Integrating cryo-OrbiSIMS with computational modelling and metadynamics simulations enhances RNA structure prediction at atomic resolution.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
22 May 2024
22 May 2024
Historique:
received:
18
09
2023
accepted:
05
05
2024
medline:
23
5
2024
pubmed:
23
5
2024
entrez:
22
5
2024
Statut:
epublish
Résumé
The 3D architecture of RNAs governs their molecular interactions, chemical reactions, and biological functions. However, a large number of RNAs and their protein complexes remain poorly understood due to the limitations of conventional structural biology techniques in deciphering their complex structures and dynamic interactions. To address this limitation, we have benchmarked an integrated approach that combines cryogenic OrbiSIMS, a state-of-the-art solid-state mass spectrometry technique, with computational methods for modelling RNA structures at atomic resolution with enhanced precision. Furthermore, using 7SK RNP as a test case, we have successfully determined the full 3D structure of a native RNA in its apo, native and disease-remodelled states, which offers insights into the structural interactions and plasticity of the 7SK complex within these states. Overall, our study establishes cryo-OrbiSIMS as a valuable tool in the field of RNA structural biology as it enables the study of challenging, native RNA systems.
Identifiants
pubmed: 38777820
doi: 10.1038/s41467-024-48694-3
pii: 10.1038/s41467-024-48694-3
doi:
Substances chimiques
RNA
63231-63-0
Ribonucleoproteins
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4367Subventions
Organisme : University of Nottingham
ID : Anne McLaren Fellowship
Organisme : RCUK | Medical Research Council (MRC)
ID : IMPACT DTP
Organisme : RCUK | Engineering and Physical Sciences Research Council (EPSRC)
ID : EP/P029868/1
Organisme : RCUK | Biotechnology and Biological Sciences Research Council (BBSRC)
ID : BB/S011102/1
Informations de copyright
© 2024. The Author(s).
Références
Zaccai, N. R., Serdyuk, I. N. & Zaccai, J. Methods in Molecular Biophysics: Structure, Dynamics, Function for Biology and Medicine,1-10. (Cambridge University Press, Cambridge, 2017). https://doi.org/10.1017/9781107297227 .
Deng, J. et al. RNA structure determination: From 2D to 3D.Fundam Res. 3, 727–737 (2023).
doi: 10.1016/j.fmre.2023.06.001
Zhang, J., Fei, Y., Sun, L. & Zhang, Q. C. Advances and opportunities in RNA structure experimental determination and computational modeling. Nat. Methods 19, 1193–1207 (2022).
pubmed: 36203019
doi: 10.1038/s41592-022-01623-y
Passarelli, M. K. et al. The 3D OrbiSIMS—label-free metabolic imaging with subcellular lateral resolution and high mass-resolving power. Nat. Methods 14, 1175–1183 (2017).
pubmed: 29131162
doi: 10.1038/nmeth.4504
Kotowska, A. M. et al. Protein identification by 3D OrbiSIMS to facilitate in situ imaging and depth profiling. Nat. Commun. 11, 5832 (2020).
pubmed: 33203841
pmcid: 7672064
doi: 10.1038/s41467-020-19445-x
K. Edney, M. et al. Time resolved growth of (N)-polycyclic aromatic hydrocarbons in engine deposits uncovered with OrbiSIMS depth profiling. Analyst 147, 3854–3866 (2022).
doi: 10.1039/D2AN00798C
Khateb, H. et al. Identification of Pseudomonas aeruginosa exopolysaccharide Psl in biofilms using 3D OrbiSIMS. Biointerphases 18, 031007 (2023).
pubmed: 37255378
pmcid: 10234676
doi: 10.1116/6.0002604
Linke, F. et al. Identifying new biomarkers of aggressive Group 3 and SHH medulloblastoma using 3D hydrogel models, single cell RNA sequencing and 3D OrbiSIMS imaging. Acta Neuropathol. Commun. 11, 6 (2023).
pubmed: 36631900
pmcid: 9835248
doi: 10.1186/s40478-022-01496-4
Suvannapruk, W. et al. Single-cell metabolic profiling of macrophages using 3D OrbiSIMS: correlations with phenotype. Anal. Chem. 94, 9389–9398 (2022).
