1. Basu, A. et al. Dynamic coupling between conformations and nucleotide states in DNA gyrase. Nat. Chem. Biol. 14, 565–574.
https://doi.org/10.1038/s41589-018-0037-0 (2018).
2. Mills, M., Tse-Dinh, Y. C. & Neuman, K. C. Direct observation of topoisomerase IA gate dynamics. Nat. Struct. Mol. Biol. 25,
1111–1118. https://doi.org/10.1038/s41594-018-0158-x (2018).
3. Kozlov, G., Määttänen, P., Thomas, D. Y. & Gehring, K. A structural overview of the PDI family of proteins. FEBS J. 277, 3924–3936.
https://doi.org/10.1111/j.1742-4658.2010.07793.x (2010).
4. Okuda, A. et al. Solution structure of multi-domain protein ER-60 studied by aggregation-free SAXS and coarse-grained-MD
simulation. Sci. Rep. 11, 5655. https://doi.org/10.1038/s41598-021-85219-0 (2021).
5. Bernadó, P., Shimizu, N., Zaccai, G., Kamikubo, H. & Sugiyama, M. Solution scattering approaches to dynamical ordering in
biomolecular systems. Biochim. Biophys. Acta Gen. Subj. 1862, 253–274. https://doi.org/10.1016/j.bbagen.2017.10.015 (2018).
6. Borges, J. C., Seraphim, T. V., Dores-Silva, P. R. & Barbosa, L. R. S. A review of multi-domain and flexible molecular chaperones
studies by small-angle X-ray scattering. Biophys. Rev. 8, 107–120. https://doi.org/10.1007/s12551-016-0194-x (2016).
7. Hura, G. L. et al. Robust, high-throughput solution structural analyses by small angle X-ray scattering (SAXS). Nat. Methods 6,
606–612. https://doi.org/10.1038/nmeth.1353 (2009).
8. Murayama, Y. et al. Tracking and visualizing the circadian ticking of the cyanobacterial clock protein KaiC in solution. EMBO J.
30, 68–78. https://doi.org/10.1038/emboj.2010.298 (2011).
9. Lattman, E. E., Grant, T. D. & Snell, E. H. Biological Small Angle Scattering: Theory and Practice 19 (Oxford University, 2013).
10. Bonomi, M., Heller, G. T., Camilloni, C. & Vendruscolo, M. Principles of protein structural ensemble determination. Curr. Opin.
Struct. Biol. 42, 106–116. https://doi.org/10.1016/j.sbi.2016.12.004 (2017).
11. Bowerman, S. et al. Determining atomistic SAXS models of tri-ubiquitin chains from Bayesian analysis of accelerated molecular
dynamics simulations. J. Chem. Theory Comput. 13, 2418–2429. https://doi.org/10.1021/acs.jctc.7b00059 (2017).
12. Bowerman, S., Curtis, J. E., Clayton, J., Brookes, E. H. & Wereszczynski, J. BEES: Bayesian ensemble estimation from SAS. Biophys.
J. 117, 399–407. https://doi.org/10.1016/j.bpj.2019.06.024 (2019).
13. Bernadó, P., Mylonas, E., Petoukhov, M. V., Blackledge, M. & Svergun, D. I. Structural characterization of flexible proteins using
small-angle X-ray scattering. J. Am. Chem. Soc. 129, 5656–5664. https://doi.org/10.1021/ja069124n (2007).
14. Pelikan, M., Hura, G. L. & Hammel, M. Structure and flexibility within proteins as identified through small angle X-ray scattering.
Gen. Physiol. Biophys. 28, 174–189. https://doi.org/10.4149/gpb_2009_02_174 (2009).
15. Tria, G., Mertens, H. D. T., Kachala, M. & Svergun, D. I. Advanced ensemble modeling of flexible macromolecules using X-ray
solution scattering. IUCrJ 2, 207–217. https://doi.org/10.1107/S205225251500202X (2015).
16. Berlin, K. et al. Recovering a representative conformational ensemble from underdetermined macromolecular structural data. J.
Am. Chem. Soc. 135, 16595–16609. https://doi.org/10.1021/ja4083717 (2013).
17. Bertini, I. et al. Conformational space of flexible biological macromolecules from average data. J. Am. Chem. Soc. 132, 13553–13558.
https://doi.org/10.1021/ja1063923 (2010).
