CoChat
Sign Up
Cited by 21SARS-CoV-2 and COVID-19 ResearchRNA and protein synthesis mechanismsRNA Research and Splicing

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Mutagenesis and structural studies reveal the basis for the specific binding of SARS-CoV-2 SL3 RNA element with human TIA1 protein

Open access
Zhang, Dong, Qiao, Lulu, Lei, Xiaobo, Dong, Xiaojing, Tong, Yunguang, Wang, Jianwei, Wang, Zhiye, Zhou, Ruhong·2023·Nature Communications
DOI 10.1038/s41467-023-39410-8PMC PMC10287707

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Viral RNA-host protein interactions are indispensable during RNA virus transcription and replication, but their detailed structural and dynamical features remain largely elusive. Here, we characterize the binding interface for the SARS-CoV-2 stem-loop 3 (SL3) cis-acting element to human TIA1 protein with a combined theoretical and experimental approaches. The highly structured SARS-CoV-2 SL3 has a high binding affinity to TIA1 protein, in which the aromatic stacking, hydrogen bonds, and hydrophobic interactions collectively direct this specific binding. Further mutagenesis studies validate our proposed 3D binding model and reveal two SL3 variants have enhanced binding affinities to TIA1. And disruptions of the identified RNA-protein interactions with designed antisense oligonucleotides dramatically reduce SARS-CoV-2 infection in cells. Finally, TIA1 protein could interact with conserved SL3 RNA elements within other betacoronavirus lineages. These findings open an avenue to explore the viral RNA-host protein interactions and provide a pioneering structural basis for RNA-targeting antiviral drug design.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-023-39410-8

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Structures of SARS-CoV-2 RNA genome stem-loop (SL) SL2 and SL3 elements and their binding ability to human TIA1 protein. a Architecture of TIA1 RNA recognition motif (RRM) RRM2 and RRM3 domains. RRM2 and RRM3 adopt the canonical βαββαβ RRM fold composed of four anti-parallel β-sheets (β1, β2, β3, and β4) and two α-helices (α1 and α2). A non-canonical helix α0 in the flexible linker is preceded to RRM3. Secondary and 3D structures for (b) SL2 and (c) SL3 RNA elements. Stem, hairpin loop, and terminal loop in 5′-/3′-end are colored by green, blue, and orange, respectively. All 3D structures are predicted by IsRNA2 model. The NMR solution structure (1st model) for SARS-CoV SL232 (PDB id: 2l6i) is given in gray in (b). Other possible 3D conformations for SL3 element predicted by IsRNA2 are shown in light gray in (c). Positions from the whole SARS-CoV-2 RNA genome for SL2 and SL3 elements are provided in parentheses. d EMSA results (left) and the binding curve (right) show TIA1 bound to 5′ end Cy3-labeled SL2 + SL3 RNA. The binding curves for individual (e) SL2 and (f) SL3 RNA element with TIA1 protein. From d to f, the Kd values were calculated from the EMSA image quantification from three independent experiments. Data are presented as mean ± SD. Source data are provided as a Source Data file.
