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RNA Modeling with the Computational Energy Landscape Framework

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RNA Scaffolds

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2323))

Abstract

The recent advances in computational abilities, such as the enormous speed-ups provided by GPU computing, allow for large scale computational studies of RNA molecules at an atomic level of detail. As RNA molecules are known to adopt multiple conformations with comparable energies, but different two-dimensional structures, all-atom models are necessary to better describe the structural ensembles for RNA molecules. This point is important because different conformations can exhibit different functions, and their regulation or mis-regulation is linked to a number of diseases. Problematically, the energy barriers between different conformational ensembles are high, resulting in long time scales for interensemble transitions. The computational potential energy landscape framework was designed to overcome this problem of broken ergodicity by use of geometry optimization. Here, we describe the algorithms used in the energy landscape explorations with the OPTIM and PATHSAMPLE programs, and how they are used in biomolecular simulations. We present a recent case study of the 5′-hairpin of RNA 7SK to illustrate how the method can be applied to interpret experimental results, and to obtain a detailed description of molecular properties.

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Notes

  1. 1.

    Formally described as a Hessian-index 1 saddle point, that is, there is one unique imaginary normal mode frequency.)

  2. 2.

    The use of AMBER requires an AMBER license.

References

  1. Quarta G, Sin K, Schlick T (2012) Dynamic energy landscapes of riboswitches help interpretconformational rearrangements and function. PLoS Comput Biol 8:e1002368

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Martinez-Zapien D, Saliou JM, Han X et al (2015) Intermolecular recognition of the non-coding RNA 7SK and HEXIM protein in perspective. Biochimie 117:63–71

    Article  CAS  PubMed  Google Scholar 

  3. Martinez-Zapien D, Legrand P, McEwen AG et al (2017) The crystal structure of the 5′functional domain of the transcription riboregulator 7SK. Nucleic Acids Res 45(6):3568–3579

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Pan J, Woodson SA (1998) Folding intermediates of a self-splicing RNA: mispairing of the catalytic core. J Mol Biol 280:597–609

    Article  CAS  PubMed  Google Scholar 

  5. Chen SJ, Dill K (2000) RNA folding energy landscapes. Proc Natl Acad Sci U S A 97:646–651

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Li PTX, Vieregg J, Tinoco I (2008) How RNA unfolds and refolds. Annu Rev Biochem 77:77–100

    Article  CAS  PubMed  Google Scholar 

  7. Solomatin SV, Greenfeld M, Chu S et al (2010) Multiple native states reveal persistent ruggedness of an RNA folding landscape. Nature 463:681–684

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Schlatterer JC, Martin JS, Laederach A et al (2014) Mapping the kinetic barriers of a large RNA molecule’s folding landscape. PLoS One 9:e85041

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Leopold PE, Montal M, Onuchic JN (1992) Protein folding funnels: a kinetic approach to the sequence-structure relationship. Proc Natl Acad Sci U S A 89(18):8721–8725

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bryngelson JD, Onuchic JN, Socci ND et al (1995) Funnels, pathways, and the energy landscape of protein folding: a synthesis. Proteins 21(3):167–195

    Article  CAS  PubMed  Google Scholar 

  11. Ferreiro DU, Hegler JA, Komives EA et al (2011) On the role of frustration in the energy landscapes of allosteric proteins. Proc Natl Acad Sci U S A 108(9):3499–3503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kouza M, Hansmann UHE (2012) Folding simulations of the a and B domains of protein G. J Phys Chem B 116(23):6645–6653

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Röder K, Wales DJ (2017) Transforming the energy landscape of a coiled-coil peptide via point mutations. J Chem Theory Comput 13(3):1468–1477

    Article  PubMed  CAS  Google Scholar 

  14. Röder K, Wales DJ (2018) Evolved minimal frustration in multifunctional biomolecules. J Phys Chem B 122:10989–10995

    Article  PubMed  CAS  Google Scholar 

  15. Burge S, Parkinson G, Neidle S (2006) Quadruplex DNA: sequence, topology and structure. Nucleic Acids Res 34:5402–5415

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang AYQ, Balasubramanian S (2012) The kinetics and folding pathways of intramolecular G-quadruplex nucleic acids. J Am Chem Soc 134(46):19297–19308

    Article  CAS  PubMed  Google Scholar 

  17. Stadlbauer P, Mazzanti L, Cragnolini T et al (2016) Folding of human telomeric G-quadruplexes studied by coarse-grained and all atom simulations. J Chem Theory Comput 12:6077–6097

