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Abstract

In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion–RNA and ligand–RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data–directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.

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2017-05-22
2024-03-29
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Literature Cited

  1. Aalberts DP, Hodas NO. 1.  2005. Asymmetry in RNA pseudoknots: observation and theory. Nucleic Acids Res. 33:2210–14 [Google Scholar]
  2. Anthony PC, Sim AYL, Chu VB, Doniach S, Block SM, Herschlag D. 2.  2012. Electrostatics of nucleic acid folding under conformational constraint. J. Am. Chem. Soc. 134:4607–14 [Google Scholar]
  3. Auffinger P, Bielecki L, Westhof E. 3.  2003. The Mg2+ binding sites of the 5S rRNA loop E motif as investigated by molecular dynamics simulations. Chem. Biol. 10:551–61 [Google Scholar]
  4. Bai Y, Chu VB, Lipfert J, Pande VS, Herschlag D, Doniach S. 4.  2008. Critical assessment of nucleic acid electrostatics via experimental and computational investigation of an unfolded state ensemble. J. Am. Chem. Soc. 130:12334–41 [Google Scholar]
  5. Bai Y, Greenfeld M, Herschlag D. 5.  2007. Quantitative and comprehensive decomposition of the ion atmosphere around nucleic acids. J. Am. Chem. Soc. 129:14981–88 [Google Scholar]
  6. Bailor MH, Mustoe AM, Brooks CL III, Al-Hashimi HM. 6.  2011. Topological constraints: using RNA secondary structure to model 3D conformation, folding pathways, and dynamic adaptation. Curr. Opin. Struct. Biol. 21:296–305 [Google Scholar]
  7. Bailor MH, Sun X, Al-Hashimi HM. 7.  2010. Topology links RNA secondary structure with global conformation, dynamics, and adaptation. Science 327:202–6 [Google Scholar]
  8. Bernauer J, Huang X, Sim AYL, Levitt M. 8.  2011. Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation. RNA 17:1066–75 [Google Scholar]
  9. Bindewald E, Kluth T, Shapiro BA. 9.  2010. CyloFold: secondary structure prediction including pseudoknots. Nucleic Acids Res. 38:W368–72 [Google Scholar]
  10. Bizarro CV, Alemany A, Ritort F. 10.  2012. Non-specific binding of Na+and Mg2+ to RNA determined by force spectroscopy methods. Nucleic Acids Res. 40:6922–35 [Google Scholar]
  11. Bon M, Orland H. 11.  2010. Prediction of RNA secondary structures with pseudoknots. Physica A 389:2987–92 [Google Scholar]
  12. Boniecki MJ, Lach G, Dawson WK, Tomala K, Lukasz P. 12.  et al. 2016. SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res. 44:e63 [Google Scholar]
  13. Borukhov I, Andelman D, Orland H. 13.  1997. Steric effects in electrolytes: a modified Poisson-Boltzmann equation. Phys. Rev. Lett. 79:435–38 [Google Scholar]
  14. Bowman GR, Huang X, Yao Y, Sun J, Carlsson G. 14.  et al. 2008. Structural insight into RNA hairpin folding intermediates. J. Am. Chem. Soc. 130:9676–78 [Google Scholar]
  15. Brion P, Westhof E. 15.  1997. Hierarchy and dynamics of RNA folding. Annu. Rev. Biophys. Biomol. Struct. 26:113–37 [Google Scholar]
  16. Brodsky AS, Williamson JR. 16.  1997. Solution structure of the HIV-2 TAR-argininamide complex. J. Mol. Biol. 267:624–39 [Google Scholar]
  17. Brooks BR, Bruccoeri RE, Olafson BD, States DJ, Swaminathan S, Karplus M. 17.  1983. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 4:187–217 [Google Scholar]
