References
  1. Hentze MW, Castello A, Schwarzl T, Preiss T. A brave new world of RNA-binding proteins. Nat Rev Mol Cell Biol. 2018 May;19(5):327-341. doi: 10.1038/nrm.2017.130. PMID: 29339797.
  2. Gebauer F, Schwarzl T, Valcárcel J, Hentze MW. RNA-binding proteins in human genetic disease. Nat Rev Genet. 2021 Mar;22(3):185-198. doi: 10.1038/s41576-020-00302-y. PMID: 33235359.
  3. Wang ZL, Li B, Luo YX, Lin Q, Liu SR, Zhang XQ, Zhou H, Yang JH, Qu LH. Comprehensive Genomic Characterization of RNA-Binding Proteins across Human Cancers. Cell Rep. 2018 Jan 2;22(1):286-298. doi: 10.1016/j.celrep.2017.12.035. PMID: 29298429.
  4. Neelamraju Y, Gonzalez-Perez A, Bhat-Nakshatri P, Nakshatri H, Janga SC. Mutational landscape of RNA-binding proteins in human cancers. RNA Biol. 2018 Jan 2;15(1):115-129. doi: 10.1080/15476286.2017.1391436. PMID: 29023197.
  5. Pereira B, Billaud M, Almeida R. RNA-Binding Proteins in Cancer: Old Players and New Actors. Trends Cancer. 2017 Jul;3(7):506-528. doi: 10.1016/j.trecan.2017.05.003. PMID: 28718405.
  6. Ray D, Kazan H, Cook KB, Weirauch MT, Najafabadi HS, Li X, et al. A compendium of RNA-binding motifs for decoding gene regulation. Nature. 2013 Jul 11;499(7457):172-7. doi: 10.1038/nature12311. PMID: 23846655.
  7. Dassi E. Handshakes and Fights: The Regulatory Interplay of RNA-Binding Proteins. Front Mol Biosci. 2017 Sep 29;4:67. doi: 10.3389/fmolb.2017.00067. PMID: 29034245.
  8. Burd CG, Dreyfuss G. Conserved structures and diversity of functions of RNA-binding proteins. Science. 1994 Jul 29;265(5172):615-21. doi: 10.1126/science.8036511. PMID: 8036511.
  9. Maris C, Dominguez C, Allain FH. The RNA recognition motif, a plastic RNA-binding platform to regulate post-transcriptional gene expression. FEBS J. 2005 May;272(9):2118-31. doi: 10.1111/j.1742-4658.2005.04653.x. PMID: 15853797.
  10. Wilson KA, Holland DJ, Wetmore SD. Topology of RNA-protein nucleobase-amino acid π-π interactions and comparison to analogous DNA-protein π-π contacts. RNA. 2016 May;22(5):696-708. doi: 10.1261/rna.054924.115. PMID: 26979279.
  11. Kooshapur H, Choudhury NR, Simon B, Mühlbauer M, Jussupow A, Fernandez N, et al. Structural basis for terminal loop recognition and stimulation of pri-miRNA-18a processing by hnRNP A1. Nat Commun. 2018 Jun 26;9(1):2479. doi: 10.1038/s41467-018-04871-9. PMID: 29946118.
  12. Mayeda A, Munroe SH, Cáceres JF, Krainer AR. Function of conserved domains of hnRNP A1 and other hnRNP A/B proteins. EMBO J. 1994 Nov 15;13(22):5483-95. PMID: 7957114.
  13. Nam Y, Chen C, Gregory RI, Chou JJ, Sliz P. Molecular basis for interaction of let-7 microRNAs with Lin28. Cell. 2011 Nov 23;147(5):1080-91. doi: 10.1016/j.cell.2011.10.020. PMID: 22078496.
  14. Abdelmohsen K, Gorospe M. RNA-binding protein nucleolin in disease. RNA Biol. 2012 Jun;9(6):799-808. doi: 10.4161/rna.19718. Epub 2012 May 23. PMID: 22617883.