18. Różycki, B., Kim, Y. C. & Hummer, G. SAXS ensemble refinement of ESCRT-III CHMP3 conformational transitions. Structure
19, 109–116. https://doi.org/10.1016/j.str.2010.10.006 (2011).
19. Larsen, A. H. et al. Combining molecular dynamics simulations with small-angle X-ray and neutron scattering data to study
multi-domain proteins in solution. PLoS Comput. Biol. 16, e1007870. https://doi.org/10.1371/journal.pcbi.1007870 (2020).
20. Kassem, N. et al. Order and disorder: an integrative structure of the full-length human growth hormone receptor. Sci. Adv. 7,
eabh3805. https://doi.org/10.1126/sciadv.abh3805 (2021).
21. Ahmed, M. C. et al. Refinement of α-synuclein ensembles against SAXS data: comparison of force fields and methods. Front. Mol.
Biosci. 8, 654333. https://doi.org/10.3389/fmolb.2021.654333 (2021).
22. Antonov, L. D., Olsson, S., Boomsma, W. & Hamelryck, T. Bayesian inference of protein ensembles from SAXS data. Phys. Chem.
Chem. Phys. 18, 5832–5838. https://doi.org/10.1039/c5cp04886a (2016).
23. Hermann, M. R. & Hub, J. S. SAXS-restrained ensemble simulations of intrinsically disordered proteins with commitment to the
principle of maximum entropy. J. Chem. Theory Comput. 15, 5103–5115. https://doi.org/10.1021/acs.jctc.9b00338 (2019).
24. Ivanović, M. T., Hermann, M. R., Wójcik, M., Pérez, J. & Hub, J. S. Small-angle X-ray scattering curves of detergent micelles: effects
of asymmetry, shape fluctuations, disorder, and atomic details. J. Phys. Chem. Lett. 11, 945–951. https://doi.org/10.1021/acs.jpcle
tt.9b03154 (2020).
25. Paissoni, C., Jussupow, A. & Camilloni, C. Determination of protein structural ensembles by hybrid-resolution SAXS restrained
molecular dynamics. J. Chem. Theory Comput. 16, 2825–2834. https://doi.org/10.1021/acs.jctc.9b01181 (2020).
26. Jussupow, A. et al. The dynamics of linear polyubiquitin. Sci. Adv. 6, eabc3786. https://doi.org/10.1126/sciadv.abc3786 (2020).
27. Shevchuk, R. & Hub, J. S. Bayesian refinement of protein structures and ensembles against SAXS data using molecular dynamics.
PLoS Comput. Biol. 13, e1005800. https://doi.org/10.1371/journal.pcbi.1005800 (2017).
28. Tanaka, T., Hori, N. & Takada, S. How co-translational folding of multi-domain protein is affected by elongation schedule: molecular simulations. PLoS Comput. Biol. 11, e1004356. https://doi.org/10.1371/journal.pcbi.1004356 (2015).
29. Terakawa, T., Kenzaki, H. & Takada, S. p53 searches on DNA by rotation-uncoupled sliding at C-terminal tails and restricted
hopping of core domains. J. Am. Chem. Soc. 134, 14555–14562. https://doi.org/10.1021/ja305369u (2012).
30. Monticelli, L. et al. The MARTINI coarse-grained force field: extension to proteins. J. Chem. Theory Comput. 4, 819–834. https://
doi.org/10.1021/ct700324x (2008).
31. Shimizu, M. et al. Near-atomic structural model for bacterial DNA replication initiation complex and its functional insights. Proc.
Natl. Acad. Sci. USA 113, E8021–E8030. https://doi.org/10.1073/pnas.1609649113 (2016).
32. Niina, T., Brandani, G. B., Tan, C. & Takada, S. Sequence-dependent nucleosome sliding in rotation-coupled and uncoupled modes
revealed by molecular simulations. PLoS Comput. Biol. 13, e1005880. https://doi.org/10.1371/journal.pcbi.1005880 (2017).
33. Dong, G., Wearsch, P. A., Peaper, D. R., Cresswell, P. & Reinisch, K. M. Insights into MHC class I peptide loading from the structure of the tapasin-ERp57 thiol oxidoreductase heterodimer. Immunity 30, 21–32. https://doi.org/10.1016/j.immuni.2008.10.018
(2009).
34. Abraham, M. J. et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001 (2015).
35. Bondi, A. A. van der Waals Volumes and Radii. J. Phys. Chem. 68, 441–451. https://doi.org/10.1021/j100785a001 (1964).