  18. Butcher SE, Pyle AM. 18.  2011. The molecular interactions that stabilize RNA tertiary structure: RNA motifs, patterns, and networks. Acc. Chem. Res. 44:1302–11 [Google Scholar]
  19. Cao S, Chen SJ. 19.  2006. Predicting RNA pseudoknot folding thermodynamics. Nucleic Acids Res. 34:2634–52 [Google Scholar]
  20. Cao S, Chen SJ. 20.  2009. Predicting structures and stabilities for H-type pseudoknots with interhelix loops. RNA 15:696–706 [Google Scholar]
  21. Cao S, Chen SJ. 21.  2011. Physics-based de novo prediction of RNA 3D structures. J. Phys. Chem. B 115:4216–26 [Google Scholar]
  22. Cao S, Giedroc DP, Chen SJ. 22.  2010. Predicting loop-helix tertiary structural contacts in RNA pseudoknots. RNA 16:538–52 [Google Scholar]
  23. Cao S, Xu X, Chen SJ. 23.  2014. Predicting structure and stability for RNA complexes with intermolecular loop-loop base-pairing. RNA 20:835–45 [Google Scholar]
  24. Capriotti E, Norambuena T, Marti-Renom MA, Melo F. 24.  2011. All-atom knowledge-based potential for RNA structure prediction and assessment. Bioinformatics 27:1086–93 [Google Scholar]
  25. Carnevali M, Parsons J, Wyles DL, Hermann T. 25.  2010. A modular approach to synthetic RNA binders of the hepatitis C virus internal ribosome entry site. ChemBioChem 11:1364–67 [Google Scholar]
  26. Case DA, Betz RM, Botello-Smith W, Cerutti DS, Cheatham TE III. 26.  et al. 2016. AMBER 2016. Molecular Simulation Software. University of California San Francisco: http://ambermd.org/ [Google Scholar]
  27. Chakraborty D, Collepardo-Guevara R, Wales DJ. 27.  2014. Energy landscapes, folding mechanisms, and kinetics of RNA tetraloop hairpins. J. Am. Chem. Soc. 136:18052–61 [Google Scholar]
  28. Chen AA, Draper DE, Pappu RV. 28.  2009. Molecular simulation studies of monovalent counterion-mediated interactions in a model RNA kissing loop. J. Mol. Biol. 390:805–19 [Google Scholar]
  29. Chen AA, Garca AE. 29.  2013. High-resolution reversible folding of hyperstable RNA tetraloops using molecular dynamics simulations. PNAS 110:16820–25 [Google Scholar]
  30. Chen H, Meisburger SP, Pabit SA, Sutton JL, Webb WW, Pollack L. 30.  2012. Ionic strength-dependent persistence lengths of single-stranded RNA and DNA. PNAS 17:799–804 [Google Scholar]
  31. Chu V, Bai Y, Lipfert J, Herschlag D, Doniach S. 31.  2007. Evaluation of ion binding to DNA duplexes using a size-modified Poisson–Boltzmann theory. Biophys. J. 93:3202–9 [Google Scholar]
  32. Cordero P, Kladwang W, VanLang CC, Das R. 32.  2012. Quantitative dimethyl sulfate mapping for automated RNA secondary structure inference. Biochemistry 51:7037–39 [Google Scholar]
  33. Cragnolini T, Derreumaux P, Pasquali S. 33.  2015. Ab initio RNA folding. J. Phys. Condens. Matter 27:233102 [Google Scholar]
  34. Cragnolini T, Laurin Y, Derreumaux P, Pasquali S. 34.  2015. Coarse-grained HiRE-RNA model for ab initio RNA folding beyond simple molecules, including noncanonical and multiple base pairings. J. Chem. Theory Comput. 11:3510–22 [Google Scholar]
  35. Cromie MJ, Shi Y, Latifi T, Groisman EA. 35.  2006. An RNA sensor for intracellular Mg2+. Cell 125:71–84 [Google Scholar]
  36. Cruz JA, Blanchet MF, Boniecki M, Bujnicki JM, Chen SJ. 36.  et al. 2012. RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. RNA 18:610–25 [Google Scholar]