  15. Daniely Y, Dimitrova DD, Borowiec JA. Stress-dependent nucleolin mobilization mediated by p53-nucleolin complex formation. Mol Cell Biol. 2002 Aug;22(16):6014-22. doi: 10.1128/MCB.22.16.6014-6022.2002. PMID: 12138209.
  16. Ginisty H, Amalric F, Bouvet P. Nucleolin functions in the first step of ribosomal RNA processing. EMBO J. 1998 Mar 2;17(5):1476-86. doi: 10.1093/emboj/17.5.1476. PMID: 9482744.
  17. Angelov D, Bondarenko VA, Almagro S, Menoni H, Mongélard F, Hans F, et al. Nucleolin is a histone chaperone with FACT-like activity and assists remodeling of nucleosomes. EMBO J. 2006 Apr 19;25(8):1669-79. doi: 10.1038/sj.emboj.7601046. PMID: 16601700
  18. Sengupta TK, Bandyopadhyay S, Fernandes DJ, Spicer EK. Identification of nucleolin as an AU-rich element binding protein involved in bcl-2 mRNA stabilization. J Biol Chem. 2004 Mar 19;279(12):10855-63. doi: 10.1074/jbc.M309111200. PMID: 14679209.
  19. Hung CY, Yang WB, Wang SA, Hsu TI, Chang WC, Hung JJ. Nucleolin enhances internal ribosomal entry site (IRES)-mediated translation of Sp1 in tumorigenesis. Biochim Biophys Acta. 2014 Dec;1843(12):2843-54. doi: 10.1016/j.bbamcr.2014.08.009. PMID: 25173817.
  20. Takagi M, Absalon MJ, McLure KG, Kastan MB. Regulation of p53 translation and induction after DNA damage by ribosomal protein L26 and nucleolin. Cell. 2005 Oct 7;123(1):49-63. doi: 10.1016/j.cell.2005.07.034. Erratum in: Cell. 2005 Nov 4;123(3):536-7. PMID: 16213212.
  21. Pickering BF, Yu D, Van Dyke MW. Nucleolin protein interacts with microprocessor complex to affect biogenesis of microRNAs 15a and 16. J Biol Chem. 2011 Dec 23;286(51):44095-44103. doi: 10.1074/jbc.M111.265439. PMID: 22049078.
  22. Bouvet P, Diaz JJ, Kindbeiter K, Madjar JJ, Amalric F. Nucleolin interacts with several ribosomal proteins through its RGG domain. J Biol Chem. 1998 Jul 24;273(30):19025-9. doi: 10.1074/jbc.273.30.19025. PMID: 9668083.
  23. Bhatt P, d’Avout C, Kane NS, Borowiec JA, Saxena A. Specific domains of nucleolin interact with Hdm2 and antagonize Hdm2-mediated p53 ubiquitination. FEBS J. 2012 Feb;279(3):370-83. doi: 10.1111/j.1742-4658.2011.08430.x. PMID: 22103682;
  24. Ginisty H, Sicard H, Roger B, Bouvet P. Structure and functions of nucleolin. J Cell Sci. 1999 Mar;112 ( Pt 6):761-72. PMID: 10036227.
  25. Chen J, Guo K, Kastan MB. Interactions of nucleolin and ribosomal protein L26 (RPL26) in translational control of human p53 mRNA. J Biol Chem. 2012 May 11;287(20):16467-76. doi: 10.1074/jbc.M112.349274. PMID: 22433872.
  26. Woo HH, Baker T, Laszlo C, Chambers SK. Nucleolin mediates microRNA-directed CSF-1 mRNA deadenylation but increases translation of CSF-1 mRNA. Mol Cell Proteomics. 2013 Jun;12(6):1661-77. doi: 10.1074/mcp.M112.025288. PMID: 23471483.
