K. & Chapman, H. N. (2016). J. Appl. Cryst. 49, 680-689.]). Partiality corrections and post-refinement protocols currently implemented in partialator do not consider BW. The following parameters were used for merging in partialator: --model = xsphere, --iterations = 1, --no-deltacchalf and --push-res = 0.5. In addition, partialator from CrystFEL 0.9.1 performed worse than from CrystFEL 0.8.0 (Fig. S7 of the supporting information). The resulting set of merged intensities was converted to the XDS_ASCII format, passed through XSCALE for outlier rejection and converted to structure-factor amplitudes using the XDSCONV software package (Kabsch, 2010[Kabsch, W. (2010). Acta Cryst. D66, 125-132.]). The anomalous correlation coefficient (CCano), SNR, Rsplit and the redundancy of individual hkl intensity measurements were calculated by compare_hkl and check_hkl programs from CrystFEL. The correlation coefficient (CCanoref) between the observed (|ΔFobs|) and expected (|ΔFcalc|) anomalous difference structure-factor amplitudes was calculated using a custom script that is available upon request. The peak-finding threshold and SNR parameters in indexamajig were optimized to yield the highest indexing rates. In addition, the intensity-integration ring sizes were adjusted and various refinement types in pinkIndexer were tested to yield the highest overall CCano values. For partialator, the number of iterations (1, 2 or 3) and push-res values [infinity (the default) or 0.5] with and without --no-deltacchalf were optimized to yield the best de novo structure solution with the minimal number of indexed patterns for our sample and experimental conditions.

Structure determination by the native-SAD phasing method and automatic model building was performed using the autoSHARP pipeline version 2.8.12 (Vonrhein et al., 2007[Vonrhein, C., Blanc, E., Roversi, P. & Bricogne, G. (2007). Methods Mol. Biol. 364, 215-230.]). AutoSHARP searched for initial sulfur sites using SHELXD (Schneider & Sheldrick, 2002[Schneider, T. R. & Sheldrick, G. M. (2002). Acta Cryst. D58, 1772-1779.]), which were then refined with SHARP. After phasing and solvent flattening with automated optimization of the solvent content, autoSHARP performed initial model building with Buccaneer (Cowtan, 2006[Cowtan, K. (2006). Acta Cryst. D62, 1002-1011.]). Automatic completion of the initial models was performed using ARP/wARP version 8 (Langer et al., 2008[Langer, G., Cohen, S. X., Lamzin, V. S. & Perrakis, A. (2008). Nat. Protoc. 3, 1171-1179.]) or the Autobuild module from Phenix (Adams et al., 2010[Adams, P. D., Afonine, P. V., Bunkóczi, G., Chen, V. B., Davis, I. W., Echols, N., Headd, J. J., Hung, L.-W., Kapral, G. J., Grosse-Kunstleve, R. W., McCoy, A. J., Moriarty, N. W., Oeffner, R., Read, R. J., Richardson, D. C., Richardson, J. S., Terwilliger, T. C. & Zwart, P. H. (2010). Acta Cryst. D66, 213-221.]). Additionally, iterative cycles of manual model rebuilding in Coot (Emsley & Cowtan, 2004[Emsley, P. & Cowtan, K. (2004). Acta Cryst. D60, 2126-2132.]) and refinement using Phenix were performed to obtain final models. The model quality was assessed using MolProbity (Chen et al., 2010[Chen, V. B., Arendall, W. B., Headd, J. J., Keedy, D. A., Immormino, R. M., Kapral, G. J., Murray, L. W., Richardson, J. S. & Richardson, D. C. (2010). Acta Cryst. D66, 12-21.]).

2.5. X-ray pulse parameters at SwissFEL

The X-ray pulses at SwissFEL (Prat, Abela et al., 2020[Prat, E., Abela, R., Aiba, M., Alarcon, A., Alex, J., Arbelo, Y., Arrell, C., Arsov, V., Bacellar, C., Beard, C., Beaud, P., Bettoni, S., Biffiger, R., Bopp, M., Braun, H.-H., Calvi, M., Cassar, A., Celcer, T., Chergui, M., Chevtsov, P., Cirelli, C., Citterio, A., Craievich, P., Divall, M. C., Dax, A., Dehler, M., Deng, Y., Dietrich, A., Dijkstal, P., Dinapoli, R., Dordevic, S., Ebner, S., Engeler, D., Erny, C., Esposito, V., Ferrari, E., Flechsig, U., Follath, R., Frei, F., Ganter, R., Garvey, T., Geng, Z., Gobbo, A., Gough, C., Hauff, A., Hauri, C. P., Hiller, N., Hunziker, S., Huppert, M., Ingold, G., Ischebeck, R., Janousch, M., Johnson, P. J. M., Johnson, S. L., Juranić, P., Jurcevic, M., Kaiser, M., Kalt, R., Keil, B., Kiselev, D., Kittel, C., Knopp, G., Koprek, W., Laznovsky, M., Lemke, H. T., Sancho, D. L., Löhl, F., Malyzhenkov, A., Mancini, G. F., Mankowsky, R., Marcellini, F., Marinkovic, G., Martiel, I., Märki, F., Milne, C. J., Mozzanica, A., Nass, K., Orlandi, G. L., Loch, C. O., Paraliev, M., Patterson, B., Patthey, L., Pedrini, B., Pedrozzi, M., Pradervand, C., Radi, P., Raguin, J.-Y., Redford, S., Rehanek, J., Reiche, S., Rivkin, L., Romann, A., Sala, L., Sander, M., Schietinger, T., Schilcher, T., Schlott, V., Schmidt, T., Seidel, M., Stadler, M., Stingelin, L., Svetina, C., Treyer, D. M., Trisorio, A., Vicario, C., Voulot, D., Wrulich, A., Zerdane, S. & Zimoch, E. (2020). Nat. Photonics, 14, 748-754.]) were generated at a repetition rate of 25 Hz with an upper limit for the pulse duration of 30 fs (FWHM) (estimated from a measured electron-bunch length of 20 fs RMS). The pulses were focused by a pair of Kirkpatrick–Baez mirrors to ∼5 × 7 µm, measured by knife-edge scans at the sample position.

In the nominal SASE mode, the X-ray pulse parameters were as follows: a photon energy of 5.99 keV (2.069 Å) with an energy BW (ΔE/E) of 0.17% (Fig. S1) as measured by the photon single-shot spectrometer (PSSS) (Juranić et al., 2018[Juranić, P., Rehanek, J., Arrell, C. A., Pradervand, C., Ischebeck, R., Erny, C., Heimgartner, P., Gorgisyan, I., Thominet, V., Tiedtke, K., Sorokin, A., Follath, R., Makita, M., Seniutinas, G., David, C., Milne, C. J., Lemke, H., Radovic, M., Hauri, C. P. & Patthey, L. (2018). J. Synchrotron Rad. 25, 1238-1248.]). The average pulse energy was 170 µJ as measured by the SwissFEL pulse intensity monitor (Juranić et al., 2018[Juranić, P., Rehanek, J., Arrell, C. A., Pradervand, C., Ischebeck, R., Erny, C., Heimgartner, P., Gorgisyan, I., Thominet, V., Tiedtke, K., Sorokin, A., Follath, R., Makita, M., Seniutinas, G., David, C., Milne, C. J., Lemke, H., Radovic, M., Hauri, C. P. & Patthey, L. (2018). J. Synchrotron Rad. 25, 1238-1248.]). In the large-BW mode, the X-ray pulse parameters were as follows: a photon energy of 6.02 keV (2.059 Å) with an energy BW (ΔE/E) of 2.2% (Fig. S2) as measured by monochromator scans. The average pulse energy was 380 µJ. Using an estimated beamline transmission of ∼50%, and attenuation factors of 50% for the nominal SASE and 85% for the large-BW X-ray pulses, the pulse energy delivered to the sample was ∼35 to 40 µJ.

The large-BW X-ray pulses were generated at SwissFEL by maximizing the energy chirp and optimizing the compression scheme of the electron beam. By using this method, the X-ray BW can be continuously tuned up to ∼4% in the whole range of X-ray photon energies between 2 and 12.4 keV accessible at SwissFEL (Prat, Dijkstal et al., 2020[Prat, E., Dijkstal, P., Ferrari, E. & Reiche, S. (2020). Phys. Rev. Lett. 124, 074801.]; Saa Hernandez et al., 2016[Saa Hernandez, A., Prat, E., Bettoni, S., Beutner, B. & Reiche, S. (2016). Phys. Rev. Accel. Beams, 19, 090702.]).

2.6. Simulations of diffraction patterns

To simulate X-ray diffraction patterns, we used the nanoBragg program (https://bl831.als.lbl.gov/~jamesh/nanoBragg/). We used the geometry of the Alvra end station, specifically the dimensions of the JUNGFRAU 16M detector of ∼373.65 × 373.65 mm with square pixel size of 75 µm2, sample-to-detector distance of 95 mm, and X-ray photon energy of 4.5 keV with 3% and 3.5% that are within the capabilities of SwissFEL. With this setup, the maximal resolution at the edge of the diffraction patterns was ∼2.6 Å. We simulated diffraction patterns of the 5-HT2B receptor crystal structure (Protein Data Bank; PDB entry 4nc3; Liu et al., 2013[Liu, W., Wacker, D., Gati, C., Han, G. W., James, D., Wang, D., Nelson, G., Weierstall, U., Katritch, V., Barty, A., Zatsepin, N. A., Li, D., Messerschmidt, M., Boutet, S., Williams, G. J., Koglin, J. E., Seibert, M. M., Wang, C., Shah, S. T., Basu, S., Fromme, R., Kupitz, C., Rendek, K. N., Grotjohann, I., Fromme, P., Kirian, R. A., Beyerlein, K. R., White, T. A., Chapman, H. N., Caffrey, M., Spence, J. C., Stevens, R. C. & Cherezov, V. (2013). Science, 342, 1521-1524.]). Parameters used for the simulations of the whole detector were -lambda 2.755, -dispersion 3.0 or 3.5, -detsize 373.65, -dispsteps 50, -pixel 0.075, -mosaic 0, 0.1, 0.2 or 0.3, -N 10, -water 0, -osc 0, -fluence 1.4e24, -tophat_spots and -nonoize. For simulations of the bottom-right quarter of the JUNGFRAU 16M detector we also used -detsize 150, -Xbeam 0 and -Ybeam 0. For simulations of the 1/16th of the detector in the bottom-right corner, we used -detsize 75, -Xbeam −75, -Ybeam −75, -fluence 1.4e34 and -mosaic_domains 50.

2.7. Data availability

All data are available in the manuscript, the supporting information or at publicly accessible repositories. We deposited all diffraction images containing crystal hits used in this study, final CrystFEL stream and geometry files to the Coherent X-ray Imaging Data Bank (Maia, 2012[Maia, F. R. (2012). Nat. Methods, 9, 854-855.]) with the accession code ID 180 (https://cxidb.org/id-180.html). The atomic coordinates and structure-factor amplitudes were deposited in the PDB under the accession codes 7o44, 7o51, 7o53, 7o5J and 7o5K. The intermediate structures, log files and scripts are available from the corresponding authors upon request.

3. Results

3.1. Experimental setup, X-ray beam and data-collection parameters

To test whether large-BW XFEL pulses offer advantages for SFX when compared with pulses with smaller typical SASE XFEL BW postulated earlier (Dejoie et al., 2013[Dejoie, C., McCusker, L. B., Baerlocher, C., Abela, R., Patterson, B. D., Kunz, M. & Tamura, N. (2013). J. Appl. Cryst. 46, 791-794.]; White et al., 2013[White, T. A., Barty, A., Stellato, F., Holton, J. M., Kirian, R. A., Zatsepin, N. A. & Chapman, H. N. (2013). Acta Cryst. D69, 1231-1240.]), we used well diffracting thaumatin microcrystals. Here, we used the weak anomalous signal from endogenous sulfur atoms in thaumatin molecules as a sensitive data-quality indicator. We evaluated the data quality while systematically decreasing the number of indexed patterns, and determined the minimum for successful native-SAD phasing and automatic model building for each data set and indexing method (see Methods[link]).