  37. Dann CE. Wakeman CA, Sieling CL, Baker SC, Irnov I, Winkler WC. 37.  III, 2007. Structure and mechanism of a metal-sensing regulatory RNA. Cell 130:878–92 [Google Scholar]
  38. Das R, Baker D. 38.  2007. Automated de novo prediction of native-like RNA tertiary structures. PNAS 104:14664–69 [Google Scholar]
  39. Das R, Karanicolas J, Baker D. 39.  2010. Atomic accuracy in predicting and designing noncanonical RNA structure. Nat. Methods 7:291–94 [Google Scholar]
  40. Das R, Kudaravalli M, Jonikas M, Laederach A, Fong R. 40.  et al. 2008. Structural inference of native and partially folded RNA by high-throughput contact mapping. PNAS 105:4144–49 [Google Scholar]
  41. Dawson WK, Bujnicki JM. 41.  2016. Computational modeling of RNA 3D structures and interactions. Curr. Opin. Struct. Biol. 37:22–28 [Google Scholar]
  42. Deigan KE, Li TW, Mathews DH, Weeks KM. 42.  2009. Accurate SHAPE-directed RNA structure determination. PNAS 106:97–102 [Google Scholar]
  43. Denesyuk NA, Thirumalai D. 43.  2011. Crowding promotes the switch from hairpin to pseudoknot conformation in human telomerase RNA. J. Am. Chem. Soc. 133:11858–61 [Google Scholar]
  44. Denesyuk NA, Thirumalai D. 44.  2013. Coarse-grained model for predicting RNA folding thermodynamics. J. Phys. Chem. B 117:4901–11 [Google Scholar]
  45. Denesyuk NA, Thirumalai D. 45.  2015. How do metal ions direct ribozyme folding?. Nat. Chem. 7:793–801 [Google Scholar]
  46. Ding F, Lavender CA, Weeks KM, Dokholyan NV. 46.  2012. Three-dimensional RNA structure refinement by hydroxyl radical probing. Nat. Methods 9:603–8 [Google Scholar]
  47. Ding F, Sharma S, Chalasani P, Demidov VV, Broude NE, Dokholyan NV. 47.  2008. Ab initio RNA folding by discrete molecular dynamics: from structure prediction to folding mechanisms. RNA 14:1164–73 [Google Scholar]
  48. Dirks RM, Pierce NA. 48.  2003. A partition function algorithm for nucleic acid secondary structure including pseudoknots. J. Comput. Chem. 24:1664–77 [Google Scholar]
  49. Do TN, Ippoliti E, Parrinello M. 49.  2012. Counterion redistribution upon binding of a Tat-protein mimic to HIV-1 TAR RNA. J. Chem. Theory Comput. 8:688–94 [Google Scholar]
  50. Edwards TE, Ferré-D'Amaré AR. 50.  2006. Crystal structures of the thi-box riboswitch bound to thiamine pyrophosphate analogs reveal adaptive RNA-small molecule recognition. Structure 14:1459–68 [Google Scholar]
  51. Ellis RJ. 51.  2001. Macromolecular crowding: obvious but underappreciated. Trends Biochem. Sci. 26:597–604 [Google Scholar]
  52. Ennifar E, Paillart JC, Bodlenner A, Walter P, Weibel JM. 52.  et al. 2006. Targeting the dimerization initiation site of HIV-1 RNA with aminoglycosides: from crystal to cell. Nucleic Acids Res. 34:2328–39 [Google Scholar]
  53. Eriksson ES, Joshi L, Billeter M, Eriksson LA. 53.  2014. De novo tertiary structure prediction using RNA123—benchmarking and application to Macugen. J. Mol. Model. 20:2389 [Google Scholar]
  54. Flores SC, Wan Y, Russell R, Altman RB. 54.  2010. Predicting RNA structure by multiple template homology modeling. Pac. Symp. Biocomput. 2010:216–27 [Google Scholar]
  55. Frellsen J, Moltke I, Thiim M, Mardia KV, Ferkinghoff-Borg J, Hamelryck T. 55.  2009. A probabilistic model of RNA conformational space. PLOS Comput. Biol. 5:e1000406 [Google Scholar]
  56. Gebala M, Giambasu GM, Lipfert J, Bisaria N, Bonilla S. 56.  et al. 2015. Cation–anion interactions within the nucleic acid ion atmosphere revealed by ion counting. J. Am. Chem. Soc. 137:14705–15 [Google Scholar]