  27. Ishimaru D, Zuraw L, Ramalingam S, Sengupta TK, Bandyopadhyay S, Reuben A, et al. Mechanism of regulation of bcl-2 mRNA by nucleolin and A+U-rich element-binding factor 1 (AUF1). J Biol Chem. 2010 Aug 27;285(35):27182-27191. doi: 10.1074/jbc.M109.098830. PMID: 20571027.
  28. Abdelmohsen K, Tominaga K, Lee EK, Srikantan S, Kang MJ, Kim MM. Enhanced translation by Nucleolin via G-rich elements in coding and non-coding regions of target mRNAs. Nucleic Acids Res. 2011 Oct;39(19):8513-30. doi: 10.1093/nar/gkr488. PMID: 21737422.
  29. Chang Q, Bhatia D, Zhang Y, Meighan T, Castranova V, Shi X, et al. Incorporation of an internal ribosome entry site-dependent mechanism in arsenic-induced GADD45 alpha expression. Cancer Res. 2007 Jul 1;67(13):6146-54. doi: 10.1158/0008-5472.CAN-07-0867. PMID: 17616671.
  30. Ginisty H, Amalric F, Bouvet P. Two different combinations of RNA-binding domains determine the RNA binding specificity of nucleolin. J Biol Chem. 2001 Apr 27;276(17):14338-43. doi: 10.1074/jbc.M011120200. PMID: 11278842.
  31. Iden M, Fye S, Li K, Chowdhury T, Ramchandran R, Rader JS. The lncRNA PVT1 Contributes to the Cervical Cancer Phenotype and Associates with Poor Patient Prognosis. PLoS One. 2016 May 27;11(5):e0156274. doi: 10.1371/journal.pone.0156274. PMID: 27232880.
  32. Shen Y, Liu S, Fan J, Jin Y, Tian B, Zheng X, et al. Nuclear retention of the lncRNA SNHG1 by doxorubicin attenuates hnRNPC-p53 protein interactions. EMBO Rep. 2017 Apr;18(4):536-548. doi: 10.15252/embr.201643139. PMID: 28264987.
  33. Nana-Sinkam SP, Croce CM. MicroRNA regulation of tumorigenesis, cancer progression and interpatient heterogeneity: towards clinical use. Genome Biol. 2014;15(9):445. doi: 10.1186/s13059-014-0445-8. PMID: 25315999.
  34. Lund E, Güttinger S, Calado A, Dahlberg JE, Kutay U. Nuclear export of microRNA precursors. Science. 2004 Jan 2;303(5654):95-8. doi: 10.1126/science.1090599. PMID: 14631048.
  35. Bernstein E, Caudy AA, Hammond SM, Hannon GJ. Role for a bidentate ribonuclease in the initiation step of RNA interference. Nature. 2001 Jan 18;409(6818):363-6. doi: 10.1038/35053110. PMID: 11201747.
  36. Song L, Han MH, Lesicka J, Fedoroff N. Arabidopsis primary microRNA processing proteins HYL1 and DCL1 define a nuclear body distinct from the Cajal body. Proc Natl Acad Sci U S A. 2007 Mar 27;104(13):5437-42. doi: 10.1073/pnas.0701061104. PMID: 17369351.
  37. Bologna NG, Schapire AL, Palatnik JF. Processing of plant microRNA precursors. Brief Funct Genomics. 2013 Jan;12(1):37-45. doi: 10.1093/bfgp/els050. PMID: 23148323.
  38. Meier D, Kruse J, Buttlar J, Friedrich M, Zenk F, Boesler B, et al. Analysis of the Microprocessor in Dictyostelium: The Role of RbdB, a dsRNA Binding Protein. PLoS Genet. 2016 Jun 6;12(6):e1006057. doi: 10.1371/journal.pgen.1006057. PMID: 27272207.
  39. Arumugam S, Miller MC, Maliekal J, Bates PJ, Trent JO, Lane AN. Solution structure of the RBD1,2 domains from human nucleolin. J Biomol NMR. 2010 May;47(1):79-83. doi: 10.1007/s10858-010-9412-1. PMID: 20376532.
