Advertisement

A Review of Quasi-perfect Secondary Structure Prediction Servers

  • Mirto MusciEmail author
  • Gioele Maruccia
  • Marco Ferretti
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1062)

Abstract

The secondary structure was first described by Pauling et al. in 1951 [14] in their findings of helical and sheet hydrogen bounding patterns in a protein backbone. Further refinements have been made since then, such as the description and identification of first 3, then 8 local conformational states [10]. The accuracy of 3-state secondary structure prediction has risen during last 3 decades and now we are approaching to the theoretical limit of 88–90%. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. In this paper we review the best four scorer servers which provide the highest accuracy for 3- and 8-state secondary structure prediction.

Keywords

Secondary structure prediction Deep neural networks Machine learning 

References

  1. 1.
    Altschul, S., et al.: Gapped blast and PSI-blast: a new generation of protein databases search programs. Nucleic Acids Res. 25, 3389–402 (1997).  https://doi.org/10.1093/nar/25.17.3389CrossRefGoogle Scholar
  2. 2.
    Anfinsen, C.B.: Principles that govern the folding of protein chains. Science 181(4096), 223–230 (1973)CrossRefGoogle Scholar
  3. 3.
    Bourne, P.E.: The protein data bank. In: Protein Structure: Determination, Analysis, and Applications for Drug Discovery, p. 389 (2003)Google Scholar
  4. 4.
    Ceroni, A., Frasconi, P.: On the role of long-range dependencies in learning protein secondary structure. In: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No. 04CH37541), vol. 3, pp. 1899–1904. IEEE (2004)Google Scholar
  5. 5.
    Ceroni, A., Frasconi, P., Pollastri, G.: Learning protein secondary structure from sequential and relational data. Neural Netw. 18(8), 1029–1039 (2005) CrossRefGoogle Scholar
  6. 6.
    Fang, C., Shang, Y., Xu, D.: MUFOLD-SS: new deep inception-inside-inception networks for protein secondary structure prediction. Proteins: Struct. Funct. Bioinform. 86(5), 592–598 (2018)CrossRefGoogle Scholar
  7. 7.
    Hanson, J., Paliwal, K., Litfin, T., Yang, Y., Zhou, Y.: Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks. Bioinformatics (2018)Google Scholar
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  9. 9.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  10. 10.
    Kabsch, W., Sander, C.: Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22(12), 2577–2637 (1983)CrossRefGoogle Scholar
  11. 11.
    Klausen, M.S., et al.: NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning. Proteins Struct. Funct. Bioinform. 87, 520–527 (2019)CrossRefGoogle Scholar
  12. 12.
    Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., Tramontano, A.: Critical assessment of methods of protein structure prediction (CASP) - round XII. Proteins: Struct. Funct. Bioinform. 86(Suppl. 1) (2017).  https://doi.org/10.1002/prot.25415CrossRefGoogle Scholar
  13. 13.
    Oldfield, C.J., Chen, K., Kurgan, L.: Computational prediction of secondary and supersecondary structures from protein sequences. In: Kister, A. (ed.) Protein Supersecondary Structures, pp. 73–100. Springer, New York (2019).  https://doi.org/10.1007/978-1-4939-9161-7_4CrossRefGoogle Scholar
  14. 14.
    Pauling, L., Corey, R.B., Branson, H.R.: The structure of proteins: two hydrogen-bonded helical configurations of the polypeptide chain. Proc. Natl. Acad. Sci. 37(4), 205–211 (1951).  https://doi.org/10.1073/pnas.37.4.205. https://www.pnas.org/content/37/4/205CrossRefGoogle Scholar
  15. 15.
    Pirovano, W., Heringa, J.: Protein secondary structure prediction. In: Carugo, O., Eisenhaber, F. (eds.) Data Mining Techniques for the Life Sciences, pp. 327–348. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-1-60327-241-4_19CrossRefGoogle Scholar
  16. 16.
    Remmert, M., Biegert, A., Hauser, A., Söding, J.: HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9(2), 173 (2012)CrossRefGoogle Scholar
  17. 17.
    Rost, B.: Protein secondary structure prediction continues to rise. J. Struct. Biol. 134(2–3), 204–218 (2001)CrossRefGoogle Scholar
  18. 18.
    Rost, B., Sander, C.: Improved prediction of protein secondary structure by use of sequence profiles and neural networks. Proc. Nat. Acad. Sci. 90(16), 7558–7562 (1993)CrossRefGoogle Scholar
  19. 19.
    Rost, B., Sander, C., Schneider, R.: Redefining the goal of protein secondary structure prediction. J. Mol. Biol. 235, 13–26 (1994).  https://doi.org/10.1016/S0022-2836(05)80007-5CrossRefGoogle Scholar
  20. 20.
    Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Sig. Process. 45(11), 2673–2681 (1997)CrossRefGoogle Scholar
  21. 21.
    Staples, M., Chan, L., Si, D., Johnson, K., Whyte, C., Cao, R.: Artificial intelligence for bioinformatics: applications in protein folding prediction (2019).  https://doi.org/10.1101/561027
  22. 22.
    Stephenson, N., et al.: Survey of machine learning techniques in drug discovery. Curr. Drug Metab. 19 (2018).  https://doi.org/10.2174/1389200219666180820112457CrossRefGoogle Scholar
  23. 23.
    Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)Google Scholar
  24. 24.
    Tai, C.H., Bai, H., Taylor, T.J., Lee, B.: Assessment of template-free modeling in CASP10 and ROLL. Proteins Struct. Funct. Bioinform. 82, 57–83 (2014)CrossRefGoogle Scholar
  25. 25.
    Torrisi, M., Kaleel, M., Pollastri, G.: Porter 5: fast, state-of-the-art AB initio prediction of protein secondary structure in 3 and 8 classes. bioRxiv, p. 289033 (2018)Google Scholar
  26. 26.
    Yang, Y., et al.: Sixty-five years of the long march in protein secondary structure prediction: the final stretch? Brief. Bioinform. 19(3), 482–494 (2016)Google Scholar
  27. 27.
    Zhang, W., Dunker, A.K., Zhou, Y.: Assessing secondary structure assignment of protein structures by using pairwise sequence-alignment benchmarks. Proteins: Struct. Funct. Bioinform. 71(1), 61–67 (2008).  https://doi.org/10.1002/prot.21654. https://onlinelibrary.wiley.com/doi/abs/10.1002/prot.21654CrossRefGoogle Scholar
  28. 28.
    Zhou, Y., Duan, Y., Yang, Y., Faraggi, E., Lei, H.: Trends in template/fragment-free protein structure prediction. Theor. Chem. Acc. 128(1), 3–16 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universita’ di PaviaPaviaItaly

Personalised recommendations