  57. Giambasu GM, Gebala MK, Panteva MT, Luchko T, Case DA, York DM. 57.  2015. Competitive interaction of monovalent cations with DNA from 3D-RISM. Nucleic Acids Res. 43:8405–15 [Google Scholar]
  58. Gilbert SD, Mediatore SJ, Batey RT. 58.  2006. Modified pyrimidines specifically bind the purine riboswitch. J. Am. Chem. Soc. 128:14214–15 [Google Scholar]
  59. Greenleaf WJ, Frieda KL, Foster DA, Woodside MT, Block SM. 59.  2008. Direct observation of hierarchical folding in single riboswitch aptamers. Science 319:630–33 [Google Scholar]
  60. Griffiths-Jones S, Bateman A, Marshall M, Khanna A, Eddy SR. 60.  2003. Rfam: an RNA family database. Nucleic Acids Res. 31:439–41 [Google Scholar]
  61. Guilbert C, James TL. 61.  2008. Docking to RNA via root-mean-square-deviation-driven energy minimization with flexible ligands and flexible targets. J. Chem. Inf. Model. 48:1257–68 [Google Scholar]
  62. Hajdin CE, Bellaousov S, Huggins W, Leonard CW, Mathews DH, Weeks KM. 62.  2013. Accurate SHAPE-directed RNA secondary structure modeling, including pseudoknots. PNAS 110:5498–503 [Google Scholar]
  63. Hayes RL, Noel JK, Mandic A, Whitford PC, Sanbonmatsu KY. 63.  et al. 2015. Generalized Manning condensation model captures the RNA ion atmosphere. Phys. Rev. Lett. 114:258105 [Google Scholar]
  64. Hayes RL, Noel JK, Mohanty U, Whitford PC, Hennelly SP. 64.  et al. 2012. Magnesium fluctuations modulate RNA dynamics in the SAM-I riboswitch. J. Am. Chem. Soc. 134:C12043–53 [Google Scholar]
  65. He Y, Maciejczyk M, Odziej S, Scheraga HA, Liwo A. 65.  2013. Mean-field interactions between nucleic-acid-base dipoles can drive the formation of a double helix. Phys. Rev. Lett. 110:098101 [Google Scholar]
  66. Homan PJ, Favorov OV, Lavender CA, Kursun O, Ge X. 66.  et al. 2014. Single-molecule correlated chemical probing of RNA. PNAS 111:13858–63 [Google Scholar]
  67. Jared HD, Trenton RF, Marco T, Samuel EB. 67.  2007. Role of metal ions in the tetraloop–receptor complex as analyzed by NMR. RNA 13:79–86 [Google Scholar]
  68. Jonikas MA, Radmer RJ, Laederach A, Das R, Pearlman S. 68.  et al. 2009. Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA 15:189–99 [Google Scholar]
  69. Jossinet F, Ludwig TE, Westhof E. 69.  2010. Assemble: an interactive graphical tool to analyze and build RNA architectures at the 2D and 3D levels. Bioinformatics 26:2057–59 [Google Scholar]
  70. Kerpedjiev P, Hner Zu Siederdissen C, Hofacker IL. 70.  2015. Predicting RNA 3D structure using a coarse-grain helix-centered model. RNA 21:1110–21 [Google Scholar]
  71. Kilburn D, Roh JH, Behrouzi R, Briber RM, Woodson SA. 71.  2013. Crowders perturb the entropy of RNA energy landscapes to favor folding. J. Am. Chem. Soc. 135:10055–63 [Google Scholar]
  72. Kim N, Zahran M, Schlick T. 72.  2015. Computational prediction of riboswitch tertiary structures including pseudoknots by RAGTOP: a hierarchical graph sampling approach. Methods Enzymol. 553:115–35 [Google Scholar]
  73. Kladwang W, VanLang CC, Cordero P, Das R. 73.  2011. A two-dimensional mutate-and-map strategy for non-coding RNA structure. Nat. Chem. 3:954–62 [Google Scholar]
  74. Kladwang W, VanLang CC, Cordero P, Das R. 74.  2011. Understanding the errors of SHAPE-directed RNA structure modeling. Biochemistry 50:8049–56 [Google Scholar]