  40. Dang, W., Muto, Y., Inoue, M., Kigawa, T., Shirouzu, M., Terada, T., et al. Solution structure of the RRM_1 domain of NCL protein (Unpublished studies) RIKEN Structural Genomics/Proteomics Initiative (RSGI).
  41. Pichiorri F, Palmieri D, De Luca L, Consiglio J, You J, Rocci A, et al. In vivo NCL targeting affects breast cancer aggressiveness through miRNA regulation. J Exp Med. 2013 May 6;210(5):951-68. doi: 10.1084/jem.20120950. PMID: 23610125.
  42. Allain FH, Gilbert DE, Bouvet P, Feigon J. Solution structure of the two N-terminal RNA-binding domains of nucleolin and NMR study of the interaction with its RNA target. J Mol Biol. 2000 Oct 20;303(2):227-41. doi: 10.1006/jmbi.2000.4118. PMID: 11023788.
  43. Varani G, McClain WH. The G x U wobble base pair. A fundamental building block of RNA structure crucial to RNA function in diverse biological systems. EMBO Rep. 2000 Jul;1(1):18-23. doi: 10.1093/embo-reports/kvd001. PMID: 11256617.
  44. Li S, Nguyen TD, Nguyen TL, Nguyen TA. Mismatched and wobble base pairs govern primary microRNA processing by human Microprocessor. Nat Commun. 2020 Apr 21;11(1):1926. doi: 10.1038/s41467-020-15674-2. PMID: 32317642.
  45. Auyeung VC, Ulitsky I, McGeary SE, Bartel DP. Beyond secondary structure: primary-sequence determinants license pri-miRNA hairpins for processing. Cell. 2013 Feb 14;152(4):844-58. doi: 10.1016/j.cell.2013.01.031. PMID: 23415231.
  46. Fang W, Bartel DP. The Menu of Features that Define Primary MicroRNAs and Enable De Novo Design of MicroRNA Genes. Mol Cell. 2015 Oct 1;60(1):131-45. doi: 10.1016/j.molcel.2015.08.015. PMID: 26412306.
  47. Michlewski G, Cáceres JF. Post-transcriptional control of miRNA biogenesis. RNA. 2019 Jan;25(1):1-16. doi: 10.1261/rna.068692.118. PMID: 30333195.
  48. Michlewski G, Cáceres JF. Antagonistic role of hnRNP A1 and KSRP in the regulation of let-7a biogenesis. Nat Struct Mol Biol. 2010 Aug;17(8):1011-8. doi: 10.1038/nsmb.1874. PMID: 20639884.
  49. Viswanathan SR, Daley GQ, Gregory RI. Selective blockade of microRNA processing by Lin28. Science. 2008 Apr 4;320(5872):97-100. doi: 10.1126/science.1154040. PMID: 18292307.
  50. Kim KK, Yang Y, Zhu J, Adelstein RS, Kawamoto S. Rbfox3 controls the biogenesis of a subset of microRNAs. Nat Struct Mol Biol. 2014 Oct;21(10):901-10. doi: 10.1038/nsmb.2892. PMID: 25240799.
  51. Choudhury NR, de Lima Alves F, de Andrés-Aguayo L, Graf T, Cáceres JF, Rappsilber J, et al. Tissue-specific control of brain-enriched miR-7 biogenesis. Genes Dev. 2013 Jan 1;27(1):24-38. doi: 10.1101/gad.199190.112. PMID: 23307866.
  52. Partin AC, Zhang K, Jeong BC, Herrell E, Li S, Chiu W, et al. Cryo-EM Structures of Human Drosha and DGCR8 in Complex with Primary MicroRNA. Mol Cell. 2020 May 7;78(3):411-422.e4. doi: 10.1016/j.molcel.2020.02.016. PMID: 32220646.