pubmed: 35713879
pmcid: 9260720
doi: 10.1021/acs.analchem.2c01375
Zhang, J. et al. Cryo-orbiSIMS for 3D molecular imaging of a bacterial biofilm in its native state. Anal. Chem. 92, 9008–9015 (2020).
pubmed: 32460495
doi: 10.1021/acs.analchem.0c01125
He, W. et al. Untargeted metabolomic characterization of glioblastoma intra-tumor heterogeneity using OrbiSIMS. Anal. Chem. 95, 5994–6001 (2023).
pubmed: 36995369
pmcid: 10100400
doi: 10.1021/acs.analchem.2c05807
Newell, C. L., Vorng, J.-L., MacRae, J. I., Gilmore, I. S. & Gould, A. P. Cryogenic orbiSIMS localizes semi-volatile molecules in biological tissues. Angew. Chem. Int. Ed. 59, 18194–18200 (2020).
doi: 10.1002/anie.202006881
Piwowar, A. M. et al. Effects of cryogenic sample analysis on molecular depth profiles with tof-secondary ion mass spectrometry. Anal. Chem. 82, 8291–8299 (2010).
pubmed: 20836508
doi: 10.1021/ac101746h
Wang, H., Castner, D. G., Ratner, B. D. & Jiang, S. Probing the orientation of surface-immobilized immunoglobulin G by time-of-flight secondary ion mass spectrometry. Langmuir ACS J. Surf. Colloids 20, 1877–1887 (2004).
doi: 10.1021/la035376f
Bellaousov, S., Reuter, J. S., Seetin, M. G. & Mathews, D. H. RNAstructure: web servers for RNA secondary structure prediction and analysis. Nucleic Acids Res. 41, W471–W474 (2013).
pubmed: 23620284
pmcid: 3692136
doi: 10.1093/nar/gkt290
Reuter, J. S. & Mathews, D. H. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinforma. 11, 129 (2010).
doi: 10.1186/1471-2105-11-129
Jiang, F., Zhou, K., Ma, L., Gressel, S. & Doudna, J. A. A Cas9–guide RNA complex preorganized for target DNA recognition. Science 348, 1477–1481 (2015).
pubmed: 26113724
doi: 10.1126/science.aab1452
Mattei, E., Pietrosanto, M., Ferrè, F. & Helmer-Citterich, M. Web-Beagle: a web server for the alignment of RNA secondary structures. Nucleic Acids Res. 43, W493–W497 (2015).
pubmed: 25977293
pmcid: 4489221
doi: 10.1093/nar/gkv489
Graf, M. et al. Visualization of translation termination intermediates trapped by the Apidaecin 137 peptide during RF3-mediated recycling of RF1. Nat. Commun. 9, 3053 (2018).
pubmed: 30076302
pmcid: 6076264
doi: 10.1038/s41467-018-05465-1
Burkhardt, D. H. et al. Operon mRNAs are organized into ORF-centric structures that predict translation efficiency. eLife 6, e22037 (2017).
pubmed: 28139975
pmcid: 5318159
doi: 10.7554/eLife.22037
Li, P., Zhou, X., Xu, K. & Zhang, Q. C. RASP: an atlas of transcriptome-wide RNA secondary structure probing data. Nucleic Acids Res 49, D183–D191 (2021).
pubmed: 33068412
doi: 10.1093/nar/gkaa880
Yesselman, J. D. et al. Updates to the RNA mapping database (RMDB), version 2. Nucleic Acids Res. 46, D375–D379 (2018).
pubmed: 30053264
doi: 10.1093/nar/gkx873
Watters, K. E., Yu, A. M., Strobel, E. J., Settle, A. H. & Lucks, J. B. Characterizing RNA structures in vitro and in vivo with selective 2′-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq). Methods 103, 34–48 (2016).
pubmed: 27064082
pmcid: 4921265
doi: 10.1016/j.ymeth.2016.04.002
Antczak, M. et al. New functionality of RNAComposer: application to shape the axis of miR160 precursor structure. Acta Biochim. Pol. 63, 737–744 (2017).