Fig. 1 Structures of SARS-CoV-2 RNA genome stem-loop (SL) SL2 and SL3 elements and their binding ability to human TIA1 protein. a Architecture of TIA1 RNA recognition motif (RRM) RRM2 and RRM3 domains. RRM2 and RRM3 adopt the canonical βαββαβ RRM fold composed of four anti-parallel β-sheets (β1, β2, β3, and β4) and two α-helices (α1 and α2). A non-canonical helix α0 in the flexible linker is preceded to RRM3. Secondary and 3D structures for (b) SL2 and (c) SL3 RNA elements. Stem, hairpin loop, and terminal loop in 5′-/3′-end are colored by green, blue, and orange, respectively. All 3D structures are predicted by IsRNA2 model. The NMR solution structure (1st model) for SARS-CoV SL232 (PDB id: 2l6i) is given in gray in (b). Other possible 3D conformations for SL3 element predicted by IsRNA2 are shown in light gray in (c). Positions from the whole SARS-CoV-2 RNA genome for SL2 and SL3 elements are provided in parentheses. d EMSA results (left) and the binding curve (right) show TIA1 bound to 5′ end Cy3-labeled SL2 + SL3 RNA. The binding curves for individual (e) SL2 and (f) SL3 RNA element with TIA1 protein. From d to f, the Kd values were calculated from the EMSA image quantification from three independent experiments. Data are presented as mean ± SD. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 2 Binding capabilities of various SL3 RNA variants with human TIA1 protein. a WT SL3 consists of a 7-nt hairpin loop (HP), a 4-base pairs stem, and a 5-nt 3′-terminal loop (TL). The binding curves of human TIA1 protein with various mutated and truncated variants of SL3 RNA element: b SL3-HP3, the 7-nt HP reduced to 3-nt length, c SL3-HP-C7, sequence of 7-nt HP mutated to oligo C, (d) SL3-HP-U7, sequence of 7-nt HP mutated to oligo U, e SL3-TL1, last four nucleotides of 5-nt TL deleted, f SL3-TL-C3, three successive uridines in TL replaced by three cytosines, g SL3-TL-G3, three successive uridines in TL replaced by three guanines, h SL3-GC, two middle A-U base pairs in the stem substituted by G-C base pairs. All the secondary structures for SL3 variants are predicted by RNAStructure36 and mutated nucleotides are colored by red. All the Kd values were calculated from the EMSA image quantification from three independent experiments. Data are presented as mean ± SD. i The yield of the bona fide SARS-CoV-2 virus with designed ASOs targeting SL3 RNA elements (including HP and TL loops and the transcriptional regulatory sequence (TRS)) in Huh7.5.1 cells for 24 h, compared to the “scramble” control treated with a non-targeting ASO. SL2/3 has previously been reported18. Data represent the mean ± SEM; n = 3 biological replicates. ***p = 3.4 × 10−6 (ASO-SL2/3), 4.4 × 10−7 (ASO-HP), 1.2 × 10−5 (ASO-TL), and 1.6 × 10−7 (ASO-TRS) using unpaired two-sample Student’s t test. Source data are provided as a Source Data file.
Fig. 2 Binding capabilities of various SL3 RNA variants with human TIA1 protein. a WT SL3 consists of a 7-nt hairpin loop (HP), a 4-base pairs stem, and a 5-nt 3′-terminal loop (TL). The binding curves of human TIA1 protein with various mutated and truncated variants of SL3 RNA element: b SL3-HP3, the 7-nt HP reduced to 3-nt length, c SL3-HP-C7, sequence of 7-nt HP mutated to oligo C, (d) SL3-HP-U7, sequence of 7-nt HP mutated to oligo U, e SL3-TL1, last four nucleotides of 5-nt TL deleted, f SL3-TL-C3, three successive uridines in TL replaced by three cytosines, g SL3-TL-G3, three successive uridines in TL replaced by three guanines, h SL3-GC, two middle A-U base pairs in the stem substituted by G-C base pairs. All the secondary structures for SL3 variants are predicted by RNAStructure36 and mutated nucleotides are colored by red. All the Kd values were calculated from the EMSA image quantification from three independent experiments. Data are presented as mean ± SD. i The yield of the bona fide SARS-CoV-2 virus with designed ASOs targeting SL3 RNA elements (including HP and TL loops and the transcriptional regulatory sequence (TRS)) in Huh7.5.1 cells for 24 h, compared to the “scramble” control treated with a non-targeting ASO. SL2/3 has previously been reported18. Data represent the mean ± SEM; n = 3 biological replicates. ***p = 3.4 × 10−6 (ASO-SL2/3), 4.4 × 10−7 (ASO-HP), 1.2 × 10−5 (ASO-TL), and 1.6 × 10−7 (ASO-TRS) using unpaired two-sample Student’s t test. Source data are provided as a Source Data file.CC BY · PubMed Central
Fig. 3 The putative 3D binding model for SARS-CoV-2 SL3 RNA element and human TIA1 complex. a Overview of the most probable 3D structure of binding complex constructed by computational modeling. TIA1 RRM2, RRM3, and flexible linker are colored by sky blue, light green, and gray, respectively. Hairpin loop, stem, and terminal loop of SL3 are separately colored by blue, green, and orange. Details of interactions between TIA1 RRM3 and nucleotides (b) U67, (c) A68 and A69 in hairpin loop region of SL3 element. Details of interactions between TIA1 RRM2 and nucleotides (d) U76, (e) U77, and (f) U78 in 3′-terminal loop of SL3. Atoms of phosphorus, oxygen, nitrogen, and hydrogen are colored yellow, red, purple, and white, respectively, and carbon atoms are colored according to their locations. Hydrogen bonds between RNA nucleotides and TIA1 residues are indicated by thin black lines. Rotations may be adopted to display the interaction details in subfigures (b–f).
