Abstract
In analogy to proteins, the function of RNA depends on its structure and dynamics, which are encoded in the linear sequence. While there are numerous methods for computational prediction of protein 3D structure from sequence, there have been however very few such methods for RNA. This chapter discusses template-based and template-free approaches for macromolecular structure prediction, with special emphasis on comparison between the already tried-and-tested methods for protein structure modeling and the very recently developed “protein-like” modeling methods for RNA. As examples, we briefly review our recently developed tools for RNA 3D structure prediction, including ModeRNA (template-based or comparative/homology modeling) and SimRNA (template-free or de novo modeling). ModeRNA requires, as an input, atomic 3D coordinates of a template RNA molecule and a user-specified sequence alignment between the target to be modeled and the template. It can model posttranscriptional modifications, a functionally important feature analogous to posttranslational modifications in proteins. It can model the structures of RNAs of essentially any length, provided that a starting template is known. SimRNA can fold RNA 3D structure starting from sequence alone. It is based on a coarse-grained representation of the polynucleotide chains (only three atoms per nucleotide) and uses a Monte Carlo sampling scheme to generate moves in the 3D space, with a statistical potential to estimate the free energy. The current implementation based on simulated annealing is able to find native-like conformations for RNAs <100 nt in length, with multiple runs required to fold long sequences.
Kristian Rother, Magdalena Rother, and Michał Boniecki contributed equally to the manuscript.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anfinsen CB (1973) Principles that govern the folding of protein chains. Science 181:223–230
Boniecki M, Rotkiewicz P, Skolnick J, Kolinski A (2003) Protein fragment reconstruction using various modeling techniques. J Comput Aided Mol Des 17:725–738
Boomsma W, Hamelryck T (2005) Full cyclic coordinate descent: solving the protein loop closure problem in Calpha space. BMC Bioinformatics 6:159
Bujnicki JM (2006) Protein-structure prediction by recombination of fragments. Chembiochem 7:19–27
Bujnicki JM (2008) Prediction of protein structures, functions and interactions
Chothia C, Gerstein M (1997) Protein evolution. How far can sequences diverge? Nature 385(579):581
Chothia C, Lesk AM (1986) The relation between the divergence of sequence and structure in proteins. EMBO J 5:823–826
Cock PJ, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJ (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25:1422–1423
Cohen-Gonsaud M, Catherinot V, Labesse G, Douguet D (2004) From molecular modeling to drug design. In: Bujnicki JM (ed) Practical bioinformatics, vol 15. Springer, Berlin, pp 35–71
Cruz JA, Blanchet MF, Boniecki M, Bujnicki JM, Chen SJ, Cao S, Das R, Ding F, Dokholyan NV, Flores SC, Huang L, Lavender CA, Lisi V, Major F, Mikolajczak K, Patel DJ, Philips A, Puton T, SantaLucia J, Sijenyi F, Hermann T, Rother K, Rother M, Serganov A, Skorupski M, Soltysinski T, Sripakdeevong P, Tuszynska I, Weeks KM, Waldsich C, Wildauer M, Leontis NB, Westhof E (2012) RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction. RNA doi: 10.1261/rna.031054.111
Czerwoniec A, Dunin-Horkawicz S, Purta E, Kaminska KH, Kasprzak JM, Bujnicki JM, Grosjean H, Rother K (2009) MODOMICS: a database of RNA modification pathways. 2008 update. Nucleic Acids Res 37:D118–121