    Article  CAS  PubMed  Google Scholar 

  18. Cragnolini T, Chakraborty D, Sponer J et al (2017) Multifunctional energy landscape for a DNA G-quadruplex: an evolved molecular switch. J Chem Phys 147(15):152715

    Article  PubMed  CAS  Google Scholar 

  19. Bryngelson JD, Wolynes PG (1987) Spin glasses and the statistical mechanics of proteinfolding. Proc Natl Acad Sci U S A 84(21):7524–7528

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Röder K, Stirnemann G, Dock-Bregeon AC et al (2020) Structural transitions in the RNA 7SK 5′hairpin and their effect on HEXIM binding. Nucleic Acids Res 48:373–389

    PubMed  Google Scholar 

  21. Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing non canonical RNA structure. Nat Methods 7:291–294

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Xu X, Zhao P, Chen SJ (2014) Vfold: a web server for RNA structure and folding thermo-dynamics prediction. PLoS One 9(9):107504

    Article  CAS  Google Scholar 

  23. Popenda M, Szachniuk M, Antczak M et al (2012) Automated 3D structure composition for large RNAs. Nucleic Acids Res 40:e112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wales DJ (2003) Energy landscapes. Cambridge University Press, Cambridge

    Google Scholar 

  25. Wales DJ, Salamon P (2014) Observation time scale, free-energy landscapes, and molecular symmetry. Proc Natl Acad Sci U S A 111(2):617–622

    Article  CAS  PubMed  Google Scholar 

  26. Joseph JA, Röder K, Chakraborty D et al (2017) Exploring biomolecular energy landscapes. Chem Commun 53:6974–6988

    Article  CAS  Google Scholar 

  27. Wales DJ (2002) Discrete path sampling. Mol Phys 100(20):3285–3305

    Article  CAS  Google Scholar 

  28. Wales DJ (2004) Some further applications of discrete path sampling to cluster isomerization. Mol Phys 102(9–10):891–908

    Article  CAS  Google Scholar 

  29. Carr JM, Trygubenko SA, Wales DJ (2005) Finding pathways between distant local minima. J Chem Phys 122(23):234903

    Article  PubMed  CAS  Google Scholar 

  30. Röder K (2018) Energy landscaping - On the relationship between functionality and sequence mutations for multifunctional biomolecules PhD thesis University of Cambridge

    Google Scholar 

  31. Henkelman G, Uberuaga B, Jónsson H (2000) A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J Chem Phys 113(22):9901–9904

    Article  CAS  Google Scholar 

  32. Henkelman G, Jónsson H (2000) Improved tangent estimate in the nudged elastic band method for finding minimum energy paths and saddle points. J Chem Phys 113(22):9978–9985

    Article  CAS  Google Scholar 

  33. Trygubenko SA, Wales DJ (2004) A doubly nudged elastic band method for finding transition states. J Chem Phys 120(5):2082–2094

    Article  CAS  PubMed  Google Scholar 

  34. Munro LJ, Wales DJ (1999) Defect migration in crystalline silicon. Phys Rev B 59(6):3969–3980

    Article  CAS  Google Scholar 

  35. Henkelman G, Jónsson H (1999) A dimer method for finding saddle points on high dimensional potential surfaces using only first derivatives. J Chem Phys 111(15):7010–7022

    Article  CAS  Google Scholar 

  36. Griffiths M, Niblett SP, Wales DJ (2017) Optimal alignment of structures of finite and periodic systems. J Chem Theory Comput 13(10):4914–4931

    Article  CAS  PubMed  Google Scholar 

  37. Röder K, Wales DJ (2018) Energy landscapes for the aggregation of Aβ17−42. J Am ChemSoc 140(11):4018–4027

    Article  CAS  Google Scholar 

  38. Strodel B, Whittleston CS, Wales DJ (2007) Thermodynamics and kinetics of aggregation for the GNNQQNY peptide. J Am Chem Soc 129(51):16005–16014

    Article  CAS  PubMed  Google Scholar 

  39. Carr JM, Wales DJ (2005) Global optimization and folding pathways of selected alpha-helical proteins. J Chem Phys 123(23):234901

    Article  PubMed  CAS  Google Scholar 

  40. Becker OM, Karplus M (1998) The topology of multidimensional potential energy surfaces:theory and application to peptide structure and kinetics. J Chem Phys 106(4):1495–1517