Fig. 3 The putative 3D binding model for SARS-CoV-2 SL3 RNA element and human TIA1 complex. a Overview of the most probable 3D structure of binding complex constructed by computational modeling. TIA1 RRM2, RRM3, and flexible linker are colored by sky blue, light green, and gray, respectively. Hairpin loop, stem, and terminal loop of SL3 are separately colored by blue, green, and orange. Details of interactions between TIA1 RRM3 and nucleotides (b) U67, (c) A68 and A69 in hairpin loop region of SL3 element. Details of interactions between TIA1 RRM2 and nucleotides (d) U76, (e) U77, and (f) U78 in 3′-terminal loop of SL3. Atoms of phosphorus, oxygen, nitrogen, and hydrogen are colored yellow, red, purple, and white, respectively, and carbon atoms are colored according to their locations. Hydrogen bonds between RNA nucleotides and TIA1 residues are indicated by thin black lines. Rotations may be adopted to display the interaction details in subfigures (b–f).CC BY · PubMed Central
Fig. 4 Influence of single nucleotide mutations of SL3 RNA element on TIA1 binding. a The relative binding free energy changes ΔΔGcalc for exhaustive single nucleotide mutations of six identified sites that are important for SL3 binding. ΔΔGcalc<0 means enhanced binding affinity with TIA1 protein than wild type SL3 element, and vice versa. Five independent FEP calculations were run and data are presented as mean ± SD. b Comparison of the relative binding free energy changes derived from FEP calculations (ΔΔGcalc) and EMSA experiments (ΔΔGexp) for twelve representative mutations of SL3. The linear fit to the data is shown as a red line and R is the Pearson correlation coefficient. Five independent FEP calculations were run for ΔΔGcalc and data are presented as mean ± SD. The values of ΔΔGexp were derived from three independent EMSA experiments and data are presented as mean ± SD. Source data are provided as a Source Data file. Structural details before (left) and after (right) mutations for SL3 (c) A68U and (d) A69C variants. The phosphorus, oxygen, nitrogen, and hydrogen atoms are colored by yellow, red, purple, and white, respectively. The carbon atoms in SL3 and TIA1 are separately colored to blue and light green. Black dash lines indicate the distances of given atom pairs.
Fig. 4 Influence of single nucleotide mutations of SL3 RNA element on TIA1 binding. a The relative binding free energy changes ΔΔGcalc for exhaustive single nucleotide mutations of six identified sites that are important for SL3 binding. ΔΔGcalc<0 means enhanced binding affinity with TIA1 protein than wild type SL3 element, and vice versa. Five independent FEP calculations were run and data are presented as mean ± SD. b Comparison of the relative binding free energy changes derived from FEP calculations (ΔΔGcalc) and EMSA experiments (ΔΔGexp) for twelve representative mutations of SL3. The linear fit to the data is shown as a red line and R is the Pearson correlation coefficient. Five independent FEP calculations were run for ΔΔGcalc and data are presented as mean ± SD. The values of ΔΔGexp were derived from three independent EMSA experiments and data are presented as mean ± SD. Source data are provided as a Source Data file. Structural details before (left) and after (right) mutations for SL3 (c) A68U and (d) A69C variants. The phosphorus, oxygen, nitrogen, and hydrogen atoms are colored by yellow, red, purple, and white, respectively. The carbon atoms in SL3 and TIA1 are separately colored to blue and light green. Black dash lines indicate the distances of given atom pairs.CC BY · PubMed Central
Fig. 5 Sequence comparison of SL3 RNA elements in betacoronavirus genomes and their binding abilities to human TIA1 protein. a Multiple sequence alignment showing nucleotide coverage within SL3 RNA elements of representative species within genus Betacoronavirus. The transcriptional regulatory sequences (TRS) are highlighted by red box. Six identified sites important for SARS-CoV-2 SL3 binding by TIA1 protein are marked by red triangles. Sequence positions of SL3 RNA element in each species are given in parentheses. BatHp, Bat Hp-betacoronavirus/Zhejiang2013; RousettusBat, Rousettus bat coronavirus; RousettusBatHKU9, Rousettus bat coronavirus HKU9; PipistrellusBat, Pipistrellus bat coronavirus HKU5; TylonycterisBat, Tylonycteris bat coronavirus HKU4; England1, Betacoronavirus England 1; MERS, Middle East respiratory syndrome-related coronavirus; Erinaceus, Betacoronavirus Erinaceus/VMC/DEU/2012. The binding curves between human TIA1 protein and SL3 RNA elements from (b) RousettusBatHKU9 and (c) MERS genomes. The Kd values were calculated from the EMSA image quantification from three independent experiments. Data are presented as mean ± SD. Source data are provided as a Source Data file.