Popenda, M. et al. Automated 3D structure composition for large RNAs. Nucleic Acids Res. 40, e112–e112 (2012).
pubmed: 22539264
pmcid: 3413140
doi: 10.1093/nar/gks339
Pettersen, E. F. et al. UCSF Chimera?A visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004).
pubmed: 15264254
doi: 10.1002/jcc.20084
Couch, G. S. Nucleic acid visualization with UCSF Chimera. Nucleic Acids Res. 34, e29–e29 (2006).
pubmed: 16478715
pmcid: 1368656
doi: 10.1093/nar/gnj031
Meng, E. C., Pettersen, E. F., Couch, G. S., Huang, C. C. & Ferrin, T. E. Tools for integrated sequence-structure analysis with UCSF Chimera. BMC Bioinforma. 7, 339 (2006).
doi: 10.1186/1471-2105-7-339
Zhang, C., Shine, M., Pyle, A. M. & Zhang, Y. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat. Methods 19, 1109–1115 (2022).
pubmed: 36038728
doi: 10.1038/s41592-022-01585-1
Dutta, P. & Sengupta, N. Expectation maximized molecular dynamics: Toward efficient learning of rarely sampled features in free energy surfaces from unbiased simulations. J. Chem. Phys. 153, 154104 (2020).
pubmed: 33092368
doi: 10.1063/5.0021910
Olson, S. W. et al. Discovery of a large-scale, cell-state-responsive allosteric switch in the 7SK RNA using DANCE-MaP. Mol. Cell 82, 1708–1723.e10 (2022).
pubmed: 35320755
pmcid: 9081252
doi: 10.1016/j.molcel.2022.02.009
Brogie, J. E. & Price, D. H. Reconstitution of a functional 7SK snRNP. Nucleic Acids Res 45, 6864–6880 (2017).
pubmed: 28431135
pmcid: 5499737
doi: 10.1093/nar/gkx262
Wassarman, D. A. & Steitz, J. A. Structural analyses of the 7SK ribonucleoprotein (RNP), the most abundant human small rnp of unknown function. Mol. Cell. Biol. 11, 3432–3445 (1991).
pubmed: 1646389
pmcid: 361072
Marz, M. et al. Evolution of 7SK RNA and its protein partners in metazoa. Mol. Biol. Evol. 26, 2821–2830 (2009).
pubmed: 19734296
doi: 10.1093/molbev/msp198
Durney, M. A. & D’Souza, V. M. Preformed protein-binding Motifs in 7SK snRNA: structural and thermodynamic comparisons with retroviral TAR. J. Mol. Biol. 404, 555–567 (2010).
pubmed: 20816986
doi: 10.1016/j.jmb.2010.08.042
Yang, Y. et al. Structural basis of RNA conformational switching in the transcriptional regulator 7SK RNP. Mol. Cell 82, 1724–1736.e7 (2022).
pubmed: 35320752
pmcid: 9081187
doi: 10.1016/j.molcel.2022.03.001
Eichhorn, C. D., Yang, Y., Repeta, L. & Feigon, J. Structural basis for recognition of human 7SK long noncoding RNA by the La-related protein Larp7. Proc. Natl Acad. Sci. USA. 115, E6457–E6466 (2018).
pubmed: 29946027
pmcid: 6048529
doi: 10.1073/pnas.1806276115
Martinez-Zapien, D. et al. The crystal structure of the 5΄ functional domain of the transcription riboregulator 7SK. Nucleic Acids Res. 45, 3568–3579 (2017).
pubmed: 28082395
pmcid: 5389472
Pham, V. V. et al. HIV-1 Tat interactions with cellular 7SK and viral TAR RNAs identifies dual structural mimicry. Nat. Commun. 9, 4266 (2018).
pubmed: 30323330
pmcid: 6189040
doi: 10.1038/s41467-018-06591-6
Yang, Y., Eichhorn, C. D., Wang, Y., Cascio, D. & Feigon, J. Structural basis of 7SK RNA 5′-γ-phosphate methylation and retention by MePCE. Nat. Chem. Biol. 15, 132–140 (2019).
pubmed: 30559425
doi: 10.1038/s41589-018-0188-z
Erk, N., Caroff, & Lepault. Electron microscopy of frozen biological objects: a study using cryosectioning and cryosubstitution. J. Microsc. 189, 236–248 (1998).