  53. Pong SK, Gullerova M. Noncanonical functions of microRNA pathway enzymes - Drosha, DGCR8, Dicer and Ago proteins. FEBS Lett. 2018 Sep;592(17):2973-2986. doi: 10.1002/1873-3468.13196. PMID: 30025156.
  54. D’Avino C, Palmieri D, Braddom A, Zanesi N, James C, Cole S, et al. A novel fully human anti-NCL immunoRNase for triple-negative breast cancer therapy. Oncotarget. 2016 Dec 27;7(52):87016-87030. doi: 10.18632/oncotarget.13522. PMID: 27894092.
  55. Yazdian-Robati R, Bayat P, Oroojalian F, Zargari M, Ramezani M, Taghdisi SM, Abnous K. Therapeutic applications of AS1411 aptamer, an update review. Int J Biol Macromol. 2020 Jul 15;155:1420-1431. doi: 10.1016/j.ijbiomac.2019.11.118. PMID: 31734366.
  56. Ramos KS, Moore S, Runge I, Tavera-Garcia MA, Cascone I, Courty J, et al. The Nucleolin Antagonist N6L Inhibits LINE1 Retrotransposon Activity in Non-Small Cell Lung Carcinoma Cells. J Cancer. 2020 Jan 1;11(3):733-740. doi: 10.7150/jca.37776. PMID: 31942196.
  57. Birmpas C, Briand JP, Courty J, Katsoris P. The pseudopeptide HB-19 binds to cell surface nucleolin and inhibits angiogenesis. Vasc Cell. 2012 Dec 24;4(1):21. doi: 10.1186/2045-824X-4-21. PMID: 23265284.
  58. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2016 Jan 4;44(D1):D7-19. doi: 10.1093/nar/gkv1290. PMID: 26615191.
  59. Schultz J, Copley RR, Doerks T, Ponting CP, Bork P. SMART: a web-based tool for the study of genetically mobile domains. Nucleic Acids Res. 2000 Jan 1;28(1):231-4. doi: 10.1093/nar/28.1.231. PMID: 10592234
  60. El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019 Jan 8;47(D1):D427-D432. doi: 10.1093/nar/gky995. PMID: 30357350; PMCID: PMC6324024.
  61. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2017 Jan 4;45(D1):D158-D169. doi: 10.1093/nar/gkw1099. Nucleic Acids Res. 2018 Mar 16;46(5):2699. PMID: 27899622.
  62. Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, et al. InterPro: the integrative protein signature database. Nucleic Acids Res. 2009 Jan;37(Database issue):D211-5. doi: 10.1093/nar/gkn785. PMID: 18940856.
  63. Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011 Oct 11;7:539. doi: 10.1038/msb.2011.75. PMID: 21988835.
  64. Robert X, Gouet P. Deciphering key features in protein structures with the new ENDscript server. Nucleic Acids Res. 2014 Jul;42(Web Server issue):W320-4. doi: 10.1093/nar/gku316. PMID: 24753421.
  65. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, Gumienny R, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018 Jul 2;46(W1):W296-W303. doi: 10.1093/nar/gky427. PMID: 29788355.
  66. McGuffin LJ, Adiyaman R, Maghrabi AHA, Shuid AN, Brackenridge DA, Nealon JO, et al. IntFOLD: an integrated web resource for high performance protein structure and function prediction. Nucleic Acids Res. 2019 Jul 2;47(W1):W408-W413. doi: 10.1093/nar/gkz322. PMID: 31045208.
  67. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ. The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc. 2015 Jun;10(6):845-58. doi: 10.1038/nprot.2015.053. Epub 2015 May 7. PMID: 25950237.
  68. Kim DE, Chivian D, Baker D. Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W526-31. doi: 10.1093/nar/gkh468. PMID: 15215442.
  69. Xu D, Zhang Y. Ab initio protein structure assembly using continuous structure fragments and optimized knowledge-based force field. Proteins. 2012 Jul;80(7):1715-35. doi: 10.1002/prot.24065. Epub 2012 Apr 13. PMID: 22411565.