Das R, Baker D (2007) Automated de novo prediction of native-like RNA tertiary structures. Proc Natl Acad Sci U S A 104:14664–14669
Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods 7:291–294
Ding F, Sharma S, Chalasani P, Demidov VV, Broude NE, Dokholyan NV (2008) Ab initio RNA folding by discrete molecular dynamics: from structure prediction to folding mechanisms. RNA 14:1164–1173
Dowell RD, Eddy SR (2006) Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints. BMC Bioinformatics 7:400
Dror O, Nussinov R, Wolfson H (2005) ARTS: alignment of RNA tertiary structures. Bioinformatics 21(Suppl 2):ii47–ii53
Duarte CM, Pyle AM (1998) Stepping through an RNA structure: A novel approach to conformational analysis. J Mol Biol 284:1465–1478
Eddy SR (2002) A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinformatics 3:18
Fiser A, Feig M, Brooks CL 3rd, Sali A (2002) Evolution and physics in comparative protein structure modeling. Acc Chem Res 35:413–421
Freyhult EK, Bollback JP, Gardner PP (2007) Exploring genomic dark matter: a critical assessment of the performance of homology search methods on noncoding RNA. Genome Res 17:117–125
Gardner PP, Daub J, Tate JG, Nawrocki EP, Kolbe DL, Lindgreen S, Wilkinson AC, Finn RD, Griffiths-Jones S, Eddy SR, Bateman A (2009) Rfam: updates to the RNA families database. Nucleic Acids Res 37:D136–140
Godzik A (2003) Fold recognition methods. Methods Biochem Anal 44:525–546
Grishin NV (2001) Fold change in evolution of protein structures. J Struct Biol 134:167–185
Grosjean H (2009) DNA and RNA modification enzymes: structure, mechanism, function and evolution:682
Hajdin CE, Ding F, Dokholyan NV, Weeks KM (2010) On the significance of an RNA tertiary structure prediction. RNA 16:1340–1349
Hardin C, Pogorelov TV, Luthey-Schulten Z (2002) Ab initio protein structure prediction. Curr Opin Struct Biol 12:176–181
Hinsen K (2000) The molecular modeling toolkit: a new approach to molecular simulations. J Comp Chem 21:79–85
Holmes I (2005) Accelerated probabilistic inference of RNA structure evolution. BMC Bioinformatics 6:73
Johnston MA, Galvan IF, Villa-Freixa J (2005) Framework-based design of a new all-purpose molecular simulation application: the Adun simulator. J Comput Chem 26:1647–1659
Jossinet F, Westhof E (2005) Sequence to Structure (S2S): display, manipulate and interconnect RNA data from sequence to structure. Bioinformatics 21:3320–3321
Jossinet F, Ludwig TE, Westhof E (2010) Assemble: an interactive graphical tool to analyze and build RNA architectures at the 2D and 3D levels. Bioinformatics
Juhling F, Morl M, Hartmann RK, Sprinzl M, Stadler PF, Putz J (2009) tRNAdb 2009: compilation of tRNA sequences and tRNA genes. Nucleic Acids Res 37:D159–162
Klein RJ, Eddy SR (2003) RSEARCH: finding homologs of single structured RNA sequences. BMC Bioinformatics 4:44
Knight R, Maxwell P, Birmingham A, Carnes J, Caporaso JG, Easton BC, Eaton M, Hamady M, Lindsay H, Liu Z, Lozupone C, McDonald D, Robeson M, Sammut R, Smit S, Wakefield MJ, Widmann J, Wikman S, Wilson S, Ying H, Huttley GA (2007) PyCogent: a toolkit for making sense from sequence. Genome Biol 8:R171
Kolinski A (2004) Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51:349–371
Kolinski A, Bujnicki JM (2005) Generalized protein structure prediction based on combination of fold-recognition with de novo folding and evaluation of models. Proteins 61(Suppl 7):84–90
Kosinski J, Cymerman IA, Feder M, Kurowski MA, Sasin JM, Bujnicki JM (2003) A “FRankenstein’s monster” approach to comparative modeling: merging the finest fragments of Fold-Recognition models and iterative model refinement aided by 3D structure evaluation. Proteins 53(Suppl 6):369–379