Bertolini, S. & Delcorte, A. Reactive molecular dynamics simulations of lysozyme desorption under Ar cluster impact. Appl. Surf. Sci. 631, 157487 (2023).
doi: 10.1016/j.apsusc.2023.157487
Delcorte, A. et al. Large cluster ions: soft local probes and tools for organic and bio surfaces. Phys. Chem. Chem. Phys. 22, 17427–17447 (2020).
pubmed: 32568320
doi: 10.1039/D0CP02398A
Robinson, M. A. & Castner, D. G. Characterization of sample preparation methods of NIH/3T3 fibroblasts for ToF-SIMS analysis. Biointerphases 8, 15 (2013).
pubmed: 24706128
pmcid: 4000548
doi: 10.1186/1559-4106-8-15
Cristaudo, V. et al. Ion yield enhancement at the organic/inorganic interface in SIMS analysis using Ar-GCIB. Appl. Surf. Sci. 536, 147716 (2021).
doi: 10.1016/j.apsusc.2020.147716
Bhattarai, G. et al. Underlying role of mechanical rigidity and topological constraints in physical sputtering and reactive ion etching of amorphous materials. Phys. Rev. Mater. 2, 055602 (2018).
doi: 10.1103/PhysRevMaterials.2.055602
Corley, M., Burns, M. C. & Yeo, G. W. How RNA-binding proteins interact with RNA: molecules and mechanisms. Mol. Cell 78, 9–29 (2020).
pubmed: 32243832
pmcid: 7202378
doi: 10.1016/j.molcel.2020.03.011
Yokoyama, Y. et al. Peptide fragmentation and surface structural analysis by means of tof-sims using large cluster ion sources. Anal. Chem. 88, 3592–3597 (2016).
pubmed: 26916620
doi: 10.1021/acs.analchem.5b04133
Matjacic, L. et al. OrbiSIMS metrology Part I: Optimisation of the target potential and collision cell pressure. Surf. Interface Anal. 54, 331–340 (2022).
doi: 10.1002/sia.7058
Blaha, G., Stanley, R. E. & Steitz, T. A. Formation of the first peptide bond: the structure of EF-P bound to the 70S ribosome. Science 325, 966–970 (2009).
pubmed: 19696344
pmcid: 3296453
doi: 10.1126/science.1175800
Yang, Z., Zhu, Q., Luo, K. & Zhou, Q. The 7SK small nuclear RNA inhibits the CDK9/cyclin T1 kinase to control transcription. Nature 414, 317–322 (2001).
pubmed: 11713532
doi: 10.1038/35104575
Bigalke, J. M. et al. Formation of Tat–TAR containing ribonucleoprotein complexes for biochemical and structural analyses. Methods 53, 78–84 (2011).
pubmed: 20385237
doi: 10.1016/j.ymeth.2010.04.001
MATLAB version 9.10.0.1613233 (R2021a). Natick, Massachusetts: The Mathworks, Inc. (2021).
Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).
doi: 10.1016/j.softx.2015.06.001
Mattei, E., Ausiello, G., Ferrè, F. & Helmer-Citterich, M. A novel approach to represent and compare RNA secondary structures. Nucleic Acids Res. 42, 6146–6157 (2014).
pubmed: 24753415
pmcid: 4041456
doi: 10.1093/nar/gku283
Borkar, A. N. et al. Structure of a low-population binding intermediate in protein-RNA recognition. Proc. Natl Acad. Sci. 113, 7171–7176 (2016).
pubmed: 27286828
pmcid: 4932932
doi: 10.1073/pnas.1521349113
Zgarbová, M. et al. Refinement of the Cornell et al. nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J. Chem. Theory Comput. 7, 2886–2902 (2011).
pubmed: 21921995
pmcid: 3171997
doi: 10.1021/ct200162x
Tan, D., Piana, S., Dirks, R. M. & Shaw, D. E. RNA force field with accuracy comparable to state-of-the-art protein force fields. Proc. Natl Acad. Sci. USA 115, E1346–E1355 (2018).
Borkar, A., Kotowska, A. & Watts, J. Integrating Cryo-OrbiSIMS with computational modelling and metadynamics simulations enhances RNA structure prediction at atomic resolution: Raw cryo-OrbiSIMS Data. Nottingham Research Data Management Repository https://doi.org/10.17639/NOTT.7354 (2023).
Borkar, A. N. GitHub Repository BorkarLab/OrbiSIMS_RNA_analysis: RNA_published. Zenodo https://doi.org/10.5281/zenodo.10960751 (2024).