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An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-Layer Neural Networks

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Abstract

A good scoring function is necessary for ab inito prediction of RNA tertiary structures. In this study, we explored the power of a machine learning based approach as a scoring function. Compared with the traditional scoring functions, the present approach is more flexible in incorporating different kinds of features; it is also free of the difficult problem of choosing the reference state. Two multi-layer neural networks were constructed and trained. They took RNA a structural candidate as input and then output its likeness score that evaluates the likeness of the candidate to the native structure. The first network was working at the coarse-grained level of RNA structures, while the second at the all-atom level. We also built an RNA database and split it into the training, validation, and testing sets, containing 322, 70, and 70 RNAs, respectively. Each RNA was accompanied with 300 decoys generated by high-temperature molecular dynamics simulations. The networks were trained on the training set and then optimized with an early-stop strategy, based on the loss of the validation set. We then tested the performance of the networks on the testing set. The results were found to be consistently better than a recent knowledge-based all-atom potential.

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REFERENCES

  1. Parisien M., Major F. 2008. The MC-Fold and MC-Sym pipeline infers RNA structure from sequence data. Nature. 452, 51‒55.

    Article  CAS  PubMed  Google Scholar 

  2. Jonikas M.A., Radmer R.J., Laederach A., Das R., Pearlman S., Herschlag D., Altman R.B. 2009. Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA. 15 (2), 189‒199.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Flores S.C., Wan Y., Russell R., Altman R.B. 2010. Predicting RNA structure by multiple template homology modeling. Pac. Symp. Biocomput. 216‒227.

  4. Sharma S., Ding F., Dokholyan N.V. 2008. iFoldRNA: Three-dimensional RNA structure prediction and folding. Bioinformatics. 24, 1951‒1952.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zwieb C., Muller F. 1997. Three-dimensional comparative modeling of RNA. Nucleic Acids Symp Ser. 36, 69‒71.

    CAS  Google Scholar 

  7. Martinez H.M., Maizel J.V. Jr., Shapiro B.A. 2008. RNA2D3D: A program for generating, viewing, and comparing 3-dimensional models of RNA. J. Biomol. Struct. Dyn. 25, 669‒683.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Popenda M., Szachniuk M., Antczak M., Purzycka K.J., Lukasiak P., Bartol N., Blazewicz J., Adamiak R.W. 2012. Automated 3D structure composition for large RNAs. Nucleic Acids Res. 40, e112. https://doi.org/10.1093/nar/gks339

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhao Y.J., Huang Y.Y., Gong Z., Wang Y., Man J., Xiao Y. 2012. Automated and fast building of three-dimensional RNA structures. Sci. Rep. 2, 734. https://doi.org/10.1038/srep00734

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wang J., Mao K.K., Zhao Y.J., Zeng C., Xiang J., Zhang Y., Xiao Y. 2017. Optimization of RNA 3D structure prediction using evolutionary restraints of nucleotide-nucleotide interactions from direct coupling analysis. Nucleic Acids Res. 45 (11), 6299‒6309.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Zhang J., Dundas J., Lin M., Chen R., Wang W., Liang J. 2009. Prediction of geometrically feasible three-dimensional structures of pseudoknotted RNA through free energy estimation. RNA. 15, 2248‒2263.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang J., Zhang Y.J., Wang W. 2010. An RNA base discrete state model toward tertiary structure prediction. Chin. Phys. Lett. 27, 118702.

    Article  CAS  Google Scholar 

  13. Zhang J., Bian Y.Q., Lin H., Wang W. 2012. RNA fragment modeling with a nucleobase discrete-state model. Phys. Rev. E. 85, 021909.

    Article  CAS  Google Scholar 

  14. Li J., Zhang J., Wang J., Wang W. 2016. Structure prediction of RNA loops with a probabilistic approach. PLoS Comput. Biol. 12, e1005032.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Qasim R., Kauser N., Jilani T. 2011. Secondary structure prediction of RNA using machine learning method. Int. J. Comput. Appl. 10 (6), 24‒28.

    Google Scholar 

  16. Frellsen J., Moltke I., Thiim M., Mardia K.V., Ferkinghoff-Borg J., Hamelryck T. 2009. A probabilistic model of RNA conformational space. PLoS Comput Biol. 5, e1000406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wang Z., Xu J. 2011. A conditional random fields method for RNA sequence–structure relationship modeling and conformation sampling. Bioinformatics. 27, i102‒110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Capriotti E., Norambuena T., Marti-Renom M.A., Melo F. 2011. All-atom knowledge-based potential for RNA structure prediction and assessment. Bioinformatics. 27, 1086‒1093.

    Article  CAS  PubMed  Google Scholar 

  19. Cao S., Chen S.J. 2006. Predicting RNA pseudoknot folding thermodynamics. Nucleic Acids Res. 34, 2634‒2652.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Tan Z.J., Chen S.J. 2011. Salt contribution to RNA tertiary structure folding stability. Biophys. J. 101, 176‒187.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wu Y.Y., Zhang Z.L., Zhang J.S., Zhu X.L., Tan Z.J. 2015. Multivalent ion-mediated nucleic acid helix-helix interactions: RNA versus DNA. Nucleic Acids Res. 43, 6156‒6165.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Shi Y.Z., Wang F.H., Wu Y.Y., Tan Z.J. 2014. A coarse-grained model with implicit salt for RNAs: Predicting 3D structure, stability and salt effect. J. Chem. Phys. 141, 105102.