The SFX data sets from thaumatin microcrystals were measured at SwissFEL (Prat, Abela et al., 2020[Prat, E., Abela, R., Aiba, M., Alarcon, A., Alex, J., Arbelo, Y., Arrell, C., Arsov, V., Bacellar, C., Beard, C., Beaud, P., Bettoni, S., Biffiger, R., Bopp, M., Braun, H.-H., Calvi, M., Cassar, A., Celcer, T., Chergui, M., Chevtsov, P., Cirelli, C., Citterio, A., Craievich, P., Divall, M. C., Dax, A., Dehler, M., Deng, Y., Dietrich, A., Dijkstal, P., Dinapoli, R., Dordevic, S., Ebner, S., Engeler, D., Erny, C., Esposito, V., Ferrari, E., Flechsig, U., Follath, R., Frei, F., Ganter, R., Garvey, T., Geng, Z., Gobbo, A., Gough, C., Hauff, A., Hauri, C. P., Hiller, N., Hunziker, S., Huppert, M., Ingold, G., Ischebeck, R., Janousch, M., Johnson, P. J. M., Johnson, S. L., Juranić, P., Jurcevic, M., Kaiser, M., Kalt, R., Keil, B., Kiselev, D., Kittel, C., Knopp, G., Koprek, W., Laznovsky, M., Lemke, H. T., Sancho, D. L., Löhl, F., Malyzhenkov, A., Mancini, G. F., Mankowsky, R., Marcellini, F., Marinkovic, G., Martiel, I., Märki, F., Milne, C. J., Mozzanica, A., Nass, K., Orlandi, G. L., Loch, C. O., Paraliev, M., Patterson, B., Patthey, L., Pedrini, B., Pedrozzi, M., Pradervand, C., Radi, P., Raguin, J.-Y., Redford, S., Rehanek, J., Reiche, S., Rivkin, L., Romann, A., Sala, L., Sander, M., Schietinger, T., Schilcher, T., Schlott, V., Schmidt, T., Seidel, M., Stadler, M., Stingelin, L., Svetina, C., Treyer, D. M., Trisorio, A., Vicario, C., Voulot, D., Wrulich, A., Zerdane, S. & Zimoch, E. (2020). Nat. Photonics, 14, 748-754.]) at the Alvra experimental station, see Section 4.1 of Milne et al. (2017[Milne, C., Schietinger, T., Aiba, M., Alarcon, A., Alex, J., Anghel, A., Arsov, V., Beard, C., Beaud, P., Bettoni, S., Bopp, M., Brands, H., Brönnimann, M., Brunnenkant, I., Calvi, M., Citterio, A., Craievich, P., Csatari Divall, M., Dällenbach, M., D'Amico, M., Dax, A., Deng, Y., Dietrich, A., Dinapoli, R., Divall, E., Dordevic, S., Ebner, S., Erny, C., Fitze, H., Flechsig, U., Follath, R., Frei, F., Gärtner, F., Ganter, R., Garvey, T., Geng, Z., Gorgisyan, I., Gough, C., Hauff, A., Hauri, C., Hiller, N., Humar, T., Hunziker, S., Ingold, G., Ischebeck, R., Janousch, M., Juranić, P., Jurcevic, M., Kaiser, M., Kalantari, B., Kalt, R., Keil, B., Kittel, C., Knopp, G., Koprek, W., Lemke, H., Lippuner, T., Llorente Sancho, D., Löhl, F., Lopez-Cuenca, C., Märki, F., Marcellini, F., Marinkovic, G., Martiel, I., Menzel, R., Mozzanica, A., Nass, K., Orlandi, G., Ozkan Loch, C., Panepucci, E., Paraliev, M., Patterson, B., Pedrini, B., Pedrozzi, M., Pollet, P., Pradervand, C., Prat, E., Radi, P., Raguin, J.-Y., Redford, S., Rehanek, J., Réhault, J., Reiche, S., Ringele, M., Rittmann, J., Rivkin, L., Romann, A., Ruat, M., Ruder, C., Sala, L., Schebacher, L., Schilcher, T., Schlott, V., Schmidt, T., Schmitt, B., Shi, X., Stadler, M., Stingelin, L., Sturzenegger, W., Szlachetko, J., Thattil, D., Treyer, D., Trisorio, A., Tron, W., Vetter, S., Vicario, C., Voulot, D., Wang, M., Zamofing, T., Zellweger, C., Zennaro, R., Zimoch, E., Abela, R., Patthey, L. & Braun, H.-H. (2017). Appl. Sci. 7, 720.]). X-ray pulses with photon energy of 5.99 keV (2.069 Å) and nominal SASE energy BW (ΔE/E) of 0.17%, and with photon energy of 6.02 keV (2.059 Å) and large energy BW (ΔE/E) of 2.2%, were used to obtain two high-resolution thaumatin SFX data sets (SASE and large-BW data sets, respectively). Diffraction images were recorded at a repetition rate of 25 Hz with the low-noise automatic gain switching JUNGFRAU 16M detector (Mozzanica et al., 2016[Mozzanica, A., Bergamaschi, A., Brueckner, M., Cartier, S., Dinapoli, R., Greiffenberg, D., Jungmann-Smith, J., Maliakal, D., Mezza, D., Ramilli, M., Ruder, C., Schaedler, L., Schmitt, B., Shi, X. & Tinti, G. (2016). J. Instrum. 11, C02047-C02047.]) installed at the back of the Alvra Prime experimental chamber. Thaumatin microcrystals embedded in LCP were delivered to the focused X-ray pulses using a high-viscosity LCP injector (see Methods[link] for more details).

In total, we recorded 350 000 images for the SASE data set, out of which 211 783 contained crystal hits identified by the real-time SFX data-processing pipeline (see Methods[link]). After increasing the photon-energy BW, we recorded 710 000 images for the large-BW data set, out of which 225 346 contained crystal hits identified by the pipeline. The lower hit rate of the large-BW data set is due to lower concentration of the microcrystals embedded in LCP that was adjusted to reduce the probability of measuring multiple hits. The average per-shot photon energy of the SASE data set was 5992 eV with a standard deviation of 4 eV (Fig. S1). This small variation of the per-shot photon energy had negligible effect on the data-quality indicators of the SASE data set, therefore we used fixed photon energy to index all diffraction patterns from the SASE data set.

3.2. De novo structure determination of the SASE data set

To perform an unbiased comparison excluding differences in experimental setups, sample characteristics and environmental conditions, we measured the SASE and large-BW data sets at the same XFEL facility during a single beam time while performing the best effort to maintain constant experimental parameters. Additionally, to avoid crystallographer's bias, we used a black-box native-SAD phasing and model-building pipeline implemented in autoSHARP to determine the minimum number of images needed for phasing. The criterion for successful automatic de novo structure determination was more than 80% of built residues correctly placed in the amino acid sequence.

First, to establish the baseline for the evaluation of potential benefits of using large-BW X-ray pulses for SFX, we analyzed the thaumatin data set measured with 5.99 keV X-ray pulses with a nominal SASE BW of 0.17%. From this data set containing 211 783 hits, CrystFEL indexed 102 277 patterns (indexing rate of 48.3%) using the XGANDALF algorithm developed for indexing diffraction patterns obtained with small-BW radiation (Δλ/λ ≤ 5 × 10−3). The per-injector optimized detector distances were 95.50 and 96.36 mm. The maximal resolution of this data set was limited by the photon wavelength and geometrical setup to 2.0 Å.

For the whole SASE thaumatin data set with 102 277 indexed images and overall CCano of 0.153 (Table 1[link]), autoSHARP determined the heavy-atom substructure and after phasing, density modification and model building, the final model consisted of 204 residues correctly placed in the protein sequence. This model was manually adjusted in Coot and refined in Phenix. Phasing details and data-quality indicators from CrystFEL are given in Tables S1 and S2 of the supporting information. The structure determination in autoSHARP with 90 000 indexed images did not result in an interpretable electron-density map. Therefore, we established that the baseline number of indexed images used for comparisons of data-quality indicators between the SASE and large-BW data sets is ∼102 000.

Table 1
SFX data-collection and refinement statistics

Data set SASE (XGANDALF) Large BW (XGANDALF) Large BW (pinkIndexer)
  Baseline Baseline 50 000 Baseline 30 000
Wavelength 2.069 Å/5.99 keV 2.059 Å/6.02 keV
Bandwidth ΔE/E (%) 0.17 2.2
Temperature (K) 297
Space group P41212
Cell dimensions a, b, c (Å) 58.52, 58.52, 151.30
Number of collected patterns 350 000 710 000
Number of hits (% of collected) 211 783 (60.5) 225 346 (31.7)
Number of indexed patterns (% of hits) 102 277 (48.3) 102 000 50 000 102 000 30 000
Number of unique reflections 33 976 25 539 25 539 31 565 31 565
Resolution range (Å) 24.8–2.0 24.8–2.2 24.8–2.2 25.25–2.05 25.25–2.05
Completeness (%) 100 (99.8) 100 (99.9) 100 (96.5) 100 (99.8) 100 (92.0)
Average redundancy 200 (11) 418 (19) 205 (9) 1218 (30) 356 (8)
CC1/2 0.993 (0.869) 0.999 (0.501) 0.998 (0.424) 0.999 (0.426) 0.997 (0.218)
CC 0.998 (0.964) 0.999 (0.817) 0.999 (0.772) 0.999 (0.773) 0.999 (0.600)
CCano 0.153 (0.145) 0.492 (0.010) 0.284 (0.133) 0.670 (0.002) 0.509 (0.078)
Overall I/σ(I) 15.09 (4.42) 15.82 (1.18) 11.09 (0.94) 23.12 (1.01) 12.90 (1.02)
Rsplit (%) 5.10 (27.30) 3.80 (89.62) 5.49 (104.20) 2.43 (100.60) 4.58 (151.50)
Refinement          
Resolution (Å) 24.73–2.00 23.14–2.20 24.73–2.20 23.14–2.05 23.14–2.05
Number of reflections used in refinement 18 561 14 057 14 017 17 261 16 369
Rwork/Rfree 0.1430/0.1755 0.1528/0.1966 0.1564/0.1941 0.1571/0.1738 0.1610/0.1919
Number of atoms 1712 1614 1615 1632  
   Protein 1558 1558 1558 1558 1558
   Ligand 10 10 10 10 10
   Water 144 46 47 64 78
Overall B factor (Å2) 28.4 49.0 49.4 48.0 45.4
R.m.s. deviations          
   Bond lengths (Å) 0.011 0.010 0.009 0.008 0.009
   Bond angles (°) 1.664 1.130 1.086 0.964 0.995
Ramachandran plot (%)          
   Most favored 97.07 96.10 97.07 96.59 97.07
   Additionally allowed 2.93 3.90 2.44 3.41 2.93
   Disallowed 0 0 0.49 0 0
†Numbers in parentheses refer to the highest resolution shell.
Rsplit = 2−1/2∑|IevenIodd|/[(1/2)∑(Ieven + Iodd)].

3.3. De novo structure determination of the large-BW data set indexed with XGANDALF

Although the XGANDALF indexing algorithm was developed for indexing diffraction patterns obtained with small-BW radiation (ΔE/E ≤ 5 × 10−3), we tested its performance on the large-BW thaumatin data set measured with photon energy of 6.02 keV and 2.2% BW.

From 225 346 crystal hits identified by the real-time SFX data-processing pipeline, the XGANDALF algorithm indexed 130 621 patterns (indexing rate of 57.9%). The optimized detector distance for this data set was 95.0 mm. The maximal resolution of this data set was limited to 2.2 Å as indicated by the SNR of 1.38 and CC1/2 of 0.594 in the highest resolution shell between 2.20 and 2.25 Å (Table S3). Although we used thaumatin crystals from the same sample-preparation batch, we observed a lower resolution limit for this data set compared with the SASE data set. In particular, the SNR in the SASE data set in the 2.00–2.05 Å resolution shell is 4.42, almost three times higher than in this data set that has almost 28 000 more indexed images. We attribute this observation to the reduced SNR at high scattering angles due to the Bragg spot intensity dilution effect arising from thicker Ewald sphere when using large-BW X-rays for crystallography and to incorrect spot predictions due to uncertainty of the incident wavelength that gave rise to a given Bragg spot.

The heavy-atom substructure determination, phasing and automatic model building in autoSHARP as the structure determination with all indexed images was straightforward, owing to accurate measurement of the anomalous signal as indicated by the high overall CCano of 0.476 (Table S4). After phasing, density modification and automatic model building in autoSHARP, the final model consisted of 200 residues correctly placed in the protein sequence (Table S1).