36. Grudinin, S., Garkavenko, M. & Kazennov, A. Pepsi-SAXS: an adaptive method for rapid and accurate computation of small-angle
X-ray scattering profiles. Acta Crystallogr. D Struct. Biol. 73, 449–464. https://doi.org/10.1107/S2059798317005745 (2017).
Scientific Reports |
Vol:.(1234567890)
(2022) 12:9970 |
https://doi.org/10.1038/s41598-022-13982-9
12
www.nature.com/scientificreports/
A Self-archived copy in
Kyoto University Research Information Repository
https://repository.kulib.kyoto-u.ac.jp
37. Wassenaar, T. A., Pluhackova, K., Böckmann, R. A., Marrink, S. J. & Tieleman, D. P. Going backward: a flexible geometric approach
to reverse transformation from coarse grained to atomistic models. J. Chem. Theory Comput. 10, 676–690. https://d
oi.o
rg/1 0.1 021/
ct400617g (2014).
38. Mistry, J. et al. Pfam: The protein families database in 2021. Nucl. Acids Res. 49, D412–D419. https://doi.org/10.1093/nar/gkaa9
13 (2020).
39. The PyMOL Molecular Graphics System, Version 1.8, Schrödinger, LLC.
40. Greger, M., Kollar, M. & Vollhardt, D. Isosbestic points: how a narrow crossing region of curves determines their leading parameter
dependence. Phys. Rev. B 87, 195140. https://doi.org/10.1103/PhysRevB.87.195140 (2013).
41. Svergun, D. I. et al. Protein hydration in solution: experimental observation by X-ray and neutron scattering. Proc. Natl. Acad. Sci.
USA 95, 2267–2272. https://doi.org/10.1073/pnas.95.5.2267 (1998).
42. Grudinin, S. Pepsi-SANS. https://team.inria.fr/nano-d/software/pepsi-sans/.
43. Yunoki, Y. et al. Overall structure of fully assembled cyanobacterial KaiABC circadian clock complex by an integrated experimentalcomputational approach. Commun. Biol. 5, 184. https://doi.org/10.1038/s42003-022-03143-z (2022).
44. Matsumoto, A. et al. Structural studies of overlapping dinucleosomes in solution. Biophys. J. 118, 2209–2219. https://doi.org/10.
1016/j.bpj.2019.12.010 (2020).
45. Han, B., Liu, Y., Ginzinger, S. W. & Wishart, D. S. SHIFTX2: significantly improved protein chemical shift prediction. J. Biomol.
NMR 50, 43–57. https://doi.org/10.1007/s10858-011-9478-4 (2011).
46. Shen, Y. & Bax, A. SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural
network. J. Biomol. NMR 48, 13–22. https://doi.org/10.1007/s10858-010-9433-9 (2010).
47. Okamoto, K. & Sako, Y. Recent advances in FRET for the study of protein interactions and dynamics. Curr. Opin. Struct. Biol. 46,
16–23. https://doi.org/10.1016/j.sbi.2017.03.010 (2017).
Acknowledgements
This work was supported by MEXT/JSPS KAKENHI Grant Numbers (JP20K22629 to M. Shimizu; JP19K16088
and 21K15051 to K. M.; JP19KK0071, and JP20K06579 to R. I.; JP17K07816 to N. S.; JP18H05229 and
JP18H05534 to M. Sugiyama), and the Sasakawa Scientific Research Grant from The Japan Science Society
assigned to A. O. The study was also partially supported by a project for the construction of the basis for advanced
materials science and analytical study by the innovative use of quantum beams and nuclear sciences at the
Institute for Integrated Radiation and Nuclear Science, Kyoto University (KURNS) and a grants for research
promotion in KURNS to M. Shimizu and Y. Y. The study was partially supported by the Platform Project for
Supporting Drug Discovery and Life Science Research (Basis for Supporting Innovative Drug Discovery and
Life Science Research (BINDS)) from AMED (JP22ama121001j0001) to M. Sugiyama.
Author contributions
M.Sh. and M.Su. designed the modeling method. M.Sh. performed MD simulations and analysed the simulation
data. All authors wrote the paper.
Competing interests The authors declare no competing interests.
Additional information
Supplementary Information The online version contains supplementary material available at https://doi.org/
10.1038/s41598-022-13982-9.
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