  75. Korolev N, Lyubartsev AP, Rupprecht A, Nordenskiöld L. 75.  1999. Competitive binding of Mg2+, Ca2+, Na+, and K+ ions to DNA in oriented DNA fibers: experimental and Monte Carlo simulation results. Biophys. J. 77:2736–49 [Google Scholar]
  76. Krasovska MV, Sefcikova J, Sponer J. 76.  2006. Cations and hydration in catalytic RNA: molecular dynamics of the Hepatitis Delta virus ribozyme. Biophys. J. 91:626–38 [Google Scholar]
  77. Krüger DM, Bergs J, Kazemi S, Gohlke H. 77.  2011. Target flexibility in RNA-ligand docking modeled by elastic potential grids. ACS Med. Chem. Lett. 2:489–93 [Google Scholar]
  78. Kwok CK, Ding Y, Tang Y, Assmann SM, Bevilacqua PC. 78.  2013. Determination of in vivo RNA structure in low-abundance transcripts. Nat. Commun. 4:2971 [Google Scholar]
  79. Laing C, Schlick T. 79.  2010. Computational approaches to 3D modeling of RNA. J. Phys. Condens. Matter 22:283101 [Google Scholar]
  80. Lang PT, Brozell SR, Mukherjee S, Pettersen EF, Meng EC. 80.  et al. 2009. DOCK 6: combining techniques to model RNA-small molecule complexes. RNA 15:1219–30 [Google Scholar]
  81. Leamy KA, Assmann SM, Mathews DH, Bevilacqua PC. 81.  2016. Bridging the gap between in vitro and in vivo RNA folding. Q. Rev. Biophys. 49:e10 [Google Scholar]
  82. Lee TS, Radak BK, Harris ME, York DM. 82.  2016. A two-metal-ion-mediated conformational switching pathway for HDV ribozyme activation. ACS Catal. 6:1853–69 [Google Scholar]
  83. Leipply D, Draper DE. 83.  2011. Effects of Mg2+ on the free energy landscape for folding a purine riboswitch RNA. Biochemistry 50:2790–99 [Google Scholar]
  84. Leipply D, Draper DE. 84.  2011. Evidence for a thermodynamically distinct Mg2+ ion associated with formation of an RNA tertiary structure. J. Am. Chem. Soc. 133:13397–405 [Google Scholar]
  85. Leontis NB, Lescoute A, Westhof E. 85.  2006. The building blocks and motifs of RNA architecture. Curr. Opin. Struct. Biol. 16:279–87 [Google Scholar]
  86. Leontis NB, Stombaugh J, Westhof E. 86.  2002. The non-Watson-Crick base pairs and their associated isostericity matrices. Nucleic Acids Res. 30:3497–531 [Google Scholar]
  87. Lescoute A, Westhof E. 87.  2006. Topology of three-way junctions in folded RNAs. RNA 12:83–93 [Google Scholar]
  88. Li P, Merz KM Jr. 88.  2014. Taking into account the ion-induced dipole interaction in the non-bonded model of ions. J. Chem. Theory Comput. 10:289–97 [Google Scholar]
  89. Lipfert J, Sim AY, Herschlag D, Doniach S. 89.  2010. Dissecting electrostatic screening, specific ion binding, and ligand binding in an energetic model for glycine riboswitch folding. RNA 16:708–19 [Google Scholar]
  90. Liu L, Chen SJ. 90.  2010. Computing the conformational entropy for RNA folds. J. Chem. Phys. 132:235104 [Google Scholar]
  91. Liu Y, Holmstrom E, Zhang J, Yu P, Wang J. 91.  et al. 2015. Synthesis and applications of RNAs with position-selective labelling and mosaic composition. Nature 522:368–72 [Google Scholar]
  92. Lorenz R, Luntzer D, Hofacker IL, Stadler PF, Wolfinger MT. 92.  2016. SHAPE directed RNA folding. Bioinformatics 32:145–47 [Google Scholar]
  93. Lorenz R, Wolfinger MT, Tanzer A, Hofacker IL. 93.  2016. Predicting RNA secondary structures from sequence and probing data. Methods 103:86–98 [Google Scholar]
  94. Low JT, Weeks KM. 94.  2010. SHAPE-directed RNA secondary structure prediction. Methods 52:150–58 [Google Scholar]
