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Journal of Zhejiang University-SCIENCE B

, Volume 20, Issue 6, pp 496–502 | Cite as

On the necessity of an integrative approach to understand protein structural dynamics

  • Qing-fen Yang
  • Chun TangEmail author
Review

Abstract

Proteins are dynamic, fluctuating between multiple conformational states. Protein dynamics, spanning orders of magnitude in time and space, allow proteins to perform specific functions. Moreover, under certain conditions, proteins can morph into a different set of conformations. Thus, a complete understanding of protein structural dynamics can provide mechanistic insights into protein function. Here, we review the latest developments in methods used to determine protein ensemble structures and to characterize protein dynamics. Techniques including X-ray crystallography, cryogenic electron microscopy, and small angle scattering can provide structural information on specific conformational states or on the averaged shape of the protein, whereas techniques including nuclear magnetic resonance, fluorescence resonance energy transfer (FRET), and chemical cross-linking coupled with mass spectrometry provide information on the fluctuation of the distances between protein domains, residues, and atoms for the multiple conformational states of the protein. In particular, FRET measurements at the single-molecule level allow rapid resolution of protein conformational states, where information is otherwise obscured in bulk measurements. Taken together, the different techniques complement each other and their integrated use can offer a clear picture of protein structure and dynamics.

Key words

Conformational dynamics Integrative structural biology Distance restraint Ensemble averaging Nuclear magnetic resonance (NMR) 

整合方法研究蛋白质结构动态之必要性

概要

蛋白质是动态的, 在多种结构状态间转化。蛋白 质的结构动态在时间和空间上跨越多个数量级, 允许蛋白质执行特定生物学功能。细胞条件和细 胞环境的变化会使蛋白质动态结构发生变化。因 此, 对蛋白质结构动态的全面表征可提供对蛋白 质功能的机制的深入了解。在这里, 我们综述了 用于测定蛋白质系综动态结构和表征蛋白质结 构动态变化的方法的最新发展。X 射线晶体学、 冷冻电镜和小角散射等技术可提供关于蛋白质 特定状态或平均的结构信息, 而核磁共振、荧光 共振能量转移(FRET)和化学交联质谱等技术 则提供了蛋白质结构域、蛋白质残基和原子之间 的处于不同结构状态时的距离波动信息。尤其要 指出的是, 单分子水平的FRET 测量能对蛋白质 构象状态进行快速区分, 而这样的信息会在其它 测量中被掩盖。总之, 不同的生物物理技术互为 补充, 只有通过它们的综合运用, 才能清晰可见 蛋白质结构和动态。