For direct comparison of data-quality indicators and native-SAD phasing results of the large-BW data set indexed using XGANDALF with the SASE data set, we prepared the baseline large-BW data set with 102 000 randomly selected indexed patterns from the whole data set. Overall and split-into-resolution-shells statistics for this baseline large-BW data set are shown in Tables 1[link] and S5. Also for this data set, phasing and automatic model building in autoSHARP was undemanding. The final model consisted of 199 residues correctly placed in the protein sequence.

We systematically decreased the number of indexed patterns and determined that 50 000 indexed images from the large-BW thaumatin data set indexed with XGANDALF was the minimum for successful automatic native-SAD phasing and model building in autoSHARP. After phasing, density modification and automatic model building, the final model consisted of 204 residues correctly placed in the protein sequence. The overall and split-into-resolution-shells statistics for this data set are shown in Tables 1[link] and S6

This result shows a twofold decrease in the number of indexed images needed for automatic de novo structure determination of thaumatin when compared with the data set measured with a nominal SASE BW of 0.17% and indexed with XGANDALF. Consequently, we determined that similar precision of structure-factor amplitudes can be obtained in SFX with at least twofold reduction in sample and beam-time consumption using X-ray pulses with 2.2% BW when compared with the nominal SASE BW of 0.17%.

We performed ten trials in autoSHARP with resolution cut-offs between 2.2 and 4.5 Å using a data set with 40 000 indexed images selected randomly from the large-BW data set indexed by XGANDALF but the automatically built model did not contain enough residues placed in the amino acid sequence to satisfy our criteria for successful automatic structure determination in autoSHARP. After visual inspection of the models obtained from autoSHARP with 40 000 images, none of them resembled the shape of a globular molecule, hence it was unlikely that manual model re-building in Coot could yield the correct thaumatin structure. Therefore, we concluded that at least 50 000 indexed images from the large-BW thaumatin data set indexed with XGANDALF were needed for automatic structure determination in autoSHARP.

3.4. De novo structure determination of the large-BW data set indexed with pinkIndexer

The main bottleneck in indexing pink-beam diffraction patterns by automatic indexing algorithms suited for monochromatic crystallography experiments (e.g. XGANDALF) is the uncertainty of the particular incident wavelength that gave rise to a given Bragg spot. This is particularly pronounced at high scattering angles where the diffraction conditions of many Bragg spots are far from the central wavelength and can contribute to the deterioration of the data quality at high resolution observed in the previous section. To overcome this problem, the pinkIndexer algorithm (Gevorkov et al., 2020[Gevorkov, Y., Barty, A., Brehm, W., White, T. A., Tolstikova, A., Wiedorn, M. O., Meents, A., Grigat, R.-R., Chapman, H. N. & Yefanov, O. (2020). Acta Cryst. A76, 121-131.]) was developed for automatic indexing of snapshot diffraction patterns obtained from serial pink-beam X-ray crystallography experiments. It was successfully used in Gevorkov et al. (2020[Gevorkov, Y., Barty, A., Brehm, W., White, T. A., Tolstikova, A., Wiedorn, M. O., Meents, A., Grigat, R.-R., Chapman, H. N. & Yefanov, O. (2020). Acta Cryst. A76, 121-131.]) for processing polychromatic diffraction patterns acquired during serial crystallography experiments at synchrotrons (Meents et al., 2017[Meents, A., Wiedorn, M. O., Srajer, V., Henning, R., Sarrou, I., Bergtholdt, J., Barthelmess, M., Reinke, P. Y. A., Dierksmeyer, D., Tolstikova, A., Schaible, S., Messerschmidt, M., Ogata, C. M., Kissick, D. J., Taft, M. H., Manstein, D. J., Lieske, J., Oberthuer, D., Fischetti, R. F. & Chapman, H. N. (2017). Nat. Commun. 8, 1281.]; Tolstikova et al., 2019[Tolstikova, A., Levantino, M., Yefanov, O., Hennicke, V., Fischer, P., Meyer, J., Mozzanica, A., Redford, S., Crosas, E., Opara, N. L., Barthelmess, M., Lieske, J., Oberthuer, D., Wator, E., Mohacsi, I., Wulff, M., Schmitt, B., Chapman, H. N. & Meents, A. (2019). IUCrJ, 6, 927-937.]).

Here, we explored whether the pinkIndexer algorithm integrated into the CrystFEL data-processing suite allows obtaining accurate measurements of structure-factor amplitudes needed for de novo phasing with fewer diffraction images from the large-BW data set than XGANDALF. As with XGANDALF, we carefully optimized the pinkIndexer parameters to yield the best de novo structure-determination results and data-quality indicators with the least number of indexed images from the large-BW data set (see Methods[link]). From 225 346 crystal hits, the pinkIndexer algorithm indexed 135 482 patterns (indexing rate of 60.1%). The maximal resolution of this data set was limited to 2.05 Å as indicated by the SNR of 1.16 and CC1/2 of 0.597 in the highest resolution shell between 2.05 and 2.10 Å (Table S7). The overall CCano of this data set indexed by pinkIndexer (CCano of 0.746) is higher than that of the same data set indexed by XGANDALF (CCano of 0.476). Additionally, the high-resolution limit of the large-BW data set processed with pinkIndexer (2.05 Å) is higher than that of the same data set processed with XGANDALF (2.2 Å) but not as high as for the SASE data set (2.0 Å). We attribute these improvements to pinkIndexer's ability to utilize BW during indexing and prediction refinement, which XGANDALF lacks.

The heavy-atom substructure determination, phasing and automatic model building in autoSHARP of the large-BW thaumatin data set with all indexed images processed with pinkIndexer was straightforward, owing to very accurate measurement of the anomalous signal as indicated by the high overall CCano of 0.746 (Table S8). After phasing, density modification and automatic model building in autoSHARP, the final model consisted of 199 residues correctly placed in the protein sequence (Table S1).

To assess if the large-BW data set indexed with pinkIndexer is of better quality than the same data set indexed by XGANDALF, we compared data-quality indicators and native-SAD phasing results using data sets with equal numbers of indexed images. For this comparison, we prepared the baseline large-BW data set with 102 000 indexed patterns randomly selected from the whole data set. Overall and split-into-resolution-shells statistics for this data set calculated up to a resolution of 2.05 Å are shown in Tables 1[link] and S9 and up to a resolution of 2.2 Å in Tables S10 and S11. For the data set with 102 000 indexed images, phasing and automatic model building in autoSHARP was successful. After phasing, density modification and automatic model building, the final model consisted of 201 residues correctly placed in the protein sequence (Table S1).

Next, we systematically decreased the number of indexed patterns and determined the minimum for which automatic phasing and model building in autoSHARP was still successful. At least 30 000 indexed images from the large-BW data set indexed with pinkIndexer were needed for automatic structure determination of thaumatin in autoSHARP. The overall and split-into-resolution-shells statistics for this data set are shown in Tables 1[link] and S12, and up to a resolution of 2.2 Å in Tables S13 and S14. For the large-BW data set with 30 000 indexed images, the final model consisted of 200 residues with 167 of them correctly placed in the protein sequence (Table S1). With 25 000 indexed images selected randomly from this data set, automatic structure determination in autoSHARP failed in all trials. With only 30 000 indexed images needed for de novo structure determination and automatic model building, this result demonstrates ∼3.5-fold improvement when compared with the number of images from the SASE thaumatin data (∼102 000) and almost twofold improvement when compared with the number of images from the large-BW data set indexed with XGANDALF (50 000).

4. Discussion

Large-BW XFEL pulses would in principle be beneficial for SFX by improving the partiality of individual reflections and increasing the number of Bragg spots per image (Dejoie et al., 2013[Dejoie, C., McCusker, L. B., Baerlocher, C., Abela, R., Patterson, B. D., Kunz, M. & Tamura, N. (2013). J. Appl. Cryst. 46, 791-794.]; White et al., 2013[White, T. A., Barty, A., Stellato, F., Holton, J. M., Kirian, R. A., Zatsepin, N. A. & Chapman, H. N. (2013). Acta Cryst. D69, 1231-1240.]). As demonstrated theoretically, these improvements lead to a faster convergence rate of SFX data sets in Monte Carlo integration. Therefore, pink-beam or large-BW SFX offers advantages not only for de novo structure determination from small and weakly diffracting crystals but also for other types of SFX experiments, e.g. time-resolved studies, by enabling accurate measurements of structure-factor amplitudes with reduced sample and beam-time consumption. Consequently, this method of data collection at XFELs might facilitate SFX to a larger community of researchers. In particular, structure determination of difficult to crystallize systems, e.g. GPCRs, is currently hindered by the need to grow large and well diffracting crystals that can withstand radiation damage at synchrotron sources. However, SFX allows essentially radiation-damage-free data collection from tiny crystals that tend to be better ordered and, in the case of GPCRs, typically diffract to higher resolution at XFEL sources than at synchrotrons (Stauch & Cherezov, 2018[Stauch, B. & Cherezov, V. (2018). Annu. Rev. Biophys. 47, 377-397.]).

To provide an unbiased assessment, we performed a detailed comparison of the quality of SFX data sets obtained using nominal SASE and large-BW X-ray pulses at SwissFEL. We based the comparison on a very sensitive data-quality indicator available in our data sets: the weak anomalous signal from sulfur atoms (estimated Bijvoet ratio 〈|ΔF|〉/〈F〉 of ∼2% for thaumatin at 6 keV). We compared the anomalous correlation coefficient (CCano), SNR, Rsplit and redundancy of individual hkl measurements as a function of resolution from data sets with the same (102 000) and minimal number of indexed images needed for automatic de novo structure determination.

4.1. Comparison of data-quality indicators between SASE and large-BW data sets with 102 000 images indexed using XGANDALF and pinkIndexer

For the comparisons, we used the maximal resolution limit of 2.2 Å for all data sets. The SASE data set has higher SNR and lower Rsplit factor in the high resolution shells between 2.3 and 2.2 Å than the large-BW data set with the same number of patterns indexed using XGANDALF or pinkIndexer (Fig. 1[link], and Tables S5, S11 and S15). As one of the contributing factors, we can explain the observed reduction of SNR and increase of Rsplit in the large-BW data set at high resolution by a smaller number of photons in a diffraction condition than for the SASE data set because we used similar average pulse energy at the sample position (see Methods[link]). Essentially, the distance between the limiting Ewald spheres for large-BW X-ray pulses at high resolution is larger than the spectral width of the Bragg peaks, therefore smaller numbers of photons contribute to Bragg peaks and at the same time larger numbers of photons contribute to the background scattering, which results in lower SNR and higher Rsplit.

[Figure 1]
Figure 1
Comparison of data-quality indicators between the SASE and large-BW thaumatin data sets indexed by XGANDALF and pinkIndexer calculated with the same number of indexed images (102 000) up to a resolution limit of 2.2 Å. (a) Rsplit, (b) CCano, (c) SNR and (d) redundancy of individual reflections as a function of resolution.

The dilution of Bragg spot intensity at high resolution that arises from the thick Ewald sphere is one of the limitations of using pink or large-BW X-ray radiation for crystallography experiments at synchrotrons. The SNR at high resolution in pink-beam serial synchrotron crystallography experiments can be improved by using setups optimized for reducing background scattering (Meents et al., 2017[Meents, A., Wiedorn, M. O., Srajer, V., Henning, R., Sarrou, I., Bergtholdt, J., Barthelmess, M., Reinke, P. Y. A., Dierksmeyer, D., Tolstikova, A., Schaible, S., Messerschmidt, M., Ogata, C. M., Kissick, D. J., Taft, M. H., Manstein, D. J., Lieske, J., Oberthuer, D., Fischetti, R. F. & Chapman, H. N. (2017). Nat. Commun. 8, 1281.]) but, due to the general pink-beam dilution effect that reduces SNR at high resolution, more photons per exposure are required to obtain similar diffraction-signal levels as with monochromatic radiation, especially for weakly diffracting micro-crystals. However, for typical synchrotron data-collection time scales, the destructive effects of global radiation damage that occur above certain absorbed dose limits (Garman & Weik, 2017[Garman, E. F. & Weik, M. (2017). Protein Crystallography: Methods and Protocols, edited by A. Wlodawer, Z. Dauter & M. Jaskolski, pp. 467-489. New York: Springer.]) hinder the achievable maximal resolution. One way to partially overcome this problem is to spread the absorbed dose over multiple crystals e.g. in a serial synchrotron crystallography experiment. Unfortunately, this approach has severe limitations for pink-beam synchrotron experiments when only small micro-crystals are available or needed [e.g. in time-resolved studies where the intensity of the pump laser traveling throughout the crystalline material decreases exponentially (Grünbein et al., 2020[Grünbein, M. L., Stricker, M., Nass Kovacs, G., Kloos, M., Doak, R. B., Shoeman, R. L., Reinstein, J., Lecler, S., Haacke, S. & Schlichting, I. (2020). Nat. Methods, 17, 681-684.])]. This is because negative effects of radiation damage increase with the number of photons per exposure while the SNR at high resolution decreases with larger X-ray BW. Fortunately, in SFX most of the radiation-damage processes that affect synchrotron data collection are mitigated by femtosecond X-ray pulses (Nass, 2019[Nass, K. (2019). Acta Cryst. D75, 211-218.]). This allows for increasing the intensity of large-BW X-ray pulses to obtain better SNR at high resolution from small micro-crystals than at synchrotrons without the negative effects of radiation damage.