Krasilnikov AS, Xiao Y, Pan T, Mondragon A (2004) Basis for structural diversity in homologous RNAs. Science 306:104–107
Krieger E, Nabuurs SB, Vriend G (2003) Homology modeling. Methods Biochem Anal 44:509–523
Kumar S, Ma B, Tsai CJ, Sinha N, Nussinov R (2000) Folding and binding cascades: dynamic landscapes and population shifts. Protein Sci 9:10–19
Levitt M, Gerstein M (1998) A unified statistical framework for sequence comparison and structure comparison. Proc Natl Acad Sci U S A 95:5913–5920
Martinez HM, Maizel JV Jr, Shapiro BA (2008) RNA2D3D: a program for generating, viewing, and comparing 3-dimensional models of RNA. J Biomol Struct Dyn 25:669–683
Metropolis N, Ulam S (1949) The Monte Carlo method. J Am Stat Assoc 44:335–341
Michalsky E, Goede A, Preissner R (2003) Loops In Proteins (LIP)–a comprehensive loop database for homology modelling. Protein Eng 16:979–985
Moult J, Fidelis K, Kryshtafovych A, Rost B, Tramontano A (2009) Critical assessment of methods of protein structure prediction—Round VIII. Proteins 77(Suppl 9):1–4
Murray LJ, Arendall WB 3rd, Richardson DC, Richardson JS (2003) RNA backbone is rotameric. Proc Natl Acad Sci U S A 100:13904–13909
Nawrocki EP, Kolbe DL, Eddy SR (2009) Infernal 1.0: inference of RNA alignments. Bioinformatics 25:1335–1337
Olson WK, Flory PJ (1972) Spatial configurations of polynucleotide chains. I. Steric interactions in polyribonucleotides: a virtual bond model. Biopolymers 11:1–23
Otto W, Will S, Backofen R (2008) Structure local multiple alignment of RNA. GCB’2008, Germany, vol P, pp 178–188
Parisien M, Major F (2008) The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452:51–55
Parisien M, Cruz JA, Westhof E, Major F (2009) New metrics for comparing and assessing discrepancies between RNA 3D structures and models. RNA 15:1875–1885
Parsons J, Holmes JB, Rojas JM, Tsai J, Strauss CE (2005) Practical conversion from torsion space to Cartesian space for in silico protein synthesis. J Comput Chem 26:1063–1068
Poehlsgaard J, Douthwaite S (2005) The bacterial ribosome as a target for antibiotics. Nat Rev Microbiol 3:870–881
Pyle AM (2002) Metal ions in the structure and function of RNA. J Biol Inorg Chem 7:679–690
Richardson JS, Schneider B, Murray LW, Kapral GJ, Immormino RM, Headd JJ, Richardson DC, Ham D, Hershkovits E, Williams LD, Keating KS, Pyle AM, Micallef D, Westbrook J, Berman HM (2008) RNA backbone: consensus all-angle conformers and modular string nomenclature (an RNA Ontology Consortium contribution). RNA 14:465–481
Rother K, Rother M, Boniecki M, Puton T, Bujnicki JM (2011a) RNA and protein 3D structure modeling: similarities and differences. J Mol Model. 17:2325–2336
Rother M, Rother K, Puton T, Bujnicki JM (2011b) ModeRNA: a tool for comparative modeling of RNA 3D structure. Nucleic Acids Res. 39:4007–4022
Sayers EW, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, Church DM, DiCuccio M, Edgar R, Federhen S, Feolo M, Geer LY, Helmberg W, Kapustin Y, Landsman D, Lipman DJ, Madden TL, Maglott DR, Miller V, Mizrachi I, Ostell J, Pruitt KD, Schuler GD, Sequeira E, Sherry ST, Shumway M, Sirotkin K, Souvorov A, Starchenko G, Tatusova TA, Wagner L, Yaschenko E, Ye J (2009) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 37:D5–15
Scheraga HA (1996) Recent developments in the theory of protein folding: searching for the global energy minimum. Biophys Chem 59:329–339
Schudoma C, May P, Nikiforova V, Walther D (2010) Sequence-structure relationships in RNA loops: establishing the basis for loop homology modeling. Nucleic Acids Res 38:970–980