关键词

结构动态 整合结构生物学 距离约束 系综平 均 核磁共振 

CLC number

Q51 

References

  1. Bahar I, Lezon TR, Bakan A, et al., 2010. Normal mode analysis of biomolecular structures: functional mechanisms of membrane proteins. Chem Rev, 110(3):1463–1497.  https://doi.org/10.1021/cr900095e CrossRefGoogle Scholar
  2. Bai XC, McMullan G, Scheres SHW, 2015. How cryo-EM is revolutionizing structural biology. Trends Biochem Sci, 40(1):49–57.  https://doi.org/10.1016/j.tibs.2014.10.005 CrossRefGoogle Scholar
  3. Bermejo GA, Clore GM, Schwieters CD, 2012. Smooth statistical torsion angle potential derived from a large conformational database via adaptive kernel density estimation improves the quality of NMR protein structures. Protein Sci, 21(12):1824–1836.  https://doi.org/10.1002/pro.2163 CrossRefGoogle Scholar
  4. Brady JP, Farber PJ, Sekhar A, et al., 2017. Structural and hydrodynamic properties of an intrinsically disordered region of a germ cell-specific protein on phase separation. Proc Natl Acad Sci USA, 114(39):E8194–E8203.  https://doi.org/10.1073/pnas.1706197114 CrossRefGoogle Scholar
  5. Brodie NI, Popov KI, Petrotchenko EV, et al., 2017. Solving protein structures using short-distance cross-linking constraints as a guide for discrete molecular dynamics simulations. Sci Adv, 3(7):e1700479.  https://doi.org/10.1126/sciadv.1700479 CrossRefGoogle Scholar
  6. Bryngelson JD, Onuchic JN, Socci ND, et al., 1995. Funnels, pathways, and the energy landscape of protein folding: a synthesis. Proteins, 21(3):167–195.  https://doi.org/10.1002/prot.340210302 CrossRefGoogle Scholar
  7. Clore GM, Iwahara J, 2009. Theory, practice, and applications of paramagnetic relaxation enhancement for the characterization of transient low-population states of biological macromolecules and their complexes. Chem Rev, 109(9): 4108–4139.  https://doi.org/10.1021/cr900033p CrossRefGoogle Scholar
  8. Ding YH, Gong Z, Dong X, et al., 2017. Modeling protein excited-state structures from “over-length” chemical cross-links. J Biol Chem, 292(4):1187–1196.  https://doi.org/10.1074/jbc.M116.761841 CrossRefGoogle Scholar
  9. Dong X, Gong Z, Lu YB, et al., 2017. Ubiquitin S65 phosphorylation engenders a pH-sensitive conformational switch. Proc Natl Acad Sci USA, 114(26):6770–6775.  https://doi.org/10.1073/pnas.1705718114 Google Scholar
  10. Dror RO, Dirks RM, Grossman JP, et al., 2012. Biomolecular simulation: a computational microscope for molecular biology. Annu Rev Biophys, 41:429–452.  https://doi.org/10.1146/annurev-biophys-042910-155245 CrossRefGoogle Scholar
  11. Elbaum-Garfinkle S, Kim Y, Szczepaniak K, et al., 2015. The disordered P granule protein LAF-1 drives phase separation into droplets with tunable viscosity and dynamics. Proc Natl Acad Sci USA, 112(23):7189–7194.  https://doi.org/10.1073/pnas.1504822112 CrossRefGoogle Scholar
  12. Ferber M, Kosinski J, Ori A, et al., 2016. Automated structure modeling of large protein assemblies using crosslinks as distance restraints. Nat Methods, 13(6):515–520.  https://doi.org/10.1038/nmeth.3838 CrossRefGoogle Scholar
  13. Fraser JS, van den Bedem H, Samelson AJ, et al., 2011. Accessing protein conformational ensembles using room-temperature X-ray crystallography. Proc Natl Acad Sci USA, 108(39):16247–16252.  https://doi.org/10.1073/pnas.1111325108 CrossRefGoogle Scholar
  14. Gladkova C, Schubert AF, Wagstaff JL, et al., 2017. An invisible ubiquitin conformation is required for efficient phosphorylation by PINK1. EMBO J, 36(24):3555–3572.  https://doi.org/10.15252/embj.201797876 CrossRefGoogle Scholar
  15. Gong Z, Ding YH, Dong X, et al., 2015. Visualizing the ensemble structures of protein complexes using chemical cross-linking coupled with mass spectrometry. Biophys Rep, 1(3):127–138.  https://doi.org/10.1007/s41048-015-0015-y CrossRefGoogle Scholar
  16. Henzler-Wildman K, Kern D, 2007. Dynamic personalities of proteins. Nature, 450(7172):964–972.  https://doi.org/10.1038/nature06522 CrossRefGoogle Scholar
  17. Iwahara J, Anderson DE, Murphy EC, et al., 2003. EDTA-derivatized deoxythymidine as a tool for rapid determination of protein binding polarity to DNA by intermolecular paramagnetic relaxation enhancement. J Am Chem Soc, 125(22):6634–6635.CrossRefGoogle Scholar
  18. Iwahara J, Tang C, Clore GM, 2007. Practical aspects of 1H transverse paramagnetic relaxation enhancement measurements on macromolecules. J Magn Reson, 184(2): 185–195.  https://doi.org/10.1016/j.jmr.2006.10.003 CrossRefGoogle Scholar
  19. Kalinin S, Valeri A, Antonik M, et al., 2010. Detection of structural dynamics by FRET: a photon distribution and fluorescence lifetime analysis of systems with multiple states. J Phys Chem B, 114(23):7983–7995.  https://doi.org/10.1021/jp102156t CrossRefGoogle Scholar
  20. Kuznetsova IM, Turoverov KK, Uversky VN, 2014. What macromolecular crowding can do to a protein? Int J Mol Sci, 15(12):23090–23140.  https://doi.org/10.3390/ijms151223090 CrossRefGoogle Scholar