Fig. 5 Sequence comparison of SL3 RNA elements in betacoronavirus genomes and their binding abilities to human TIA1 protein. a Multiple sequence alignment showing nucleotide coverage within SL3 RNA elements of representative species within genus Betacoronavirus. The transcriptional regulatory sequences (TRS) are highlighted by red box. Six identified sites important for SARS-CoV-2 SL3 binding by TIA1 protein are marked by red triangles. Sequence positions of SL3 RNA element in each species are given in parentheses. BatHp, Bat Hp-betacoronavirus/Zhejiang2013; RousettusBat, Rousettus bat coronavirus; RousettusBatHKU9, Rousettus bat coronavirus HKU9; PipistrellusBat, Pipistrellus bat coronavirus HKU5; TylonycterisBat, Tylonycteris bat coronavirus HKU4; England1, Betacoronavirus England 1; MERS, Middle East respiratory syndrome-related coronavirus; Erinaceus, Betacoronavirus Erinaceus/VMC/DEU/2012. The binding curves between human TIA1 protein and SL3 RNA elements from (b) RousettusBatHKU9 and (c) MERS genomes. The Kd values were calculated from the EMSA image quantification from three independent experiments. Data are presented as mean ± SD. Source data are provided as a Source Data file.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 73

  1. Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature 579, 265–269 (2020). · DOI · Paper page
  2. Perlman, S. Another decade, another coronavirus. N. Engl. J. Med. 382, 760–762 (2020). · DOI
  3. Li, Z. & Nagy, P. D. Diverse roles of host RNA binding proteins in RNA virus replication. RNA Biol. 8, 305–315 (2011). · DOI
  4. Nagy, P. D. & Pogany, J. The dependence of viral RNA replication on co-opted host factors. Nat. Rev. Microbiol. 10, 137–149 (2011). · DOI
  5. Sola, I., Mateos-Gomez, P. A., Almazan, F., Zuniga, S. & Enjuanes, L. RNA-RNA and RNA-protein interactions in coronavirus replication and transcription. RNA Biol. 8, 237–248 (2011). · DOI
  6. Baggen, J., Vanstreels, E., Jansen, S. & Daelemans, D. Cellular host factors for SARS-CoV-2 infection. Nat. Microbiol. 6, 1219–1232 (2021). · DOI
  7. Flynn, R. A. et al. Discovery and functional interrogation of SARS-CoV-2 RNA-host protein interactions. Cell 184, 2394–2411 (2021). · DOI
  8. Kamel, W. et al. Global analysis of protein-RNA interactions in SARS-CoV-2-infected cells reveals key regulators of infection. Mol. Cell 81, 2851–2867 (2021). · DOI
  9. Schmidt, N. et al. The SARS-CoV-2 RNA-protein interactome in infected human cells. Nat. Microbiol. 6, 339–353 (2021). · DOI · Paper page
  10. Chen, S. C. & Olsthoorn, R. C. Group-specific structural features of the 5′-proximal sequences of coronavirus genomic RNAs. Virology 401, 29–41 (2010). · DOI
  11. Madhugiri, R., Fricke, M., Marz, M. & Ziebuhr, J. Coronavirus cis-acting RNA elements. Adv. Virus Res. 96, 127–163 (2016). · DOI
  12. Yang, D. & Leibowitz, J. L. The structure and functions of coronavirus genomic 3′ and 5′ ends. Virus Res. 206, 120–133 (2015). · DOI
  13. Andrews, R. J. et al. A map of the SARS-CoV-2 RNA structurome. NAR Genom. Bioinform. 3, lqab043 (2021). · DOI
  14. Huston, N. C. et al. Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms. Mol. Cell 81, 584–598 (2021). · DOI
  15. Manfredonia, I. et al. Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements. Nucleic Acids Res. 48, 12436–12452 (2020). · DOI
  16. Miao, Z., Tidu, A., Eriani, G. & Martin, F. Secondary structure of the SARS-CoV-2 5′-UTR. RNA Biol. 18, 447–456 (2021). · DOI · Paper page