At low resolution the SNR of the large-BW data set indexed by XGANDALF and pinkIndexer is higher than of the SASE data set (Fig. 1[link]), which can also be explained by a larger spectral coverage of the strong low-resolution reflections and consequently higher precision of the measurements of structure-factor amplitudes at low resolution. The higher CCano, CCanoref and lower Rsplit values for the large-BW data set at low resolution (Figs. 1[link] and S3) also indicate this. As expected from theoretical considerations, the redundancy of individual measurements is the largest for the large-BW data set (Fig. 1[link]). We also observe that for the large-BW data set processed with pinkIndexer, the maximal resolution limit, SNR, Rsplit, CC1/2 and CCano are higher than when using XGANDALF (Fig. 1[link], and Tables 1[link], S5, S10 and S11). This validates that pinkIndexer is indeed better suited for indexing diffraction patterns obtained with large-BW X-ray radiation than XGANDALF.

4.2. Comparison of data-quality indicators between SASE and large-BW data sets with minimal number of indexed images needed for de novo structure determination

The minimal numbers of indexed images needed for automatic de novo structure determination from the SASE and large-BW thaumatin data sets indexed by XGANDALF and pinkIndexer were 102 000, 50 000 and 30 000, respectively. We calculated various data-quality indicators as functions of resolution for these data sets using the same high-resolution limit of 2.2 Å (Figs. 2[link] and S3, and Tables S6, S13, S14 and S15). Fig. 2[link] shows that Rsplit and SNR for the large-BW XGANDALF data set with 50 000 images are of lower quality than for the large-BW pinkIndexer data set with 30 000 images or for the SASE data set with 102 000 images. The CCano and CCanoref are similar in the low-resolution range for all three data sets and decrease in higher resolution shells.

[Figure 2]
Figure 2
Comparison of data-quality indicators between the SASE and large-BW thaumatin data sets indexed by XGANDALF and pinkIndexer calculated with the minimal number of indexed images required for automatic de novo structure determination up to a resolution limit of 2.2 Å. (a) Rsplit, (b) CCano, (c) SNR and (d) redundancy of individual reflections as a function of resolution.

Interestingly, the Rsplit, CCanoref and SNR of the large-BW pinkIndexer data set with 30 000 images are similar to the SASE data set with 102 000 images and deteriorate only at high resolution. This observation might be attributed to the higher redundancy and improved partiality of individual reflections in the large-BW data set indexed by pinkIndexer, and the effects associated with the thicker Ewald sphere discussed above.

4.3. Comparison of the data-quality indicators for SASE and large-BW data sets with minimal number of indexed images needed for a complete set of accurate structure-factor amplitudes

To investigate whether large-BW X-ray pulses can also be beneficial for other SFX applications, where molecular replacement is used for structure determination that does not require as high accuracy as native SAD, we calculated and compared data-quality indicators with smaller numbers of indexed images from the SASE and large-BW data sets (Fig. 3[link]). To obtain high completeness (>80%), CC1/2 (>0.8) and SNR (>3) at high resolution from the SASE data set we needed at least 20 000 indexed images. In contrast, from the large-BW data set indexed using pinkIndexer, SNR values similar to the SASE data set with 20 000 images can be obtained with only 5000 indexed images, except for the high resolution shells. We attribute the deterioration of CC1/2, completeness and SNR at high resolution in the large-BW data set to the diffraction dilution effects discussed above and postulate that it can be overcome by increasing the intensity of large-BW XFEL pulses without negative global radiation-damage effects. The high-dynamic range of the JUNGFRAU detector used in this experiment would allow larger pulse intensity without observing overloads (Fig. S4). Therefore, if the large-BW X-ray pulses in our study had higher intensity we could achieve similar quality data also at high resolution with 5000 large-BW images as with 20 000 SASE images. Detailed comparison of Rsplit and CC1/2 as functions of the number of indexed diffraction patterns and resolution for various numbers of indexed patterns from the SASE and large-BW data sets is shown in Figs. S5 and S6.

[Figure 3]
Figure 3
Comparison of data-quality indicators between the SASE and large-BW thaumatin data sets with various number of indexed images calculated up to a resolution limit of 2.05 Å. (a) Completeness, (b) CC1/2, (c) SNR and (d) redundancy of individual reflections as a function of resolution.

4.4. Simulations of diffraction patterns from a typical GPCR crystal structure

Radially streaked and overlapping Bragg spots in diffraction patterns measured from macromolecular crystals with high mosaicity or with very large unit cells are one of the limitations of polychromatic Laue X-ray crystallography at synchrotrons (Ren & Moffat, 1994[Ren, Z. & Moffat, K. (1994). J. Synchrotron Rad. 1, 78-82.]; Shrive et al., 1990[Shrive, A. K., Clifton, I. J., Hajdu, J. & Greenhough, T. J. (1990). J. Appl. Cryst. 23, 169-174.]). Although methods to extract individual Bragg spot intensities from overlapping reflections measured at synchrotrons exist (Shrive et al., 1990[Shrive, A. K., Clifton, I. J., Hajdu, J. & Greenhough, T. J. (1990). J. Appl. Cryst. 23, 169-174.]), they are unpractical for serial crystallography where thousands of still images are measured from crystals of different sizes with no orientation relation between consecutive images. In this study, we used well ordered thaumatin crystals with very small mosaicity and relatively small unit-cell constants. Additionally, the large-area JUNGFRAU 16M detector and the low photon energy of 6 keV decreased the probability of observing overlaps. However, for SFX experiments with large-BW X-ray pulses, overlapping reflections from crystals with high mosaicity and large unit cells might be problematic.

To investigate the effects of realistic mosaicity in SFX with large-BW X-ray pulses, we simulated diffraction patterns using the nanoBragg program (see Methods[link]) for various mosaicity and X-ray BW settings using experimental geometry similar to the Alvra end station at SwissFEL. To focus on GPCR structure determination, as one of the most promising applications of SFX, we used the crystal structure of the 5-HT2B receptor determined via SFX to a resolution of 2.8 Å (PDB entry 4nc3) with unit-cell lengths of a = 61.5, b = 122.2 and c = 168.5 Å (Liu et al., 2013[Liu, W., Wacker, D., Gati, C., Han, G. W., James, D., Wang, D., Nelson, G., Weierstall, U., Katritch, V., Barty, A., Zatsepin, N. A., Li, D., Messerschmidt, M., Boutet, S., Williams, G. J., Koglin, J. E., Seibert, M. M., Wang, C., Shah, S. T., Basu, S., Fromme, R., Kupitz, C., Rendek, K. N., Grotjohann, I., Fromme, P., Kirian, R. A., Beyerlein, K. R., White, T. A., Chapman, H. N., Caffrey, M., Spence, J. C., Stevens, R. C. & Cherezov, V. (2013). Science, 342, 1521-1524.]). We selected this PDB entry as an representative example of published GPCR crystal structures based on the distribution of unit-cell lengths of unique GPCR entries deposited in the PDB (Berman et al., 2003[Berman, H., Henrick, K. & Nakamura, H. (2003). Nat. Struct. Mol. Biol. 10, 980.]) (Fig. 4[link]).

[Figure 4]
Figure 4
Histograms of the unit-cell lengths for all unique GPCR entries deposited in the PDB (Berman et al., 2003[Berman, H., Henrick, K. & Nakamura, H. (2003). Nat. Struct. Mol. Biol. 10, 980.]). Numbers above the bars indicate the count in each bin. The unit-cell lengths of the 5-HT2B receptor belong to one of the most frequently occurring GPCR unit-cell lengths in the PDB (bins marked with the star) and therefore serve as a representative candidate for the simulations.

As an example, Fig. 5[link] shows simulated diffraction patterns from a randomly oriented crystal of the 5-HT2B receptor with mosaicity of 0.2° calculated using 3.5% X-ray pulse BW and a photon energy of 4.5 keV. This simulated result demonstrates that sufficient separation between neighboring Bragg spots for intensity integration can be achieved in the whole achievable resolution range of a typical GPCR crystal with realistic mosaicity and average unit-cell lengths when the X-ray pulse parameters are tuned to specific values predicted by the simulations that are within the capabilities of SwissFEL. This opens up the opportunity for using more than one order of magnitude larger X-ray BW than the typical SASE BW for SFX with low photon energy and a large area detector with small pixel size. Simulated diffraction patterns with crystal mosaicity between 0 and 0.3° and X-ray BW of 3 and 3.5% can be found in Figs. S8–S15. Examples of thaumatin diffraction patterns measured with large-BW and SASE X-ray pulses at SwissFEL are shown in Fig. 6[link] and in the supporting information.

[Figure 5]
Figure 5
Simulated snapshot diffraction patterns from a randomly oriented crystal of the 5-HT2B receptor (PDB entry 4nc3) with mosaicity of 0.2°. The simulations were performed with an X-ray BW of 3.5% (top-hat shape), photon energy of 4.5 keV and experimental geometry similar to the SFX setup at the Alvra end station at SwissFEL with the JUNGFRAU 16M detector. The resolution at the edge of the image is 2.6 Å.
[Figure 6]
Figure 6
A fragment of a snapshot diffraction pattern from single thaumatin microcrystal recorded using the JUNGFRAU 16M detector and a large-BW X-ray pulse (Δλ/λ = 2.2% FWHM) from SwissFEL.

5. Summary and outlook

By using large-BW X-ray pulses, we were able to reduce the number of images needed for obtaining an accurate set of complete structure-factor amplitudes in SFX experiments by more than a factor of three in comparison with using X-ray pulses with nominal SASE BW. We attribute this achievement to improved partiality of individual reflections and increased number of Bragg spots per image obtained with large-BW (pink) X-ray pulses used in combination with the new indexing algorithm developed for processing snapshot polychromatic diffraction patterns from crystals in random orientations. To obtain these results, we measured two large SFX thaumatin data sets using the same experimental conditions at SwissFEL with the only changes between measurements being the average spectral BW of the X-ray pulses (0.17 and 2.2%). Moreover, we performed detailed comparisons of data-quality indicators (Rsplit, CCano, CCanoref, CC1/2, SNR and completeness) calculated for data sets with the same number of indexed images and determined the minimum needed for automatic de novo structure determination via the native-SAD phasing method for each data set.

We deliberately used a model system and employed the weak anomalous signal from endogenous sulfur atoms to be able to provide unbiased assessment of the data-set accuracy between SASE and large-BW data sets. Additionally, we determined the minimal numbers of images from the two data sets needed for obtaining high-quality data sets for typical SFX applications that do not require as high accuracy as native SAD, such as time-resolved SFX and structure determination via molecular replacement. We found that the minimal number of images needed for a good quality data set could be reduced by a factor of four when using X-ray pulses with a large BW of 2.2% when compared with X-ray pulses with a nominal SASE BW of 0.17% and similar experimental conditions.

This makes the method of pink-beam SFX very well suited not only for cases where a limited amount of sample is available (e.g. GPCR structure determination) but also when in general it is difficult to obtain large high-quality crystals for structure determination at synchrotrons (e.g. membrane proteins) and only microcrystals are available. In addition, with the significant reduction in the number of indexed images needed for a high-quality data set and the development of automatic real-time data-processing pipelines, this method enables efficient usage of beam time and thus facilitates SFX to a larger community of researchers without extensive SFX experience.

X-ray pulses with increased spectral BW can be produced at other XFEL facilities and are not unique only to SwissFEL; however, because of the SwissFEL accelerator design with specific components that preserve the electron-beam quality after electron-bunch compression, SwissFEL can produce X-ray pulses with the largest energy spread. Moreover, the large energy spread is easier to achieve at low photon energies, which then requires SFX instrumentation optimized for data collection with long wavelengths such as He or vacuum environments and large-area X-ray detectors.