  70. Yang J, Yan R, Roy A, Xu D, Poisson J, Zhang Y. The I-TASSER Suite: protein structure and function prediction. Nat Methods. 2015 Jan;12(1):7-8. doi: 10.1038/nmeth.3213. PMID: 25549265.
  71. Eisenberg D, Lüthy R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol. 1997;277:396-404. doi: 10.1016/s0076-6879(97)77022-8. PMID: 9379925.
  72. Olechnovič K, Venclovas Č. VoroMQA: Assessment of protein structure quality using interatomic contact areas. Proteins. 2017 Jun;85(6):1131-1145. doi: 10.1002/prot.25278. PMID: 28263393.
  73. Wiederstein M, Sippl MJ. ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007 Jul;35(Web Server issue):W407-10. doi: 10.1093/nar/gkm290. PMID: 17517781.
  74. Uziela K, Menéndez Hurtado D, Shu N, Wallner B, Elofsson A. ProQ3D: improved model quality assessments using deep learning. Bioinformatics. 2017 May 15;33(10):1578-1580. doi: 10.1093/bioinformatics/btw819. PMID: 28052925.
  75. Xu D, Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys J. 2011 Nov 16;101(10):2525-34. doi: 10.1016/j.bpj.2011.10.024. PMID: 22098752.
  76. Krivov GG, Shapovalov MV, Dunbrack RL Jr. Improved prediction of protein side-chain conformations with SCWRL4. Proteins. 2009 Dec;77(4):778-95. doi: 10.1002/prot.22488. PMID: 19603484.
  77. Chirayil S, Wu Q, Amezcua C, Luebke KJ. NMR characterization of an oligonucleotide model of the miR-21 pre-element. PLoS One. 2014 Sep 24;9(9):e108231. doi: 10.1371/journal.pone.0108231. PMID: 25250627.
  78. Shortridge MD, Walker MJ, Pavelitz T, Chen Y, Yang W, Varani G. A Macrocyclic Peptide Ligand Binds the Oncogenic MicroRNA-21 Precursor and Suppresses Dicer Processing. ACS Chem Biol. 2017 Jun 16;12(6):1611-1620. doi: 10.1021/acschembio.7b00180. PMID: 28437065.
  79. Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019 Jan 8;47(D1):D155-D162. doi: 10.1093/nar/gky1141. PMID: 30423142.
  80. Reuter JS, Mathews DH. RNAstructure: software for RNA secondary structure prediction and analysis. BMC Bioinformatics. 2010 Mar 15;11:129. doi: 10.1186/1471-2105-11-129. PMID: 20230624.
  81. Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL. The Vienna RNA websuite. Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W70-4. doi: 10.1093/nar/gkn188. PMID: 18424795.
  82. Parisien M, Major F. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature. 2008 Mar 6;452(7183):51-5. doi: 10.1038/nature06684. PMID: 18322526.
  83. Sato K, Hamada M, Asai K, Mituyama T. CENTROIDFOLD: a web server for RNA secondary structure prediction. Nucleic Acids Res. 2009 Jul;37(Web Server issue):W277-80. doi: 10.1093/nar/gkp367. PMID: 19435882.
  84. Singh J, Hanson J, Paliwal K, Zhou Y. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning. Nat Commun. 2019 Nov 27;10(1):5407. doi: 10.1038/s41467-019-13395-9. PMID: 31776342.
  85. Biesiada M, Purzycka KJ, Szachniuk M, Blazewicz J, Adamiak RW. Automated RNA 3D Structure Prediction with RNAComposer. Methods Mol Biol. 2016;1490:199-215. doi: 10.1007/978-1-4939-6433-8_13. PMID: 27665601.
  86. Sharma S, Ding F, Dokholyan NV. iFoldRNA: three-dimensional RNA structure prediction and folding. Bioinformatics. 2008 Sep 1;24(17):1951-2. doi: 10.1093/bioinformatics/btn328. PMID: 18579566.