  21. Lerner E, Cordes T, Ingargiola A, et al., 2018. Toward dynamic structural biology: two decades of single-molecule Förster resonance energy transfer. Science, 359(6373): eaan1133.  https://doi.org/10.1126/science.aan1133 CrossRefGoogle Scholar
  22. Li PL, Banjade S, Cheng HC, et al., 2012. Phase transitions in the assembly of multivalent signalling proteins. Nature, 483(7389):336–340.  https://doi.org/10.1038/nature10879 CrossRefGoogle Scholar
  23. Liu Z, Gong Z, Dong X, et al., 2016. Transient protein-protein interactions visualized by solution NMR. Biochim Biophys Acta, 1864(1):115–122.  https://doi.org/10.1016/j.bbapap.2015.04.009 CrossRefGoogle Scholar
  24. Liu Z, Gong Z, Cao Y, et al., 2018. Characterizing protein dynamics with integrative use of bulk and single-molecule techniques. Biochemistry, 57(3):305–313.  https://doi.org/10.1021/acs.biochem.7b00817 CrossRefGoogle Scholar
  25. Luchinat E, Banci L, 2016. A unique tool for cellular structural biology: in-cell NMR. J Biol Chem, 291(8):3776–3784.  https://doi.org/10.1074/jbc.R115.643247 CrossRefGoogle Scholar
  26. MacCallum JL, Perez A, Dill KA, 2015. Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference. Proc Natl Acad Sci USA, 112(22):6985–6990.  https://doi.org/10.1073/pnas.1506788112 CrossRefGoogle Scholar
  27. Matthew Allen Bullock J, Schwab J, Thalassinos K, et al., 2016. The importance of non-accessible crosslinks and solvent accessible surface distance in modeling proteins with restraints from crosslinking mass spectrometry. Mol Cell Proteomics, 15(7):2491–2500.  https://doi.org/10.1074/mcp.M116.058560 CrossRefGoogle Scholar
  28. Pan BB, Yang F, Ye YS, et al., 2016. 3D structure determination of a protein in living cells using paramagnetic NMR spectroscopy. Chem Commun, 52(67):10237–10240.  https://doi.org/10.1039/c6cc05490k CrossRefGoogle Scholar
  29. Peulen TO, Opanasyuk O, Seidel CAM, 2017. Combining graphical and analytical methods with molecular simulations to analyze time-resolved FRET measurements of labeled macromolecules accurately. J Phys Chem B, 121(35): 8211–8241.  https://doi.org/10.1021/acs.jpcb.7b03441 CrossRefGoogle Scholar
  30. Rieping W, Habeck M, Nilges M, 2005. Inferential structure determination. Science, 309(5732):303–306.  https://doi.org/10.1126/science.1110428 CrossRefGoogle Scholar
  31. Sakakibara D, Sasaki A, Ikeya T, et al., 2009. Protein structure determination in living cells by in-cell NMR spectroscopy. Nature, 458(7234):102–105.  https://doi.org/10.1038/nature07814 CrossRefGoogle Scholar
  32. Sali A, Berman HM, Schwede T, et al., 2015. Outcome of the first wwPDB hybrid/integrative methods task force workshop. Structure, 23(7):1156–1167.  https://doi.org/10.1016/j.str.2015.05.013 CrossRefGoogle Scholar
  33. Schwieters CD, Clore GM, 2014. Using small angle solution scattering data in Xplor-NIH structure calculations. Prog Nucl Magn Reson Spectrosc, 80:1–11.  https://doi.org/10.1016/j.pnmrs.2014.03.001 CrossRefGoogle Scholar
  34. Schwieters CD, Bermejo GA, Clore GM, 2018. Xplor-NIH for molecular structure determination from NMR and other data sources. Protein Sci, 27(1):26–40.  https://doi.org/10.1002/pro.3248 CrossRefGoogle Scholar
  35. Sekhar A, Kay LE, 2013. NMR paves the way for atomic level descriptions of sparsely populated, transiently formed biomolecular conformers. Proc Natl Acad Sci USA, 110(32):12867–12874.  https://doi.org/10.1073/pnas.1305688110 CrossRefGoogle Scholar
  36. Tang C, Schwieters CD, Clore GM, 2007. Open-to-closed transition in apo maltose-binding protein observed by paramagnetic NMR. Nature, 449(7165):1078–1082.  https://doi.org/10.1038/nature06232 CrossRefGoogle Scholar
  37. Tang YF, Huang YJ, Hopf TA, et al., 2015. Protein structure determination by combining sparse NMR data with evolutionary couplings. Nat Methods, 12(8):751–754.  https://doi.org/10.1038/nmeth.3455 CrossRefGoogle Scholar
  38. Wagner G, Wüthrich K, 1978. Dynamic model of globular protein conformations based on NMR studies in solution. Nature, 275(5677):247–248.  https://doi.org/10.1038/275247a0 CrossRefGoogle Scholar
  39. Xing Q, Huang P, Yang J, et al., 2014. Visualizing an ultraweak protein-protein interaction in phosphorylation signaling. Angew Chem Int Ed Engl, 53(43):11501–11505.  https://doi.org/10.1002/anie.201405976 CrossRefGoogle Scholar
  40. Yang B, Wu YJ, Zhu M, et al., 2012. Identification of cross-linked peptides from complex samples. Nat Methods, 9(9):904–906.  https://doi.org/10.1038/nmeth.2099 CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Hubei Key Laboratory of Natural Medicinal Chemistry and Resource Evaluation, School of Pharmacy, Tongji Medical CollegeHuazhong University of Science and TechnologyWuhanChina
  2. 2.CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, National Center for Magnetic Resonance at WuhanWuhan Institute of Physics and Mathematics of the Chinese Academy of SciencesWuhanChina

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