  95. Lu C, Smith AM, Fuchs RT, Ding F, Rajashankar K. 95.  et al. 2008. Crystal structures of the SAM-III/SMK riboswitch reveal the SAM-dependent translation inhibition mechanism. Nat. Struct. Mol. Biol. 15:1076–83 [Google Scholar]
  96. Mak CH, Henke PS. 96.  2013. Ions and RNAs: free energies of counterion-mediated RNA fold stabilities. J. Chem. Theory Comput. 9:621–39 [Google Scholar]
  97. Markham GD, Glusker JP, Bock CW. 97.  2002. The arrangement of first- and second-sphere water molecules in divalent magnesium complexes: results from molecular orbital and density functional theory and from structural crystallography. J. Phys. Chem. B 106:5118–34 [Google Scholar]
  98. Martinez HM, Maizel JV Jr., Shapiro BA. 98.  2008. RNA2D3D: a program for generating, viewing, and comparing 3-dimensional models of RNA. J. Biomol. Struct. Dyn. 25:669–83 [Google Scholar]
  99. Mathews DH, Turner DH. 99.  2006. Prediction of RNA secondary structure by free energy minimization. Curr. Opin. Struct. Biol. 16:270–78 [Google Scholar]
  100. Meng Y, Aalberts DP. 100.  2013. Free energy cost of stretching mRNA hairpin loops inhibits small RNA binding. Biophys. J. 104:482–87 [Google Scholar]
  101. Miao Z, Adamiak RW, Blanchet MF, Boniecki M, Bujnicki JM. 101.  et al. 2015. RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures. RNA 21:1066–84 [Google Scholar]
  102. Miao Z, Westhof E. 102.  2017. RNA structure: advances and assessment of 3D structure prediction. Annu. Rev. Biophys. 46:483–503 [Google Scholar]
  103. Misra VK, Draper DE. 103.  1998. On the role of magnesium ions in RNA stability. Biopolymers 48:113–35 [Google Scholar]
  104. Moitessier N, Westhof E, Hanessian S. 104.  2006. Docking of aminoglycosides to hydrated and flexible RNA. J. Med. Chem. 49:1023–33 [Google Scholar]
  105. Morley SD, Afshar M. 105.  2004. Validation of an empirical RNA-ligand scoring function for fast flexible docking using Ribodock. J. Comput. Aided Mol. Des. 18:189–208 [Google Scholar]
  106. Murchie AI, Davis B, Isel C, Afshar M, Drysdale MJ. 106.  et al. 2004. Structure-based drug design targeting an inactive RNA conformation: exploiting the flexibility of HIV-1 TAR RNA. J. Mol. Biol. 336:625–38 [Google Scholar]
  107. Mustoe AM, Al-Hashimi HM, Brooks CL III. 107.  2014. Coarse grained models reveal essential contributions of topological constraints to the conformational free energy of RNA bulges. J. Phys. Chem. B 118:2615–27 [Google Scholar]
  108. Nakano S, Karimata HT, Kitagawa Y, Sugimoto N. 108.  2009. Facilitation of RNA enzyme activity in the molecular crowding media of cosolutes. J. Am. Chem. Soc. 131:16881–88 [Google Scholar]
  109. Nawrocki EP, Burge SW, Bateman A, Daub J, Eberhardt RY. 109.  et al. 2015. Rfam 12.0: updates to the RNA families database. Nucleic Acids Res. 43:D130–37 [Google Scholar]
  110. Noid WG. 110.  2013. Perspective: coarse-grained models for biomolecular systems. J. Chem. Phys. 139:090901 [Google Scholar]
  111. Panteva MT, Giambasu GM, York DM. 111.  2015. Force field for Mg2+, Mn2+, Zn2+, and Cd2+ ions that have balanced interactions with nucleic acids. J. Phys. Chem. B 119:15460–70 [Google Scholar]
  112. Parisien M, Major F. 112.  2008. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452:51–55 [Google Scholar]