Upgraded third-generation synchrotron sources will provide ∼100 times larger average photon flux density in pink-beam modes. However, even with the shortest exposure times available at synchrotrons (∼100 ps), the radiation-damage effects caused by primary damage mechanisms cannot be outrun. The absorbed dose limits established for room-temperature serial synchrotron crystallography experiments (de la Mora et al., 2020[Mora, E. de la, Coquelle, N., Bury, C. S., Rosenthal, M., Holton, J. M., Carmichael, I., Garman, E. F., Burghammer, M., Colletier, J. P. & Weik, M. (2020). Proc. Natl Acad. Sci. USA, 117, 4142-4151.]; Ebrahim et al., 2019[Ebrahim, A., Moreno-Chicano, T., Appleby, M. V., Chaplin, A. K., Beale, J. H., Sherrell, D. A., Duyvesteyn, H. M. E., Owada, S., Tono, K., Sugimoto, H., Strange, R. W., Worrall, J. A. R., Axford, D., Owen, R. L. & Hough, M. A. (2019). IUCrJ, 6, 543-551.]) can be easily exceeded at upgraded synchrotron sources during experiments with small, weakly diffracting and radiation-sensitive microcrystals. In contrast to room-temperature pink-beam serial crystallography at synchrotrons, obtaining high-quality data from small and weakly diffracting microcrystals with large-BW SFX at XFEL sources is not limited by radiation damage. Additionally, the optimal photon energy and BW of the XFEL pulses for a given crystal system can be easily estimated with simulations before the experiment and tuned at the beamline to achieve sufficient Bragg spot separation and resolution. This, in combination with the new pink-beam indexing software that is fully automatic and can deal with multiple crystals per pattern, suggests that pink-beam SFX might become the mainstream method for measuring accurate structure-factor amplitudes from small micro-crystals with reduced sample and beam-time consumption at XFEL facilities. In addition to indexing, other aspects of serial crystallography data-processing software such as partiality corrections and post-refinement protocols should be updated to include wider X-ray BW. Further improvements could be achieved by using better Bragg spot prediction and integration methods that are able to resolve spot overlaps and model Bragg spot shapes and the intensity of each pixel contributing to a Bragg spot by taking into account the crystal and X-ray pulse properties, such as per-shot X-ray spectra (Mendez et al., 2020[Mendez, D., Bolotovsky, R., Bhowmick, A., Brewster, A. S., Kern, J., Yano, J., Holton, J. M. & Sauter, N. K. (2020). IUCrJ, 7, 1151-1167.]). Moreover, a promising method has been proposed for obtaining sub-femtosecond time resolution in pump–probe SFX experiments with polychromatic hard X-ray FEL pulses with an energy chirp available at SwissFEL (Fadini et al., 2020[Fadini, A., Reiche, S., Nass, K. & van Thor, J. J. (2020). Appl. Sci. 10, 2599.]).

In conclusion, we have demonstrated that XFEL-based pink-beam SFX is not only feasible but also advantageous. Using a well characterized model system and weak anomalous signal, we showed that significantly fewer indexed patterns are required for de novo phasing and for obtaining sufficiently accurate sets of complete structure factors for general SFX applications using large-BW X-ray pulses compared with nominal SASE BW. The reduction of the required amounts of sample and beam time offered by pink-beam SFX, as well as the automatic SFX data-processing pipeline developed at SwissFEL combined with experienced beamline staff, is expected to improve the accessibility of XFELs for unexperienced SFX users. We expect pink-beam SFX data collection to be particularly useful for SFX projects where it is difficult to obtain large amounts of sample typically required for SFX or where only tiny microcrystals are available that are too small for structural studies at synchrotron sources.

Footnotes

Current address: European XFEL GmbH, Holzkoppel 4, 22869 Schenefeld, Germany

Acknowledgements

We thank Christoph Bostedt for discussion, Pavle Juranić for help with the PSSS setup and data analysis, Meitian Wang and May E. Marsh for laboratory space and materials needed to prepare protein crystals, and Filipe Maia for uploading the SFX data to CXIDB.org.

Funding information

We thank SwissFEL and the Swiss Light Source for logistic support and funding.