    Article  Google Scholar 

  41. Wales DJ, Miller MA, Walsh TR (1998) Archetypal energy landscapes. Nature 394(6695):758–760

    Article  CAS  Google Scholar 

  42. Crangolini T, Laurin Y, Derreumaux P et al (2015) Coarse-Grained HiRE-RNA model for ab initio RNA folding beyond simple molecules, including noncanonical and multiple base pairings. J Chem Theory Comput 14:3510–3522

    Google Scholar 

  43. Hess B, Kutzner C, Van Der Spoel D et al (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447.16

    Article  CAS  PubMed  Google Scholar 

  44. McGibbon RT, Beauchamp KA, Harrigan MP et al (2015) MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys J 109(8):1528–1532

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Antczak M, Zok T, Popenda M et al (2014) RNApdbee—a webserver to derive secondary structures from pdb files of knotted and unknotted RNAs. Nucleic Acids Res 42(W1):W368–W372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang J, Cieplak P, Kollman PA (2000) How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J Comput Chem 21(21):1049–1074

    Article  CAS  Google Scholar 

  47. Pérez A, Marchán I, Svozil D et al (2007) Refinement of the AMBER force field for nucleic acids: improving the description of α/γ conformers. Biophys J 92(11):3817–3829

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Banás P, Hollas D, Zgarbová M, Jurecka P et al (2010) Performance of molecular mechanics force fields for RNA simulations: stability of UUCGand GNRA hairpins. J Chem Theory Comput 6(12):3836–3849

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zgarbová M, Otyepka M, Sponer J et al (2011) 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(9):2886–2902

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Bourbigot S, Dock-Bregeon AC, Eberling P et al (2016) Solution structure of the 5′-terminal hairpin of the 7SK small nuclear RNA. RNA 22(12):1844–1858

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kusumaatmaja H, Whittleston CS, Wales DJ (2012) A local rigid body framework for global optimization of biomolecules. J Chem Theory Comput 8(12):5159–5165

    Article  CAS  PubMed  Google Scholar 

  52. Strodel B, Wales DJ (2008) Free energy surfaces from an extended harmonic superposition approach and kinetics for alanine dipeptide. Chem Phys Lett 466:105–115

    Article  CAS  Google Scholar 

  53. Carr JM, Wales DJ (2008) Folding pathways and rates for the three-stranded β-sheet peptide Beta3s using discrete path sampling. J Phys Chem B 112:8760–8769

    Article  CAS  PubMed  Google Scholar 

  54. Wales DJ (2017) Decoding heat capacity features from the energy landscape. Phys Rev E 95:030105

    Article  PubMed  Google Scholar 

  55. Wales DJ (2009) Calculating rate constants and committor probabilities for transition networks by graph transformation. J Chem Phys 130:204111

    Article  PubMed  CAS  Google Scholar 

  56. Röder K, Wales DJ (2018) Analysis of the Ub to Ub-CR transition in ubiquitin. Biochemistry 57(43):6180–6186

    Article  PubMed  CAS  Google Scholar 

  57. Li Z, Scheraga HA (1987) Monte Carlo-minimization approach to the multiple-minima problem in protein folding. Proc Natl Acad Sci U S A 84(19):6611–6615

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Li Z, Scheraga HA (1988) Structure and free-energy of complex thermodynamic systems. J Mol Struct 48:333–352

    Article  CAS  Google Scholar 

  59. Wales DJ, Doye JPK (1997) Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J Chem Phys A 101(28):5111–5116

    Article  CAS  Google Scholar 

  60. Wales DJ, Carr JM (2012) Quasi-continuous interpolation scheme for pathways between distant configurations. J Chem Theory Comput 8(12):5020–5034

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Yusuf Hamied Department of Chemistry, University of Chemistry, Cambridge, UK

    Konstantin Röder

  2. Laboratoire CiTCoM, CNRS UMR 8038, Université de Paris, Paris, France

    Samuela Pasquali

Authors
  1. Konstantin Röder
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  2. Samuela Pasquali
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Corresponding authors

Correspondence to Konstantin Röder or Samuela Pasquali .

Editor information

Editors and Affiliations

  1. Faculté de Pharmacie, University of Paris, PARIS, France

    Luc Ponchon

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Röder, K., Pasquali, S. (2021). RNA Modeling with the Computational Energy Landscape Framework. In: Ponchon, L. (eds) RNA Scaffolds. Methods in Molecular Biology, vol 2323. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1499-0_5

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  • DOI: https://doi.org/10.1007/978-1-0716-1499-0_5

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1498-3

  • Online ISBN: 978-1-0716-1499-0

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