  113. Pavlov M, Siegbahn PEM, Sandstrom M. 113.  1998. Hydration of beryllium, magnesium, calcium, and zinc ions using density functional theory. J. Phys. Chem. A 102:219–28 [Google Scholar]
  114. Pfeffer P, Gohlke H. 114.  2007. DrugScoreRNA–knowledge-based scoring function to predict RNA-ligand interactions. J. Chem. Inf. Model. 47:1868–76 [Google Scholar]
  115. Philips A, Lach G, Bujnicki JM. 115.  2015. Computational methods for prediction of RNA interactions with metal ions and small organic ligands. Methods Enzymol. 553:261–85 [Google Scholar]
  116. Philips A, Milanowska K, Lach G, Boniecki M, Rother K, Bujnicki JM. 116.  2012. MetalionRNA: computational predictor of metal-binding sites in RNA structures. Bioinformatics 28:198–205 [Google Scholar]
  117. Philips A, Milanowska K, Lach G, Bujnicki JM. 117.  2013. LigandRNA: computational predictor of RNA-ligand interactions. RNA 19:1605–16 [Google Scholar]
  118. Popenda M, Szachniuk M, Antczak M, Purzycka KJ, Lukasiak P. 118.  et al. 2012. Automated 3D structure composition for large RNAs. Nucleic Acids Res. 40:e112 [Google Scholar]
  119. Radak BK, Lee TS, Harris ME, York DM. 119.  2015. Assessment of metal-assisted nucleophile activation in the hepatitis delta virus ribozyme from molecular simulation and 3D-RISM. RNA 21:1566–77 [Google Scholar]
  120. Ren J, Rastegari B, Condon A, Hoos HH. 120.  2005. HotKnots: heuristic prediction of RNA secondary structures including pseudoknots. RNA 11:1494–504 [Google Scholar]
  121. Rice GM, Leonard CW, Weeks KM. 121.  2014. RNA secondary structure modeling at consistent high accuracy using differential SHAPE. RNA 20:846–54 [Google Scholar]
  122. Rother M, Rother K, Puton T, Bujnicki JM. 122.  2011. ModeRNA: a tool for comparative modeling of RNA 3D structure. Nucleic Acids Res. 39:4007–22 [Google Scholar]
  123. Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S. 123.  et al. 2014. rDock: a fast, versatile and open source code for docking ligands to proteins and nucleic acids. PLOS Comput. Biol. 10:e1003571 [Google Scholar]
  124. Schaffer MF, Peng G, Spingler B, Schnabl J, Wang M. 124.  et al. 2016. The X-ray structures of six octameric RNA duplexes in the presence of different di- and trivalent cations. Int. J. Mol. Sci. 17:e988 [Google Scholar]
  125. Serganov A, Yuan YR, Pikovskaya O, Polonskaia A, Malinina L. 125.  et al. 2004. Structural basis for discriminative regulation of gene expression by adenine- and guanine-sensing mRNAs. Chem. Biol. 11:1729–41 [Google Scholar]
  126. Shi X, Huang L, Lilley DMJ, Harbury PB, Herschlag D. 126.  2016. The solution structural ensembles of RNA kink-turn motifs and their protein complexes. Nat. Chem. Biol 12:146–52 [Google Scholar]
  127. Shi YZ, Wang FH, Wu YY, Tan ZJ. 127.  2014. A coarse-grained model with implicit salt for RNAs: predicting 3D structure, stability and salt effect. J. Chem. Phys. 141:105102 [Google Scholar]
  128. Shortridge MD, Varani G. 128.  2015. Structure based approaches for targeting non-coding RNAs with small molecules. Curr. Opin. Struct. Biol. 30:79–88 [Google Scholar]
  129. Sloma MF, Mathews DH. 129.  2015. Improving RNA secondary structure prediction with structure mapping data. Methods Enzymol. 553:91–114 [Google Scholar]
  130. Smith AL, Kassman J, Srour KJ, Soto AM. 130.  2011. Effect of salt concentration on the conformation of TAR RNA and its association with aminoglycoside antibiotics. Biochemistry 50:9434–45 [Google Scholar]