References

First citationAdams, P. D., Afonine, P. V., Bunkóczi, G., Chen, V. B., Davis, I. W., Echols, N., Headd, J. J., Hung, L.-W., Kapral, G. J., Grosse-Kunstleve, R. W., McCoy, A. J., Moriarty, N. W., Oeffner, R., Read, R. J., Richardson, D. C., Richardson, J. S., Terwilliger, T. C. & Zwart, P. H. (2010). Acta Cryst. D66, 213–221.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationBarends, T. R., Foucar, L., Ardevol, A., Nass, K., Aquila, A., Botha, S., Doak, R. B., Falahati, K., Hartmann, E., Hilpert, M., Heinz, M., Hoffmann, M. C., Kofinger, J., Koglin, J. E., Kovacsova, G., Liang, M., Milathianaki, D., Lemke, H. T., Reinstein, J., Roome, C. M., Shoeman, R. L., Williams, G. J., Burghardt, I., Hummer, G., Boutet, S. & Schlichting, I. (2015). Science, 350, 445–450.  Web of Science CrossRef CAS PubMed Google Scholar
First citationBarends, T. R., Foucar, L., Botha, S., Doak, R. B., Shoeman, R. L., Nass, K., Koglin, J. E., Williams, G. J., Boutet, S., Messerschmidt, M. & Schlichting, I. (2014). Nature, 505, 244–247.  Web of Science CrossRef CAS PubMed Google Scholar
First citationBarty, A., Caleman, C., Aquila, A., Timneanu, N., Lomb, L., White, T. A., Andreasson, J., Arnlund, D., Bajt, S., Barends, T. R., Barthelmess, M., Bogan, M. J., Bostedt, C., Bozek, J. D., Coffee, R., Coppola, N., Davidsson, J., Deponte, D. P., Doak, R. B., Ekeberg, T., Elser, V., Epp, S. W., Erk, B., Fleckenstein, H., Foucar, L., Fromme, P., Graafsma, H., Gumprecht, L., Hajdu, J., Hampton, C. Y., Hartmann, R., Hartmann, A., Hauser, G., Hirsemann, H., Holl, P., Hunter, M. S., Johansson, L., Kassemeyer, S., Kimmel, N., Kirian, R. A., Liang, M., Maia, F. R., Malmerberg, E., Marchesini, S., Martin, A. V., Nass, K., Neutze, R., Reich, C., Rolles, D., Rudek, B., Rudenko, A., Scott, H., Schlichting, I., Schulz, J., Seibert, M. M., Shoeman, R. L., Sierra, R. G., Soltau, H., Spence, J. C., Stellato, F., Stern, S., Struder, L., Ullrich, J., Wang, X., Weidenspointner, G., Weierstall, U., Wunderer, C. B. & Chapman, H. N. (2012). Nat. Photonics, 6, 35–40.  Web of Science CrossRef CAS PubMed Google Scholar
First citationBarty, A., Kirian, R. A., Maia, F. R. N. C., Hantke, M., Yoon, C. H., White, T. A. & Chapman, H. (2014). J. Appl. Cryst. 47, 1118–1131.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationBattye, T. G. G., Kontogiannis, L., Johnson, O., Powell, H. R. & Leslie, A. G. W. (2011). Acta Cryst. D67, 271–281.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationBatyuk, A., Galli, L., Ishchenko, A., Han, G. W., Gati, C., Popov, P. A., Lee, M. Y., Stauch, B., White, T. A., Barty, A., Aquila, A., Hunter, M. S., Liang, M., Boutet, S., Pu, M., Liu, Z. J., Nelson, G., James, D., Li, C., Zhao, Y., Spence, J. C., Liu, W., Fromme, P., Katritch, V., Weierstall, U., Stevens, R. C. & Cherezov, V. (2016). Sci. Adv. 2, e1600292.  Web of Science CrossRef PubMed Google Scholar
First citationBerman, H., Henrick, K. & Nakamura, H. (2003). Nat. Struct. Mol. Biol. 10, 980.  Web of Science CrossRef Google Scholar
First citationChapman, H. N., Fromme, P., Barty, A., White, T. A., Kirian, R. A., Aquila, A., Hunter, M. S., Schulz, J., DePonte, D. P., Weierstall, U., Doak, R. B., Maia, F. R., Martin, A. V., Schlichting, I., Lomb, L., Coppola, N., Shoeman, R. L., Epp, S. W., Hartmann, R., Rolles, D., Rudenko, A., Foucar, L., Kimmel, N., Weidenspointner, G., Holl, P., Liang, M., Barthelmess, M., Caleman, C., Boutet, S., Bogan, M. J., Krzywinski, J., Bostedt, C., Bajt, S., Gumprecht, L., Rudek, B., Erk, B., Schmidt, C., Hömke, A., Reich, C., Pietschner, D., Strüder, L., Hauser, G., Gorke, H., Ullrich, J., Herrmann, S., Schaller, G., Schopper, F., Soltau, H., Kühnel, K. U., Messerschmidt, M., Bozek, J. D., Hau-Riege, S. P., Frank, M., Hampton, C. Y., Sierra, R. G., Starodub, D., Williams, G. J., Hajdu, J., Timneanu, N., Seibert, M. M., Andreasson, J., Rocker, A., Jönsson, O., Svenda, M., Stern, S., Nass, K., Andritschke, R., Schröter, C. D., Krasniqi, F., Bott, M., Schmidt, K. E., Wang, X., Grotjohann, I., Holton, J. M., Barends, T. R., Neutze, R., Marchesini, S., Fromme, R., Schorb, S., Rupp, D., Adolph, M., Gorkhover, T., Andersson, I., Hirsemann, H., Potdevin, G., Graafsma, H., Nilsson, B. & Spence, J. C. (2011). Nature, 470, 73–77.  Web of Science CrossRef CAS PubMed Google Scholar
First citationChen, V. B., Arendall, W. B., Headd, J. J., Keedy, D. A., Immormino, R. M., Kapral, G. J., Murray, L. W., Richardson, J. S. & Richardson, D. C. (2010). Acta Cryst. D66, 12–21.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationColletier, J. P., Sawaya, M. R., Gingery, M., Rodriguez, J. A., Cascio, D., Brewster, A. S., Michels-Clark, T., Hice, R. H., Coquelle, N., Boutet, S., Williams, G. J., Messerschmidt, M., DePonte, D. P., Sierra, R. G., Laksmono, H., Koglin, J. E., Hunter, M. S., Park, H. W., Uervirojnangkoorn, M., Bideshi, D. K., Brunger, A. T., Federici, B. A., Sauter, N. K. & Eisenberg, D. S. (2016). Nature, 539, 43–47.  Web of Science CrossRef CAS PubMed Google Scholar
First citationCoquelle, N., Sliwa, M., Woodhouse, J., Schirò, G., Adam, V., Aquila, A., Barends, T. R. M., Boutet, S., Byrdin, M., Carbajo, S., De la Mora, E., Doak, R. B., Feliks, M., Fieschi, F., Foucar, L., Guillon, V., Hilpert, M., Hunter, M. S., Jakobs, S., Koglin, J. E., Kovacsova, G., Lane, T. J., Lévy, B., Liang, M., Nass, K., Ridard, J., Robinson, J. S., Roome, C. M., Ruckebusch, C., Seaberg, M., Thepaut, M., Cammarata, M., Demachy, I., Field, M., Shoeman, R. L., Bourgeois, D., Colletier, J. P., Schlichting, I. & Weik, M. (2018). Nat. Chem. 10, 31–37.  Web of Science CrossRef CAS PubMed Google Scholar
First citationCowtan, K. (2006). Acta Cryst. D62, 1002–1011.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationDejoie, C., McCusker, L. B., Baerlocher, C., Abela, R., Patterson, B. D., Kunz, M. & Tamura, N. (2013). J. Appl. Cryst. 46, 791–794.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationDods, R., Båth, P., Morozov, D., Gagnér, V. A., Arnlund, D., Luk, H. L., Kübel, J., Maj, M., Vallejos, A., Wickstrand, C., Bosman, R., Beyerlein, K. R., Nelson, G., Liang, M., Milathianaki, D., Robinson, J., Harimoorthy, R., Berntsen, P., Malmerberg, E., Johansson, L., Andersson, R., Carbajo, S., Claesson, E., Conrad, C. E., Dahl, P., Hammarin, G., Hunter, M. S., Li, C., Lisova, S., Royant, A., Safari, C., Sharma, A., Williams, G. J., Yefanov, O., Westenhoff, S., Davidsson, J., DePonte, D. P., Boutet, S., Barty, A., Katona, G., Groenhof, G., Brändén, G. & Neutze, R. (2021). Nature, 589, 310–314.  Web of Science CrossRef CAS PubMed Google Scholar
First citationEbrahim, A., Moreno-Chicano, T., Appleby, M. V., Chaplin, A. K., Beale, J. H., Sherrell, D. A., Duyvesteyn, H. M. E., Owada, S., Tono, K., Sugimoto, H., Strange, R. W., Worrall, J. A. R., Axford, D., Owen, R. L. & Hough, M. A. (2019). IUCrJ, 6, 543–551.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationEmsley, P. & Cowtan, K. (2004). Acta Cryst. D60, 2126–2132.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationFadini, A., Reiche, S., Nass, K. & van Thor, J. J. (2020). Appl. Sci. 10, 2599.  Web of Science CrossRef Google Scholar
First citationGarman, E. F. & Weik, M. (2017). Protein Crystallography: Methods and Protocols, edited by A. Wlodawer, Z. Dauter & M. Jaskolski, pp. 467–489. New York: Springer.  Google Scholar
First citationGevorkov, Y., Barty, A., Brehm, W., White, T. A., Tolstikova, A., Wiedorn, M. O., Meents, A., Grigat, R.-R., Chapman, H. N. & Yefanov, O. (2020). Acta Cryst. A76, 121–131.  Web of Science CrossRef IUCr Journals Google Scholar
First citationGevorkov, Y., Yefanov, O., Barty, A., White, T. A., Mariani, V., Brehm, W., Tolstikova, A., Grigat, R.-R. & Chapman, H. N. (2019). Acta Cryst. A75, 694–704.  Web of Science CrossRef IUCr Journals Google Scholar
First citationGinn, H. M., Brewster, A. S., Hattne, J., Evans, G., Wagner, A., Grimes, J. M., Sauter, N. K., Sutton, G. & Stuart, D. I. (2015). Acta Cryst. D71, 1400–1410.  Web of Science CrossRef IUCr Journals Google Scholar
First citationGinn, H. M., Evans, G., Sauter, N. K. & Stuart, D. I. (2016). J. Appl. Cryst. 49, 1065–1072.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationGorel, A., Motomura, K., Fukuzawa, H., Doak, R. B., Grünbein, M. L., Hilpert, M., Inoue, I., Kloos, M., Kovácsová, G., Nango, E., Nass, K., Roome, C. M., Shoeman, R. L., Tanaka, R., Tono, K., Joti, Y., Yabashi, M., Iwata, S., Foucar, L., Ueda, K., Barends, T. R. M. & Schlichting, I. (2017). Nat. Commun. 8, 1170.  Web of Science CrossRef PubMed Google Scholar
First citationGrünbein, M. L., Stricker, M., Nass Kovacs, G., Kloos, M., Doak, R. B., Shoeman, R. L., Reinstein, J., Lecler, S., Haacke, S. & Schlichting, I. (2020). Nat. Methods, 17, 681–684.  Web of Science PubMed Google Scholar
First citationHalsted, T. P., Yamashita, K., Gopalasingam, C. C., Shenoy, R. T., Hirata, K., Ago, H., Ueno, G., Blakeley, M. P., Eady, R. R., Antonyuk, S. V., Yamamoto, M. & Hasnain, S. S. (2019). IUCrJ, 6, 761–772.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationHirata, K., Shinzawa-Itoh, K., Yano, N., Takemura, S., Kato, K., Hatanaka, M., Muramoto, K., Kawahara, T., Tsukihara, T., Yamashita, E., Tono, K., Ueno, G., Hikima, T., Murakami, H., Inubushi, Y., Yabashi, M., Ishikawa, T., Yamamoto, M., Ogura, T., Sugimoto, H., Shen, J. R., Yoshikawa, S. & Ago, H. (2014). Nat. Methods, 11, 734–736.  Web of Science CrossRef CAS PubMed Google Scholar
First citationHunter, M. S., Yoon, C. H., DeMirci, H., Sierra, R. G., Dao, E. H., Ahmadi, R., Aksit, F., Aquila, A. L., Ciftci, H., Guillet, S., Hayes, M. J., Lane, T. J., Liang, M., Lundström, U., Koglin, J. E., Mgbam, P., Rao, Y., Zhang, L., Wakatsuki, S., Holton, J. M. & Boutet, S. (2016). Nat. Commun. 7, 13388.  Web of Science CrossRef PubMed Google Scholar
First citationIm, D., Inoue, A., Fujiwara, T., Nakane, T., Yamanaka, Y., Uemura, T., Mori, C., Shiimura, Y., Kimura, K. T., Asada, H., Nomura, N., Tanaka, T., Yamashita, A., Nango, E., Tono, K., Kadji, F. M. N., Aoki, J., Iwata, S. & Shimamura, T. (2020). Nat. Commun. 11, 6442.  Web of Science CrossRef PubMed Google Scholar
First citationJohansson, L. C., Stauch, B., McCorvy, J. D., Han, G. W., Patel, N., Huang, X. P., Batyuk, A., Gati, C., Slocum, S. T., Li, C., Grandner, J. M., Hao, S., Olsen, R. H. J., Tribo, A. R., Zaare, S., Zhu, L., Zatsepin, N. A., Weierstall, U., Yous, S., Stevens, R. C., Liu, W., Roth, B. L., Katritch, V. & Cherezov, V. (2019). Nature, 569, 289–292.  Web of Science CrossRef CAS PubMed Google Scholar
First citationJuranić, P., Rehanek, J., Arrell, C. A., Pradervand, C., Ischebeck, R., Erny, C., Heimgartner, P., Gorgisyan, I., Thominet, V., Tiedtke, K., Sorokin, A., Follath, R., Makita, M., Seniutinas, G., David, C., Milne, C. J., Lemke, H., Radovic, M., Hauri, C. P. & Patthey, L. (2018). J. Synchrotron Rad. 25, 1238–1248.  Web of Science CrossRef IUCr Journals Google Scholar
First citationKabsch, W. (2010). Acta Cryst. D66, 125–132.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationKabsch, W. (2014). Acta Cryst. D70, 2204–2216.  Web of Science CrossRef IUCr Journals Google Scholar