  17. Rangan, R. et al. RNA genome conservation and secondary structure in SARS-CoV-2 and SARS-related viruses: a first look. RNA 26, 937–959 (2020). · DOI
  18. Sun, L. et al. In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs. Cell 184, 1865–1883 e1820 (2021). · DOI
  19. Wacker, A. et al. Secondary structure determination of conserved SARS-CoV-2 RNA elements by NMR spectroscopy. Nucleic Acids Res. 48, 12415–12435 (2020). · DOI
  20. Beck, A. R., Medley, Q. G., O’Brien, S., Anderson, P. & Streuli, M. Structure, tissue distribution and genomic organization of the murine RRM-type RNA binding proteins TIA-1 and TIAR. Nucleic Acids Res. 24, 3829–3835 (1996). · DOI
  21. Sanchez-Jimenez, C. & Izquierdo, J. M. T-cell intracellular antigens in health and disease. Cell Cycle 14, 2033–2043 (2015). · DOI
  22. Dember, L. M., Kim, N. D., Liu, K. Q. & Anderson, P. Individual RNA recognition motifs of TIA-1 and TIAR have different RNA binding specificities. J. Biol. Chem. 271, 2783–2788 (1996). · DOI
  23. Wang, I. et al. Structure, dynamics and RNA binding of the multi-domain splicing factor TIA-1. Nucleic Acids Res. 42, 5949–5966 (2014). · DOI
  24. Waris, S. et al. TIA-1 RRM23 binding and recognition of target oligonucleotides. Nucleic Acids Res. 45, 4944–4957 (2017). · DOI
  25. Emara, M. M. & Brinton, M. A. Interaction of TIA-1/TIAR with West Nile and dengue virus products in infected cells interferes with stress granule formation and processing body assembly. Proc. Natl Acad. Sci. USA 104, 9041–9046 (2007). · DOI
  26. Emara, M. M., Liu, H., Davis, W. G. & Brinton, M. A. Mutation of mapped TIA-1/TIAR binding sites in the 3’ terminal stem-loop of West Nile virus minus-strand RNA in an infectious clone negatively affects genomic RNA amplification. J. Virol. 82, 10657–10670 (2008). · DOI
  27. Li, W. et al. Cell proteins TIA-1 and TIAR interact with the 3’ stem-loop of the West Nile virus complementary minus-strand RNA and facilitate virus replication. J. Virol. 76, 11989–12000 (2002). · DOI
  28. Albornoz, A., Carletti, T., Corazza, G. & Marcello, A. The stress granule component TIA-1 binds tick-borne encephalitis virus RNA and is recruited to perinuclear sites of viral replication to inhibit viral translation. J. Virol. 88, 6611–6622 (2014). · DOI
  29. Zafferani, M. et al. Amilorides inhibit SARS-CoV-2 replication in vitro by targeting RNA structures. Sci. Adv. 7, eabl6096 (2021). · DOI
  30. Park, S. J., Kim, Y. G. & Park, H. J. Identification of RNA pseudoknot-binding ligand that inhibits the −1 ribosomal frameshifting of SARS-coronavirus by structure-based virtual screening. J. Am. Chem. Soc. 133, 10094–10100 (2011). · DOI
  31. Zhang, D., Chen, S. J. & Zhou, R. Modeling noncanonical RNA base pairs by a coarse-grained IsRNA2 model. J. Phys. Chem. B 125, 11907–11915 (2021). · DOI
  32. Lee, C. W., Li, L. & Giedroc, D. P. The solution structure of coronaviral stem-loop 2 (SL2) reveals a canonical CUYG tetraloop fold. FEBS Lett. 585, 1049–1053 (2011). · DOI
  33. Rangan, R. et al. De novo 3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures. Nucleic Acids Res. 49, 3092–3108 (2021). · DOI
  34. Bottaro, S., Bussi, G. & Lindorff-Larsen, K. Conformational ensembles of noncoding elements in the SARS-CoV-2 genome from molecular dynamics simulations. J. Am. Chem. Soc. 143, 8333–8343 (2021). · DOI
  35. Afroz, T., Cienikova, Z., Clery, A. & Allain, F. H. T. One, two, three, four! how multiple RRMs read the genome sequence. Method Enzymol. 558, 235–278 (2015). · DOI · Paper page