  87. Garcia-Martin JA, Clote P, Dotu I. RNAiFold: a web server for RNA inverse folding and molecular design. Nucleic Acids Res. 2013 Jul;41(Web Server issue):W465-70. doi: 10.1093/nar/gkt280. PMID: 23700314.
  88. Wang J, Wang J, Huang Y, Xiao Y. 3dRNA v2.0: An Updated Web Server for RNA 3D Structure Prediction. Int J Mol Sci. 2019 Aug 23;20(17):4116. doi: 10.3390/ijms20174116. PMID: 31450739.
  89. Magnus M, Boniecki MJ, Dawson W, Bujnicki JM. SimRNAweb: a web server for RNA 3D structure modeling with optional restraints. Nucleic Acids Res. 2016 Jul 8;44(W1):W315-9. doi: 10.1093/nar/gkw279. PMID: 27095203.
  90. Chen VB, Arendall WB 3rd, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, et al. MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr. 2010 Jan;66(Pt 1):12-21. doi: 10.1107/S0907444909042073. PMID: 20057044.
  91. Antczak M, Zok T, Osowiecki M, Popenda M, Adamiak RW, Szachniuk M. RNAfitme: a webserver for modeling nucleobase and nucleoside residue conformation in fixed-backbone RNA structures. BMC Bioinformatics. 2018 Aug 22;19(1):304. doi: 10.1186/s12859-018-2317-9. PMID: 30134831.
  92. Yan Y, Zhang D, Zhou P, Li B, Huang SY. HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy. Nucleic Acids Res. 2017 Jul 3;45(W1):W365-W373. doi: 10.1093/nar/gkx407. PMID: 28521030.
  93. Tuszynska I, Magnus M, Jonak K, Dawson W, Bujnicki JM. NPDock: a web server for protein-nucleic acid docking. Nucleic Acids Res. 2015 Jul 1;43(W1):W425-30. doi: 10.1093/nar/gkv493. PMID: 25977296.
  94. DeLano WL. The PyMOL Molecular Graphics System. 2008. http://pymol.org.
  95. Laskowski RA, Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: Structural summaries of PDB entries. Protein Sci. 2018 Jan;27(1):129-134. doi: 10.1002/pro.3289. PMID: 28875543.
  96. Livi CM, Klus P, Delli Ponti R, Tartaglia GG. catRAPID signature: identification of ribonucleoproteins and RNA-binding regions. Bioinformatics. 2016 Mar 1;32(5):773-5. doi: 10.1093/bioinformatics/btv629. PMID: 26520853.Figure legends
Figure 1 . NCL tandem RBD models display high structural similarities with existing individual NCL RBD crystal structures. A)Superposition of the modeled RBD1-4 (pale green) with individual crystal structures of NCL RBD1 (PDB ID: 1FJ7 ;marine blue), RBD2 (PDB ID: 1FJC deep salmon), RBD3 (PDB ID: 2FC9; Lightblue), and RBD4 (PDB ID: 2FC8; deep olive). RMSD scores for RBD1, RBD2, RBD3, and RBD4 are 3.594 Å, 2.324 Å, 1.775 Å, and 2.442 Å, respectively. B) Superposition of the modeled RBD3-4 (pale green) with individual structures of NCL RBD3 (PDB ID: 2FC9; Lightblue), and RBD4 (PDB ID: 2FC8; deep olive) RMSD scores for RBD3 and RBD4 are 0.956 Å and 1.753 Å, respectively.
Figure 2. Structural models of the miRNAs analyzed in this study. Top ranked models for all 6 pri-miRNA (orange backbone with bases shown in blue). Alternative miRNA models that exhibited comparative evaluation profiles are shown in dark red.