First citationKang, Y., Zhou, X. E., Gao, X., He, Y., Liu, W., Ishchenko, A., Barty, A., White, T. A., Yefanov, O., Han, G. W., Xu, Q., de Waal, P. W., Ke, J., Tan, M. H., Zhang, C., Moeller, A., West, G. M., Pascal, B. D., Van Eps, N., Caro, L. N., Vishnivetskiy, S. A., Lee, R. J., Suino-Powell, K. M., Gu, X., Pal, K., Ma, J., Zhi, X., Boutet, S., Williams, G. J., Messerschmidt, M., Gati, C., Zatsepin, N. A., Wang, D., James, D., Basu, S., Roy-Chowdhury, S., Conrad, C. E., Coe, J., Liu, H., Lisova, S., Kupitz, C., Grotjohann, I., Fromme, R., Jiang, Y., Tan, M., Yang, H., Li, J., Wang, M., Zheng, Z., Li, D., Howe, N., Zhao, Y., Standfuss, J., Diederichs, K., Dong, Y., Potter, C. S., Carragher, B., Caffrey, M., Jiang, H., Chapman, H. N., Spence, J. C., Fromme, P., Weierstall, U., Ernst, O. P., Katritch, V., Gurevich, V. V., Griffin, P. R., Hubbell, W. L., Stevens, R. C., Cherezov, V., Melcher, K. & Xu, H. E. (2015). Nature, 523, 561–567.  Web of Science CrossRef CAS PubMed Google Scholar
First citationKirian, R. A., White, T. A., Holton, J. M., Chapman, H. N., Fromme, P., Barty, A., Lomb, L., Aquila, A., Maia, F. R. N. C., Martin, A. V., Fromme, R., Wang, X., Hunter, M. S., Schmidt, K. E. & Spence, J. C. H. (2011). Acta Cryst. A67, 131–140.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationLanger, G., Cohen, S. X., Lamzin, V. S. & Perrakis, A. (2008). Nat. Protoc. 3, 1171–1179.  Web of Science CrossRef PubMed CAS Google Scholar
First citationLee, M.-Y., Geiger, J., Ishchenko, A., Han, G. W., Barty, A., White, T. A., Gati, C., Batyuk, A., Hunter, M. S., Aquila, A., Boutet, S., Weierstall, U., Cherezov, V. & Liu, W. (2020). IUCrJ, 7, 976–984.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationLiu, W., Wacker, D., Gati, C., Han, G. W., James, D., Wang, D., Nelson, G., Weierstall, U., Katritch, V., Barty, A., Zatsepin, N. A., Li, D., Messerschmidt, M., Boutet, S., Williams, G. J., Koglin, J. E., Seibert, M. M., Wang, C., Shah, S. T., Basu, S., Fromme, R., Kupitz, C., Rendek, K. N., Grotjohann, I., Fromme, P., Kirian, R. A., Beyerlein, K. R., White, T. A., Chapman, H. N., Caffrey, M., Spence, J. C., Stevens, R. C. & Cherezov, V. (2013). Science, 342, 1521–1524.  Web of Science CrossRef CAS PubMed Google Scholar
First citationLomb, L., Barends, T. R., Kassemeyer, S., Aquila, A., Epp, S. W., Erk, B., Foucar, L., Hartmann, R., Rudek, B., Rolles, D., Rudenko, A., Shoeman, R. L., Andreasson, J., Bajt, S., Barthelmess, M., Barty, A., Bogan, M. J., Bostedt, C., Bozek, J. D., Caleman, C., Coffee, R., Coppola, N., DePonte, D. P., Doak, R. B., Ekeberg, T., Fleckenstein, H., Fromme, P., Gebhardt, M., Graafsma, H., Gumprecht, L., Hampton, C. Y., Hartmann, A., Hauser, G., Hirsemann, H., Holl, P., Holton, J. M., Hunter, M. S., Kabsch, W., Kimmel, N., Kirian, R. A., Liang, M., Maia, F. R., Meinhart, A., Marchesini, S., Martin, A. V., Nass, K., Reich, C., Schulz, J., Seibert, M. M., Sierra, R., Soltau, H., Spence, J. C., Steinbrener, J., Stellato, F., Stern, S., Timneanu, N., Wang, X., Weidenspointner, G., Weierstall, U., White, T. A., Wunderer, C., Chapman, H. N., Ullrich, J., Strüder, L. & Schlichting, I. (2011). Phys. Rev. B, 84, 214111.  Web of Science CrossRef Google Scholar
First citationMaia, F. R. (2012). Nat. Methods, 9, 854–855.  Web of Science CrossRef CAS PubMed Google Scholar
First citationMartin-Garcia, J. M., Conrad, C. E., Coe, J., Roy-Chowdhury, S. & Fromme, P. (2016). Arch. Biochem. Biophys. 602, 32–47.  Web of Science CAS PubMed Google Scholar
First citationMartin-Garcia, J. M., Zhu, L., Mendez, D., Lee, M.-Y., Chun, E., Li, C., Hu, H., Subramanian, G., Kissick, D., Ogata, C., Henning, R., Ishchenko, A., Dobson, Z., Zhang, S., Weierstall, U., Spence, J. C. H., Fromme, P., Zatsepin, N. A., Fischetti, R. F., Cherezov, V. & Liu, W. (2019). IUCrJ, 6, 412–425.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationMeents, A., Wiedorn, M. O., Srajer, V., Henning, R., Sarrou, I., Bergtholdt, J., Barthelmess, M., Reinke, P. Y. A., Dierksmeyer, D., Tolstikova, A., Schaible, S., Messerschmidt, M., Ogata, C. M., Kissick, D. J., Taft, M. H., Manstein, D. J., Lieske, J., Oberthuer, D., Fischetti, R. F. & Chapman, H. N. (2017). Nat. Commun. 8, 1281.  Web of Science CrossRef PubMed Google Scholar
First citationMendez, D., Bolotovsky, R., Bhowmick, A., Brewster, A. S., Kern, J., Yano, J., Holton, J. M. & Sauter, N. K. (2020). IUCrJ, 7, 1151–1167.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationMilne, C., Schietinger, T., Aiba, M., Alarcon, A., Alex, J., Anghel, A., Arsov, V., Beard, C., Beaud, P., Bettoni, S., Bopp, M., Brands, H., Brönnimann, M., Brunnenkant, I., Calvi, M., Citterio, A., Craievich, P., Csatari Divall, M., Dällenbach, M., D'Amico, M., Dax, A., Deng, Y., Dietrich, A., Dinapoli, R., Divall, E., Dordevic, S., Ebner, S., Erny, C., Fitze, H., Flechsig, U., Follath, R., Frei, F., Gärtner, F., Ganter, R., Garvey, T., Geng, Z., Gorgisyan, I., Gough, C., Hauff, A., Hauri, C., Hiller, N., Humar, T., Hunziker, S., Ingold, G., Ischebeck, R., Janousch, M., Juranić, P., Jurcevic, M., Kaiser, M., Kalantari, B., Kalt, R., Keil, B., Kittel, C., Knopp, G., Koprek, W., Lemke, H., Lippuner, T., Llorente Sancho, D., Löhl, F., Lopez-Cuenca, C., Märki, F., Marcellini, F., Marinkovic, G., Martiel, I., Menzel, R., Mozzanica, A., Nass, K., Orlandi, G., Ozkan Loch, C., Panepucci, E., Paraliev, M., Patterson, B., Pedrini, B., Pedrozzi, M., Pollet, P., Pradervand, C., Prat, E., Radi, P., Raguin, J.-Y., Redford, S., Rehanek, J., Réhault, J., Reiche, S., Ringele, M., Rittmann, J., Rivkin, L., Romann, A., Ruat, M., Ruder, C., Sala, L., Schebacher, L., Schilcher, T., Schlott, V., Schmidt, T., Schmitt, B., Shi, X., Stadler, M., Stingelin, L., Sturzenegger, W., Szlachetko, J., Thattil, D., Treyer, D., Trisorio, A., Tron, W., Vetter, S., Vicario, C., Voulot, D., Wang, M., Zamofing, T., Zellweger, C., Zennaro, R., Zimoch, E., Abela, R., Patthey, L. & Braun, H.-H. (2017). Appl. Sci. 7, 720.  Web of Science CrossRef Google Scholar
First citationMora, E. de la, Coquelle, N., Bury, C. S., Rosenthal, M., Holton, J. M., Carmichael, I., Garman, E. F., Burghammer, M., Colletier, J. P. & Weik, M. (2020). Proc. Natl Acad. Sci. USA, 117, 4142–4151.  Web of Science PubMed Google Scholar
First citationMozzanica, A., Bergamaschi, A., Brueckner, M., Cartier, S., Dinapoli, R., Greiffenberg, D., Jungmann-Smith, J., Maliakal, D., Mezza, D., Ramilli, M., Ruder, C., Schaedler, L., Schmitt, B., Shi, X. & Tinti, G. (2016). J. Instrum. 11, C02047–C02047.  Web of Science CrossRef Google Scholar
First citationNakane, T., Hanashima, S., Suzuki, M., Saiki, H., Hayashi, T., Kakinouchi, K., Sugiyama, S., Kawatake, S., Matsuoka, S., Matsumori, N., Nango, E., Kobayashi, J., Shimamura, T., Kimura, K., Mori, C., Kunishima, N., Sugahara, M., Takakyu, Y., Inoue, S., Masuda, T., Hosaka, T., Tono, K., Joti, Y., Kameshima, T., Hatsui, T., Yabashi, M., Inoue, T., Nureki, O., Iwata, S., Murata, M. & Mizohata, E. (2016). Proc. Natl Acad. Sci. USA, 113, 13039–13044.  Web of Science CrossRef CAS PubMed Google Scholar
First citationNakane, T., Song, C., Suzuki, M., Nango, E., Kobayashi, J., Masuda, T., Inoue, S., Mizohata, E., Nakatsu, T., Tanaka, T., Tanaka, R., Shimamura, T., Tono, K., Joti, Y., Kameshima, T., Hatsui, T., Yabashi, M., Nureki, O., Iwata, S. & Sugahara, M. (2015). Acta Cryst. D71, 2519–2525.  Web of Science CrossRef IUCr Journals Google Scholar
First citationNango, E., Royant, A., Kubo, M., Nakane, T., Wickstrand, C., Kimura, T., Tanaka, T., Tono, K., Song, C., Tanaka, R., Arima, T., Yamashita, A., Kobayashi, J., Hosaka, T., Mizohata, E., Nogly, P., Sugahara, M., Nam, D., Nomura, T., Shimamura, T., Im, D., Fujiwara, T., Yamanaka, Y., Jeon, B., Nishizawa, T., Oda, K., Fukuda, M., Andersson, R., Båth, P., Dods, R., Davidsson, J., Matsuoka, S., Kawatake, S., Murata, M., Nureki, O., Owada, S., Kameshima, T., Hatsui, T., Joti, Y., Schertler, G., Yabashi, M., Bondar, A. N., Standfuss, J., Neutze, R. & Iwata, S. (2016). Science, 354, 1552–1557.  Web of Science CrossRef CAS PubMed Google Scholar
First citationNass, K. (2019). Acta Cryst. D75, 211–218.  Web of Science CrossRef IUCr Journals Google Scholar
First citationNass, K., Cheng, R., Vera, L., Mozzanica, A., Redford, S., Ozerov, D., Basu, S., James, D., Knopp, G., Cirelli, C., Martiel, I., Casadei, C., Weinert, T., Nogly, P., Skopintsev, P., Usov, I., Leonarski, F., Geng, T., Rappas, M., Doré, A. S., Cooke, R., Nasrollahi Shirazi, S., Dworkowski, F., Sharpe, M., Olieric, N., Bacellar, C., Bohinc, R., Steinmetz, M. O., Schertler, G., Abela, R., Patthey, L., Schmitt, B., Hennig, M., Standfuss, J., Wang, M. & Milne, C. J. (2020). IUCrJ, 7, 965–975.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationNass, K., Gorel, A., Abdullah, M. M. V., Martin, A., Kloos, M., Marinelli, A., Aquila, A., Barends, T. R. M., Decker, F. J., Bruce Doak, R., Foucar, L., Hartmann, E., Hilpert, M., Hunter, M. S., Jurek, Z., Koglin, J. E., Kozlov, A., Lutman, A. A., Kovacs, G. N., Roome, C. M., Shoeman, R. L., Santra, R., Quiney, H. M., Ziaja, B., Boutet, S. & Schlichting, I. (2020). Nat. Commun. 11, 1814.  Web of Science CrossRef PubMed Google Scholar
First citationNass, K., Meinhart, A., Barends, T. R. M., Foucar, L., Gorel, A., Aquila, A., Botha, S., Doak, R. B., Koglin, J., Liang, M., Shoeman, R. L., Williams, G., Boutet, S. & Schlichting, I. (2016). IUCrJ, 3, 180–191.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationNass, K., Redecke, L., Perbandt, M., Yefanov, O., Klinge, M., Koopmann, R., Stellato, F., Gabdulkhakov, A., Schönherr, R., Rehders, D., Lahey-Rudolph, J. M., Aquila, A., Barty, A., Basu, S., Doak, R. B., Duden, R., Frank, M., Fromme, R., Kassemeyer, S., Katona, G., Kirian, R., Liu, H., Majoul, I., Martin-Garcia, J. M., Messerschmidt, M., Shoeman, R. L., Weierstall, U., Westenhoff, S., White, T. A., Williams, G. J., Yoon, C. H., Zatsepin, N., Fromme, P., Duszenko, M., Chapman, H. N. & Betzel, C. (2020). Nat. Commun. 11, 620.  Web of Science CrossRef PubMed Google Scholar
First citationNass Kovacs, G., Colletier, J. P., Grünbein, M. L., Yang, Y., Stensitzki, T., Batyuk, A., Carbajo, S., Doak, R. B., Ehrenberg, D., Foucar, L., Gasper, R., Gorel, A., Hilpert, M., Kloos, M., Koglin, J. E., Reinstein, J., Roome, C. M., Schlesinger, R., Seaberg, M., Shoeman, R. L., Stricker, M., Boutet, S., Haacke, S., Heberle, J., Heyne, K., Domratcheva, T., Barends, T. R. M. & Schlichting, I. (2019). Nat. Commun. 10, 3177.  Web of Science CrossRef PubMed Google Scholar
First citationNogly, P., Weinert, T., James, D., Carbajo, S., Ozerov, D., Furrer, A., Gashi, D., Borin, V., Skopintsev, P., Jaeger, K., Nass, K., Bath, P., Bosman, R., Koglin, J., Seaberg, M., Lane, T., Kekilli, D., Brunle, S., Tanaka, T., Wu, W., Milne, C., White, T., Barty, A., Weierstall, U., Panneels, V., Nango, E., Iwata, S., Hunter, M., Schapiro, I., Schertler, G., Neutze, R. & Standfuss, J. (2018). Science, 361, eaat0094.  Web of Science CrossRef PubMed Google Scholar
First citationOrville, A. M. (2020). Curr. Opin. Struct. Biol. 65, 193–208.  Web of Science CrossRef CAS PubMed Google Scholar