  36. Reuter, J. S. & Mathews, D. H. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinform. 11, 129 (2010). · DOI
  37. van den Born, E., Posthuma, C. C., Gultyaev, A. P. & Snijder, E. J. Discontinuous subgenomic RNA synthesis in arteriviruses is guided by an RNA hairpin structure located in the genomic leader region. J. Virol. 79, 6312–6324 (2005). · DOI
  38. Oubridge, C., Ito, N., Evans, P. R., Teo, C. H. & Nagai, K. Crystal-structure at 1.92 angstrom resolution of the RNA-binding domain of the U1a spliceosomal protein complexed with an RNA Hairpin. Nature 372, 432–438 (1994). · DOI
  39. Clery, A., Blatter, M. & Allain, F. H. RNA recognition motifs: boring? Not quite. Curr. Opin. Struct. Biol. 18, 290–298 (2008). · DOI
  40. Das, P., Li, J., Royyuru, A. K. & Zhou, R. Free energy simulations reveal a double mutant avian H5N1 virus hemagglutinin with altered receptor binding specificity. J. Comput. Chem. 30, 1654–1663 (2009). · DOI
  41. Xia, Z., Huynh, T., Kang, S. G. & Zhou, R. H. Free-energy simulations reveal that both hydrophobic and polar interactions are important for influenza hemagglutinin antibody binding. Biophys. J. 102, 1453–1461 (2012). · DOI
  42. Zhou, R., Das, P. & Royyuru, A. K. Single mutation induced H3N2 hemagglutinin antibody neutralization: a free energy perturbation study. J. Phys. Chem. B 112, 15813–15820 (2008). · DOI
  43. Fu, H. et al. Accurate determination of protein:ligand standard binding free energies from molecular dynamics simulations. Nat. Protoc. 17, 1114–1141 (2022). · DOI
  44. Gapsys, V. & de Groot, B. L. Alchemical free energy calculations for nucleotide mutations in protein-DNA complexes. J. Chem. Theory Comput. 13, 6275–6289 (2017). · DOI
  45. Peiris, J. S. M. et al. Coronavirus as a possible cause of severe acute respiratory syndrome. Lancet 361, 1319–1325 (2003). · DOI
  46. Zaki, A. M., van Boheemen, S., Bestebroer, T. M., Osterhaus, A. D. & Fouchier, R. A. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N. Engl. J. Med. 367, 1814–1820 (2012). · DOI
  47. Moore, M. J. From birth to death: the complex lives of eukaryotic mRNAs. Science 309, 1514–1518 (2005). · DOI
  48. Dawson, W. K. & Bujnicki, J. M. Computational modeling of RNA 3D structures and interactions. Curr. Opin. Struct. Biol. 37, 22–28 (2016). · DOI
  49. Miao, Z. et al. RNA-puzzles round IV: 3D structure predictions of four ribozymes and two aptamers. RNA 26, 982–995 (2020). · DOI
  50. Sun, L. Z., Zhang, D. & Chen, S. J. Theory and modeling of RNA structure and interactions with metal lons and small molecules. Annu. Rev. Biophys. 46, 227–246 (2017). · DOI
  51. Best, R. B. et al. Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone phi, psi and side-chain chi(1) and chi(2) dihedral angles. J. Chem. Theory Comput. 8, 3257–3273 (2012). · DOI
  52. Denning, E. J., Priyakumar, U. D., Nilsson, L. & Mackerell, A. D. Jr. Impact of 2’-hydroxyl sampling on the conformational properties of RNA: update of the CHARMM all-atom additive force field for RNA. J. Comput. Chem. 32, 1929–1943 (2011). · DOI
  53. Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015). · DOI
  54. Zgarbova, 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). · DOI
  55. Fang, S. et al. GESS: a database of global evaluation of SARS-CoV-2/hCoV-19 sequences. Nucleic Acids Res. 49, D706–D714 (2021). · DOI
  56. Sreeramulu, S. et al. Exploring the druggability of conserved RNA regulatory elements in the SARS-CoV-2 genome. Angew. Chem. Int. Ed. Engl. 60, 19191–19200 (2021). · DOI