Figure 3. Representative docking poses exhibiting binding mode 1 involving RBD3-4. A) Complete docking scenarios. B)Zoomed-in insets with intermolecular distances between residues indicated. miRNA molecule backbone (orange), RBD1 (marine blue), RBD2 (deep salmon), RBD3 (light blue), RBD4 (deep olive). The linker regions between RBDs 1 and 2, 3 and 4 are colored in green while the linker between RBDs 2 and 3 are colored in yellow. Interacting nucleotides on the miRNA and NCL-RBDs are indicated with deep teal and hot pink, respectively.
Figure 4. Representative docking poses exhibiting binding mode 2 involving RBD4. A) Complete docking scenarios. B)Zoomed in version with intermolecular distances between residues indicated. RBD1 (marine blue), RBD2 (deep salmon), RBD3 (light blue), RBD4 (deep olive The linker regions between RBDs 1 and 2, 3 and 4 are colored in green while the linker between RBDs 2 and 3 are colored in yellow. Interacting nucleotides on the miRNA and NCL-RBDs are indicated with deep teal and hot pink, respectively.
Figure 5. Representative docking poses exhibiting binding mode 3 involving RBD234. A) Complete docking scenarios. B)Zoomed in version with intermolecular distances between residues indicated. RBD1 (marine blue), RBD2 (deep salmon), RBD3 (light blue), RBD4 (deep olive). The linker regions between RBDs 1 and 2, 3 and 4 are colored in green while the linker between RBDs 2 and 3 are colored in yellow. Interacting nucleotides on the miRNA and NCL-RBDs are indicated with deep teal and hot pink, respectively.
Figure 6. NCL residues predicted to interact with miRNAs A) Comparison of hnRNP A1 RBDs with NCL RBDs (PDB ID: 6DCL). Beta strands and RNP motifs are indicated with arrows. Known hnRNP A1 residues that interact with ssDNA and miR-18 are indicated in light brown background. NCL residues conserved in the equivalent positions are indicated in yellow background. Conserved residues are indicated in bold and black background. B) Summary of NCL-RBD residues most frequently predicted to interact with miRNA based on docking . Different types of interactions are indicated with background colors C) Summary of predicted NCL-miRNA binding modes and a comparison with hnRNP A1-miRNA binding model. 1) Known hnRNP A1-mir18 interaction residues. 2) Proposed NCL-miRNA binding mode 1 where both RBD3&RBD4 are involved in the interactions. 3) Proposed NCL-miRNA binding mode 2 where RBD4 alone interacts with miRNA4) Proposed NCL-miRNA binding mode 3 which RBD3&4 and a single residue from RBD2. Beta sheets are numbered on top, and residues predicted to interact with miRNA are indicated in red. Newly identified residues are indicated in black. The numbers indicate the position of these residues in the corresponding beta strands.
Figure 7. Mapping of miRNA residues interacting with NCL. A)NCL contacts miRNA molecules at the UGU/GUG motifs closest to the loop.B) NCL contacts miRNA molecules on UGU/GUG motifs distant from the loop and closer to GHG/CUC motifs. C) NCL contacts longer miRNA molecules on UGU/GUG motifs slightly distant from the apical loop and closer to the loop stem. Beta strands are numbered on top of the arrowheads. RBD1 (marine blue), RBD2 (deep salmon), RBD3 (light blue), RBD4 (deep olive), and linker loops between RBDs (neon green). UGU/GUG motifs on miRNA that NCL interacts are indicated in green. miRNA residues that NCL interacts with are but is not part of an UGG/GUG motif is indicated in purple. UGU/GUG motifs on miRNA that NCL is not interacting are indicated in red. GHG/CUC motifs are indicated in orange.
Figure 8. Canonical and proposed models of NCL, Drosha, and DGCR8 interactions with pri-miRNA molecules. A) Canonical model involving Drosha & DGCR8-1and 252 B)Hypothetical model A where NCL-RBDs could replace DGCR8 – 2 C)Hypothetical model B where all MPC proteins and either one or both NCL-RBDs interact with the pri-miRNA molecule. NCL-RBD3&4 are colored in light blue and deep olive, respectively. DROSHA is light green. DGCR8-1 is red, and DGCR8-2 is orange.