    Article  CAS  PubMed  Google Scholar 

  23. Shi Y.Z., Wu Y.Y., Wang F.H., et al. 2014. RNA structure prediction: Progress and perspective. Chinese Phys B. 23, 078701.

    Article  CAS  Google Scholar 

  24. Gong S., Wang Y.J., Zhang W.B. 2015. The regulation mechanism of yitJ and metF riboswitches. J. Chem. Phys. 143, 045103.

    Article  CAS  PubMed  Google Scholar 

  25. Zhang W.B., Chen S.J. 2001. A three-dimensional statistical mechanical model of folding double-stranded chain molecules. J. Chem. Phys. 114, 7669‒7681.

    Article  CAS  Google Scholar 

  26. Yang Y., Zhao H., Wang J., Zhou Y. 2014. SPOT-Seq-RNA: Predicting protein–RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction. Methods Mol Biol. 1137, 119‒130.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yang Y., Li X., Zhao H., Zhan J., Wang J., Zhou Y. 2017. Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction. RNA. 23, 14‒22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang X., El Naqa I.M. 2008. Prediction of both conserved and nonconserved microRNA targets in animals. Bioinformatics. 24, 325‒332.

    Article  CAS  Google Scholar 

  29. Xu X., Zhao P., Chen S.J. 2014. Vfold: A web server for RNA structure and folding thermodynamics prediction. PLoS One. 9, e107504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Magnus M., Boniecki M.J., Dawson W., Bujnicki J.M. 2016. SimRNAweb: A web server for RNA 3D structure modeling with optional restraints. Nucleic Acids Res. 44, W315‒W319. https://doi.org/10.1093/nar/gkw279

    Article  CAS  Google Scholar 

  31. Magnus M., Matelska D., Lach G., Chojnowski G., Boniecki M.J., Purta E., Dawson W., Dunin-Horkawicz S., Bujnicki J.M. 2014. Computational modeling of RNA 3D structures, with the aid of experimental restraints. RNA Biol. 11, 522‒536.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhang J., Lin M., Chen R., Wang W., Liang J. 2008. Discrete state model and accurate estimation of loop entropy of RNA secondary structures. J. Chem. Phys. 128, 125107.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Tang K., Zhang J.F., Liang J. 2014. Fast protein loop sampling and structure prediction using distance-guided sequential chain-growth Monte Carlo method. PLoS Comput. Biol. 10, e1003539.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Goodfellow I., Bengio Y., Courville A. 2016. Deep Learning. Cambridge, MA: MIT Press.

    Google Scholar 

  35. Silver D., Huang A., Maddison C.J., Guez A., Sifre L., van den Driessche G., Schrittwieser J., Antonoglou I., Panneershelvam V., Lanctot M., Dieleman S., Grewe D., Nham J., Kalchbrenner N., Sutskever I., et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature. 529, 484‒489.

    Article  CAS  PubMed  Google Scholar 

  36. Silver D., Schrittwieser J., Simonyan K., Antonoglou I., Huang A., Guez A., Hubert T., Baker L., Lai M., Bolton A., Chen Y., Lillicrap T., Hui F., Sifre L., van den Driessche G., et al. 2017. Mastering the game of Go without human knowledge. Nature. 550, 354‒359.

    Article  CAS  PubMed  Google Scholar 

  37. Carleo G., Troyer M. 2017. Solving the quantum many-body problem with artificial neural networks. Science. 355, 602‒605.

    Article  CAS  PubMed  Google Scholar 

  38. Carrasquilla J., Melko R.G. 2017. Machine learning phases of matter. Nat. Phys. 13, 431‒434.

    Article  CAS  Google Scholar 

  39. van Nieuwenburg E.P.L., Liu Y.H., Huber S.D. 2017. Learning phase transitions by confusion. Nat. Phys. 13, 435‒439.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. School of Physics, Collaborative Innovation Center of Advanced Microstructures, and National Laboratory of Solid State Microstructures, Nanjing University, 210093, Nanjing, China

    Y. Z. Wang, J. Li, S. Zhang, B. Huang, G. Yao & J. Zhang

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  1. Y. Z. Wang
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  2. J. Li
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  3. S. Zhang
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  4. B. Huang
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  5. G. Yao
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  6. J. Zhang
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Corresponding author

Correspondence to J. Zhang.

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The text was submitted by the author(s) in English.

These authors contribute equally.

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Wang, Y.Z., Li, J., Zhang, S. et al. An RNA Scoring Function for Tertiary Structure Prediction Based on Multi-Layer Neural Networks. Mol Biol 53, 118–126 (2019). https://doi.org/10.1134/S0026893319010175

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  • DOI: https://doi.org/10.1134/S0026893319010175

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