Schwede T, Kopp J, Guex N, Peitsch MC (2003) SWISS-MODEL: an automated protein homology-modeling server. Nucleic Acids Res 31:3381–3385
Simons KT, Kooperberg C, Huang E, Baker D (1997) Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J Mol Biol 268:209–225
Sippl M (1993) Boltzmann’s principle, knowledge-based mean fields and protein folding. An approach to the computational determination of protein structures. J Comput Aided Mol Des 7:473–501
Thirumalai D, Hyeon C (2005) RNA and protein folding: common themes and variations. Biochemistry 44:4957–4970
Torarinsson E, Havgaard JH, Gorodkin J (2007) Multiple structural alignment and clustering of RNA sequences. Bioinformatics 23:926–932
Tozzini V (2009) Multiscale modeling of proteins. Acc Chem Res
Weinberg Z, Ruzzo WL (2006) Sequence-based heuristics for faster annotation of non-coding RNA families. Bioinformatics 22:35–39
Wilm A, Higgins DG, Notredame C (2008) R-Coffee: a method for multiple alignment of non-coding RNA. Nucleic Acids Res 36:e52
Zemla A (2003) LGA: A method for finding 3D similarities in protein structures. Nucleic Acids Res 31:3370–3374
Zhang Y, Skolnick J (2004a) Automated structure prediction of weakly homologous proteins on a genomic scale. Proc Natl Acad Sci U S A 101:7594–7599
Zhang Y, Skolnick J (2004b) Scoring function for automated assessment of protein structure template quality. Proteins 57:702–710
Zwieb C, Muller F (1997) Three-dimensional comparative modeling of RNA. Nucleic Acids Symp Ser:69–71
Acknowledgments
Our work on template-based modeling of RNA structures was supported by the Faculty of Biology, Adam Mickiewicz University (PBWB-03/2009 grant to M.R.), and by the Polish Ministry of Science (PBZ/MNiSW/07/2006 grant to M.B.). Our work on template-free modeling of RNA structures was supported by the Polish Ministry of Science (HISZPANIA/152/2006 grant to J.M.B.) and by the EU (6FP grant “EURASNET,” LSHG-CT-2005-518238). Software development in the Bujnicki laboratory in IIMCB has been supported by the EU structural funds (POIG.02.03.00-00-003/09). K.R. was independently supported by the German Academic Exchange Service (grant D/09/42768).
We thank present and former members of the Bujnicki laboratory in IIMCB and at the UAM, in particular Ewa Wywiał, Pawel Skiba, Piotr Byzia, Irina Tuszynska, Joanna Kasprzak, Jerzy Orlowski, Tomasz Osiński, Marcin Domagalski, Anna Czerwoniec, Stanisław Dunin-Horkawicz, Marcin Skorupski, and Marcin Feder, for their comments and constructive criticism during development of our software. The unit test framework was brought near to us by Sandra Smit, Rob Knight, and Gavin Huttley. Special thanks go to the group of Russ Altman, who provided us with their modeling example to test ModeRNA. We also would like to thank Neocles Leontis for the critical reading of the manuscript of this chapter and him as well as Magda Jonikas, Fabrice Jossinet, Samuel Flores, Alain Laederach, Francois Major, and Eric Westhof for stimulating discussions and helpful advice on various occasions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Rother, K. et al. (2012). Template-Based and Template-Free Modeling of RNA 3D Structure: Inspirations from Protein Structure Modeling. In: Leontis, N., Westhof, E. (eds) RNA 3D Structure Analysis and Prediction. Nucleic Acids and Molecular Biology, vol 27. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25740-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-25740-7_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25739-1
Online ISBN: 978-3-642-25740-7
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)