  57. Zhang, S. et al. Comparison of viral RNA-host protein interactomes across pathogenic RNA viruses informs rapid antiviral drug discovery for SARS-CoV-2. Cell Res. 32, 9–23 (2022). · DOI
  58. Gumna, J. et al. Computational pipeline for reference-free comparative analysis of RNA 3D structures applied to SARS-CoV-2 UTR models. Int. J. Mol. Sci. 23, 9630 (2022). · DOI
  59. Zhang, D., Li, J. & Chen, S. J. IsRNA1: de novo prediction and blind screening of RNA 3D structures. J. Chem. Theory Comput. 17, 1842–1857 (2021). · DOI
  60. Cao, S. & Chen, S. J. Physics-based de novo prediction of RNA 3D structures. J. Phys. Chem. B. 115, 4216–4226 (2011). · DOI
  61. Xu, X. & Chen, S. J. Hierarchical assembly of RNA three-dimensional structures based on loop templates. J. Phys. Chem. B. 122, 5327–5335 (2018). · DOI
  62. Pall, S. et al. Heterogeneous parallelization and acceleration of molecular dynamics simulations in GROMACS. J. Chem. Phys. 153, 134110 (2020). · DOI
  63. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983). · DOI
  64. Li, P., Song, L. F. & Merz, K. M. Jr. Systematic parameterization of monovalent lons employing the nonbonded model. J. Chem. Theory Comput. 11, 1645–1657 (2015). · DOI
  65. Hess, B., Bekker, H., Berendsen, H. J. C. & Fraaije, J. G. E. M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 18, 1463–1472 (1997). · DOI
  66. Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. J. Chem. Phys. 126, 014101 (2007). · DOI
  67. Daura, X. et al. Peptide folding: when simulation meets experiment. Angew. Chem. Int. Ed. 38, 236–240 (1999). · DOI
  68. Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 27–38 (1996). 33-38. · DOI · Paper page
  69. Pettersen, E. F. et al. UCSF Chimera–a visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612 (2004). · DOI
  70. Sali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993). · DOI · Paper page
  71. Zhao, B. et al. Structural basis for concerted recruitment and activation of IRF-3 by innate immune adaptor proteins. Proc. Natl Acad. Sci. USA 113, E3403–E3412 (2016). · DOI
  72. Bennett, C. H. Efficient estimation of free-energy differences from Monte-Carlo data. J. Comput. Phys. 22, 245–268 (1976). · DOI
  73. Wang, Z. et al. SWI2/SNF2 ATPase CHR2 remodels pri-miRNAs via Serrate to impede miRNA production. Nature. 557, 516–521 (2018). · DOI

Metadata

Authors
Zhang, D., Qiao, L., Lei, X., Dong, X., Tong, Y., Wang, J., Wang, Z., & Zhou, R.
Affiliations
  • Zhejiang University (CN)
  • State Key Laboratory of Plant Physiology and Biochemistry (CN)
  • Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
  • China Jiliang University (CN)
  • First Affiliated Hospital Zhejiang University (CN)
Corresponding author
  • Wang, Jianwei
  • Wang, Zhiye
  • Zhou, Ruhong
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2023-06-22
Volume
14
Issue
1
Pages
3715-3715
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-023-39410-8
PMC
PMC10287707
OpenAlex
W4381683186
Cited by
21
References
6
Citations by year
2026: 6 · 2025: 7 · 2024: 7 · 2023: 1
FWCI
4.35
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Natural Science Foundation of China (12104396, U1967217, 81930063)
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Keywords
SARS-CoV-2, RNA-protein interactions, cis-acting RNA elements, binding interface, mutagenesis, antisense oligonucleotides, Betacoronavirus
MeSH terms
T-Cell Intracellular Antigen-1, COVID-19, SARS-CoV-2, Humans, Protein Binding, RNA, Viral, Mutagenesis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review