First citationPande, K., Hutchison, C. D., Groenhof, G., Aquila, A., Robinson, J. S., Tenboer, J., Basu, S., Boutet, S., DePonte, D. P., Liang, M., White, T. A., Zatsepin, N. A., Yefanov, O., Morozov, D., Oberthuer, D., Gati, C., Subramanian, G., James, D., Zhao, Y., Koralek, J., Brayshaw, J., Kupitz, C., Conrad, C., Roy-Chowdhury, S., Coe, J. D., Metz, M., Xavier, P. L., Grant, T. D., Koglin, J. E., Ketawala, G., Fromme, R., rajer, V., Henning, R., Spence, J. C., Ourmazd, A., Schwander, P., Weierstall, U., Frank, M., Fromme, P., Barty, A., Chapman, H. N., Moffat, K., van Thor, J. J. & Schmidt, M. (2016). Science, 352, 725–729.  Web of Science CrossRef CAS PubMed Google Scholar
First citationPrat, E., Abela, R., Aiba, M., Alarcon, A., Alex, J., Arbelo, Y., Arrell, C., Arsov, V., Bacellar, C., Beard, C., Beaud, P., Bettoni, S., Biffiger, R., Bopp, M., Braun, H.-H., Calvi, M., Cassar, A., Celcer, T., Chergui, M., Chevtsov, P., Cirelli, C., Citterio, A., Craievich, P., Divall, M. C., Dax, A., Dehler, M., Deng, Y., Dietrich, A., Dijkstal, P., Dinapoli, R., Dordevic, S., Ebner, S., Engeler, D., Erny, C., Esposito, V., Ferrari, E., Flechsig, U., Follath, R., Frei, F., Ganter, R., Garvey, T., Geng, Z., Gobbo, A., Gough, C., Hauff, A., Hauri, C. P., Hiller, N., Hunziker, S., Huppert, M., Ingold, G., Ischebeck, R., Janousch, M., Johnson, P. J. M., Johnson, S. L., Juranić, P., Jurcevic, M., Kaiser, M., Kalt, R., Keil, B., Kiselev, D., Kittel, C., Knopp, G., Koprek, W., Laznovsky, M., Lemke, H. T., Sancho, D. L., Löhl, F., Malyzhenkov, A., Mancini, G. F., Mankowsky, R., Marcellini, F., Marinkovic, G., Martiel, I., Märki, F., Milne, C. J., Mozzanica, A., Nass, K., Orlandi, G. L., Loch, C. O., Paraliev, M., Patterson, B., Patthey, L., Pedrini, B., Pedrozzi, M., Pradervand, C., Radi, P., Raguin, J.-Y., Redford, S., Rehanek, J., Reiche, S., Rivkin, L., Romann, A., Sala, L., Sander, M., Schietinger, T., Schilcher, T., Schlott, V., Schmidt, T., Seidel, M., Stadler, M., Stingelin, L., Svetina, C., Treyer, D. M., Trisorio, A., Vicario, C., Voulot, D., Wrulich, A., Zerdane, S. & Zimoch, E. (2020). Nat. Photonics, 14, 748–754.  Web of Science CrossRef CAS Google Scholar
First citationPrat, E., Dijkstal, P., Ferrari, E. & Reiche, S. (2020). Phys. Rev. Lett. 124, 074801.  Web of Science CrossRef PubMed Google Scholar
First citationRedecke, L., Nass, K., DePonte, D. P., White, T. A., Rehders, D., Barty, A., Stellato, F., Liang, M., Barends, T. R. M., Boutet, S., Williams, G. J., Messerschmidt, M., Seibert, M. M., Aquila, A., Arnlund, D., Bajt, S., Barth, T., Bogan, M. J., Caleman, C., Chao, T. C., Doak, R. B., Fleckenstein, H., Frank, M., Fromme, R., Galli, L., Grotjohann, I., Hunter, M. S., Johansson, L. C., Kassemeyer, S., Katona, G., Kirian, R. A., Koopmann, R., Kupitz, C., Lomb, L., Martin, A. V., Mogk, S., Neutze, R., Shoeman, R. L., Steinbrener, J., Timneanu, N., Wang, D., Weierstall, U., Zatsepin, N. A., Spence, J. C. H., Fromme, P., Schlichting, I., Duszenko, M., Betzel, C. & Chapman, H. N. (2013). Science, 339, 227–230.  Web of Science CrossRef CAS PubMed Google Scholar
First citationRen, Z. & Moffat, K. (1994). J. Synchrotron Rad. 1, 78–82.  CrossRef CAS Web of Science IUCr Journals Google Scholar
First citationRoedig, P., Duman, R., Sanchez-Weatherby, J., Vartiainen, I., Burkhardt, A., Warmer, M., David, C., Wagner, A. & Meents, A. (2016). J. Appl. Cryst. 49, 968–975.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationRose, S. L., Antonyuk, S. V., Sasaki, D., Yamashita, K., Hirata, K., Ueno, G., Ago, H., Eady, R. R., Tosha, T., Yamamoto, M. & Hasnain, S. S. (2021). Sci. Adv. 7, eabd8523.  Web of Science CrossRef PubMed Google Scholar
First citationSaa Hernandez, A., Prat, E., Bettoni, S., Beutner, B. & Reiche, S. (2016). Phys. Rev. Accel. Beams, 19, 090702.  Web of Science CrossRef Google Scholar
First citationSauter, N. K. (2015). J. Synchrotron Rad. 22, 239–248.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationSawaya, M. R., Cascio, D., Gingery, M., Rodriguez, J., Goldschmidt, L., Colletier, J. P., Messerschmidt, M. M., Boutet, S., Koglin, J. E., Williams, G. J., Brewster, A. S., Nass, K., Hattne, J., Botha, S., Doak, R. B., Shoeman, R. L., DePonte, D. P., Park, H. W., Federici, B. A., Sauter, N. K., Schlichting, I. & Eisenberg, D. S. (2014). Proc. Natl Acad. Sci. USA, 111, 12769–12774.  Web of Science CrossRef CAS PubMed Google Scholar
First citationSchneider, T. R. & Sheldrick, G. M. (2002). Acta Cryst. D58, 1772–1779.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationShrive, A. K., Clifton, I. J., Hajdu, J. & Greenhough, T. J. (1990). J. Appl. Cryst. 23, 169–174.  CrossRef CAS Web of Science IUCr Journals Google Scholar
First citationSkopintsev, P., Ehrenberg, D., Weinert, T., James, D., Kar, R. K., Johnson, P. J. M., Ozerov, D., Furrer, A., Martiel, I., Dworkowski, F., Nass, K., Knopp, G., Cirelli, C., Arrell, C., Gashi, D., Mous, S., Wranik, M., Gruhl, T., Kekilli, D., Brünle, S., Deupi, X., Schertler, G. F. X., Benoit, R. M., Panneels, V., Nogly, P., Schapiro, I., Milne, C., Heberle, J. & Standfuss, J. (2020). Nature, 583, 314–318.  Web of Science CrossRef CAS PubMed Google Scholar
First citationSon, S. K., Chapman, H. N. & Santra, R. (2011). Phys. Rev. Lett. 107, 218102.  Web of Science CrossRef PubMed Google Scholar
First citationSorigué, D., Hadjidemetriou, K., Blangy, S., Gotthard, G., Bonvalet, A., Coquelle, N., Samire, P., Aleksandrov, A., Antonucci, L., Benachir, A., Boutet, S., Byrdin, M., Cammarata, M., Carbajo, S., Cuine, S., Doak, R. B., Foucar, L., Gorel, A., Grünbein, M., Hartmann, E., Hienerwadel, R., Hilpert, M., Kloos, M., Lane, T. J., Legeret, B., Legrand, P., Li-Beisson, Y., Moulin, S. L. Y., Nurizzo, D., Peltier, G., Schiro, G., Shoeman, R. L., Sliwa, M., Solinas, X., Zhuang, B., Barends, T. R. M., Colletier, J. P., Joffre, M., Royant, A., Berthomieu, C., Weik, M., Domratcheva, T., Brettel, K., Vos, M. H., Schlichting, I., Arnoux, P., Muller, P. & Beisson, F. (2021). Science, 372, eabd5687.  Web of Science PubMed Google Scholar
First citationStauch, B. & Cherezov, V. (2018). Annu. Rev. Biophys. 47, 377–397.  Web of Science CrossRef CAS PubMed Google Scholar
First citationStauch, B., Johansson, L. C., McCorvy, J. D., Patel, N., Han, G. W., Huang, X. P., Gati, C., Batyuk, A., Slocum, S. T., Ishchenko, A., Brehm, W., White, T. A., Michaelian, N., Madsen, C., Zhu, L., Grant, T. D., Grandner, J. M., Shiriaeva, A., Olsen, R. H. J., Tribo, A. R., Yous, S., Stevens, R. C., Weierstall, U., Katritch, V., Roth, B. L., Liu, W. & Cherezov, V. (2019). Nature, 569, 284–288.  Web of Science CrossRef CAS PubMed Google Scholar
First citationSuga, M., Akita, F., Hirata, K., Ueno, G., Murakami, H., Nakajima, Y., Shimizu, T., Yamashita, K., Yamamoto, M., Ago, H. & Shen, J. R. (2015). Nature, 517, 99–103.  Web of Science CrossRef CAS PubMed Google Scholar
First citationSugahara, M., Nakane, T., Masuda, T., Suzuki, M., Inoue, S., Song, C., Tanaka, R., Nakatsu, T., Mizohata, E., Yumoto, F., Tono, K., Joti, Y., Kameshima, T., Hatsui, T., Yabashi, M., Nureki, O., Numata, K., Nango, E. & Iwata, S. (2017). Sci. Rep. 7, 703.  Web of Science CrossRef PubMed Google Scholar
First citationTolstikova, A., Levantino, M., Yefanov, O., Hennicke, V., Fischer, P., Meyer, J., Mozzanica, A., Redford, S., Crosas, E., Opara, N. L., Barthelmess, M., Lieske, J., Oberthuer, D., Wator, E., Mohacsi, I., Wulff, M., Schmitt, B., Chapman, H. N. & Meents, A. (2019). IUCrJ, 6, 927–937.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationVonrhein, C., Blanc, E., Roversi, P. & Bricogne, G. (2007). Methods Mol. Biol. 364, 215–230.  PubMed CAS Google Scholar
First citationWeierstall, U., James, D., Wang, C., White, T. A., Wang, D., Liu, W., Spence, J. C., Bruce Doak, R., Nelson, G., Fromme, P., Fromme, R., Grotjohann, I., Kupitz, C., Zatsepin, N. A., Liu, H., Basu, S., Wacker, D., Won Han, G., Katritch, V., Boutet, S., Messerschmidt, M., Williams, G. J., Koglin, J. E., Marvin Seibert, M., Klinker, M., Gati, C., Shoeman, R. L., Barty, A., Chapman, H. N., Kirian, R. A., Beyerlein, K. R., Stevens, R. C., Li, D., Shah, S. T., Howe, N., Caffrey, M. & Cherezov, V. (2014). Nat. Commun. 5, 3309.  Web of Science CrossRef PubMed Google Scholar
First citationWhite, T. A. (2014). Phil. Trans. R. Soc. B, 369, 20130330.  Web of Science CrossRef PubMed Google Scholar
First citationWhite, T. A., Barty, A., Stellato, F., Holton, J. M., Kirian, R. A., Zatsepin, N. A. & Chapman, H. N. (2013). Acta Cryst. D69, 1231–1240.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationWhite, T. A., Kirian, R. A., Martin, A. V., Aquila, A., Nass, K., Barty, A. & Chapman, H. N. (2012). J. Appl. Cryst. 45, 335–341.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationWhite, T. A., Mariani, V., Brehm, W., Yefanov, O., Barty, A., Beyerlein, K. R., Chervinskii, F., Galli, L., Gati, C., Nakane, T., Tolstikova, A., Yamashita, K., Yoon, C. H., Diederichs, K. & Chapman, H. N. (2016). J. Appl. Cryst. 49, 680–689.  Web of Science CrossRef CAS IUCr Journals Google Scholar
First citationWoodhouse, J., Nass Kovacs, G., Coquelle, N., Uriarte, L. M., Adam, V., Barends, T. R. M., Byrdin, M., de la Mora, E., Bruce Doak, R., Feliks, M., Field, M., Fieschi, F., Guillon, V., Jakobs, S., Joti, Y., Macheboeuf, P., Motomura, K., Nass, K., Owada, S., Roome, C. M., Ruckebusch, C., Schirò, G., Shoeman, R. L., Thepaut, M., Togashi, T., Tono, K., Yabashi, M., Cammarata, M., Foucar, L., Bourgeois, D., Sliwa, M., Colletier, J. P., Schlichting, I. & Weik, M. (2020). Nat. Commun. 11, 741.  Web of Science CrossRef PubMed Google Scholar
First citationYamashita, K., Kuwabara, N., Nakane, T., Murai, T., Mizohata, E., Sugahara, M., Pan, D., Masuda, T., Suzuki, M., Sato, T., Kodan, A., Yamaguchi, T., Nango, E., Tanaka, T., Tono, K., Joti, Y., Kameshima, T., Hatsui, T., Yabashi, M., Manya, H., Endo, T., Kato, R., Senda, T., Kato, H., Iwata, S., Ago, H., Yamamoto, M., Yumoto, F. & Nakatsu, T. (2017). IUCrJ, 4, 639–647.  Web of Science CrossRef CAS PubMed IUCr Journals Google Scholar
First citationYamashita, K., Pan, D., Okuda, T., Sugahara, M., Kodan, A., Yamaguchi, T., Murai, T., Gomi, K., Kajiyama, N., Mizohata, E., Suzuki, M., Nango, E., Tono, K., Joti, Y., Kameshima, T., Park, J., Song, C., Hatsui, T., Yabashi, M., Iwata, S., Kato, H., Ago, H., Yamamoto, M. & Nakatsu, T. (2015). Sci. Rep. 5, 14017.  Web of Science CrossRef PubMed Google Scholar
First citationYefanov, O., Mariani, V., Gati, C., White, T. A., Chapman, H. N. & Barty, A. (2015). Opt. Express, 23, 28459–28470.  Web of Science CrossRef PubMed Google Scholar
First citationYun, J. H., Li, X., Yue, J., Park, J. H., Jin, Z., Li, C., Hu, H., Shi, Y., Pandey, S., Carbajo, S., Boutet, S., Hunter, M. S., Liang, M., Sierra, R. G., Lane, T. J., Zhou, L., Weierstall, U., Zatsepin, N. A., Ohki, M., Tame, J. R. H., Park, S. Y., Spence, J. C. H., Zhang, W., Schmidt, M., Lee, W. & Liu, H. (2021). Proc. Natl Acad. Sci. USA, 118, e2020486118.  Web of Science CrossRef PubMed Google Scholar
First citationZhang, H., Unal, H., Gati, C., Han, G. W., Liu, W., Zatsepin, N. A., James, D., Wang, D., Nelson, G., Weierstall, U., Sawaya, M. R., Xu, Q., Messerschmidt, M., Williams, G. J., Boutet, S., Yefanov, O. M., White, T. A., Wang, C., Ishchenko, A., Tirupula, K. C., Desnoyer, R., Coe, J., Conrad, C. E., Fromme, P., Stevens, R. C., Katritch, V., Karnik, S. S. & Cherezov, V. (2015). Cell, 161, 833–844.  Web of Science CrossRef CAS PubMed Google Scholar

This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.

IUCrJ
Volume 8| Part 6| November 2021| Pages 905-920
ISSN: 2052-2525