  131. Somarowthu S. 131.  2016. Progress and current challenges in modeling large RNAs. J. Mol. Biol. 428:736–47 [Google Scholar]
  132. Sperschneider J, Datta A, Wise MJ. 132.  2011. Heuristic RNA pseudoknot prediction including intramolecular kissing hairpins. RNA 17:27–38 [Google Scholar]
  133. Sripakdeevong P, Cevec M, Chang AT, Erat MC, Ziegeler M. 133.  et al. 2014. Structure determination of noncanonical RNA motifs guided by 1H NMR chemical shifts. Nat. Methods 11:413–16 [Google Scholar]
  134. Sripakdeevong P, Kladwang W, Das R. 134.  2011. An enumerative stepwise ansatz enables atomic-accuracy RNA loop modeling. PNAS 108:20573–78 [Google Scholar]
  135. Sun LZ, Chen SJ. 135.  2016. Monte Carlo tightly bound ion model: predicting ion binding properties of RNA with ion correlations and fluctuations. J. Chem. Theory Comput. 12:3370–81 [Google Scholar]
  136. Sushko ML, Thomas DG, Pabit SA, Pollack L, Onufriev AV, Baker NA. 136.  2016. The role of correlation and solvation in ion interactions with B-DNA. Biophys. J. 110:315–26 [Google Scholar]
  137. Tan ZJ, Chen SJ. 137.  2005. Electrostatic correlations and fluctuations for ion binding to a finite length polyelectrolyte. J. Chem. Phys. 122:44903 [Google Scholar]
  138. Thomas PD, Dill KA. 138.  1996. An iterative method for extracting energy-like quantities from protein structures. PNAS 93:11628–33 [Google Scholar]
  139. Tyrrell J, McGinnis JL, Weeks KM, Pielak GJ. 139.  2013. The cellular environment stabilizes adenine riboswitch RNA structure. Biochemistry 52:8777–85 [Google Scholar]
  140. Wu Y, Shi B, Ding X, Liu T, Hu X. 140.  et al. 2015. Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data. Nucleic Acids Res. 43:7247–59 [Google Scholar]
  141. Xia Z, Bell DR, Shi Y, Ren P. 141.  2013. RNA 3D structure prediction by using a coarse-grained model and experimental data. J. Phys. Chem. B 117:3135–44 [Google Scholar]
  142. Yamauchi T, Miyoshi D, Kubodera T, Nishimura A, Nakai S, Sugimoto N. 142.  2005. Roles of Mg2+ in TPP-dependent riboswitch. FEBS Lett. 579:2583–88 [Google Scholar]
  143. Yang S, Parisien M, Major F, Roux B. 143.  2010. RNA structure determination using SAXS data. J. Phys. Chem. B 114:10039–48 [Google Scholar]
  144. Yu T, Zhu Y, He Z, Chen SJ. 144.  2016. Predicting molecular crowding effects in ion-RNA interactions. J. Phys. Chem. B 120:8837–44 [Google Scholar]
  145. Zhang J, Dundas J, Lin M, Chen R, Wang W, Liang J. 145.  2009. Prediction of geometrically feasible three-dimensional structures of pseudoknotted RNA through free energy estimation. RNA 15:2248–63 [Google Scholar]
  146. Zhang J, Ferré-D'Amaré AR. 146.  2014. Dramatic improvement of crystals of large RNAs by cation replacement and dehydration. Structure 22:1363–71 [Google Scholar]
  147. Zhang Y, Zhang J, Wang W. 147.  2011. Atomistic analysis of pseudoknotted RNA unfolding. J. Am. Chem. Soc. 133:6882–85 [Google Scholar]
  148. Zhao Y, Huang Y, Gong Z, Wang Y, Man J, Xiao Y. 148.  2012. Automated and fast building of three-dimensional RNA structures. Sci. Rep. 2:734 [Google Scholar]
  149. Zirbel CL, Sponer JE, Sponer J, Stombaugh J, Leontis NB. 149.  2009. Classification and energetics of the base-phosphate interactions in RNA. Nucleic Acids Res. 37:4898–918 [Google Scholar]
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