Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

Abstract

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions and solvent accessibility.

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References 79

1. 1.

   Baker D. & Sali A. Protein structure prediction and structural genomics. Science 294, 93–96 (2001 ).

   Not in our library10.1126/science.1065659

2. 2.

   Dill K. A. & MacCallum J. L. The protein-folding problem, 50 years on. Science 338, 1042–1046 (2012 ).

   Not in our library10.1126/science.1219021

3. 3.

   Petsko G. A. & Ringe D. Protein structure and function. (New Science Press, 2004 ).

   Not in our library

4. 4.

   Whittle P. J. & Blundell T. L. Protein structure-based drug design. Annu. Rev. Biophys. Biomol. Struct. 23, 349–375 (1994 ).

   Not in our library10.1146/annurev.bb.23.060194.002025

5. 5.

   Schaffhausen J. Advances in structure-based drug design. Trends Pharmacol. Sci. 33, 223 (2012 ).

   Not in our library10.1016/j.tips.2012.03.011

6. 6.

   Whisstock J. C. & Lesk A. M. Prediction of protein function from protein sequence and structure. Q. Rev. Biophys. 36, 307–340 (2003 ).

   Not in our library10.1017/s0033583503003901

7. 7.

   Lee D., Redfern O. & Orengo C. Predicting protein function from sequence and structure. Nat. Rev. Mol. Cell Biol. 8, 995–1005 (2007 ).

   Not in our library10.1038/nrm2281

8. 8.

   Radivojac P. et al. A large-scale evaluation of computational protein function prediction. Nat. Methods 10, 221–227 (2013 ).

   Not in our library10.1038/nmeth.2340

9. 9.

   Rost B., Yachdav G. & Liu J. The predictprotein server. Nucleic Acids Res. 32, W321–W326 (2004 ).

   Not in our library10.1093/nar/gkh377

10. 10.

    Lin K., Simossis V. A., Taylor W. R. & Heringa J. A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 21, 152–159 (2005 ).

    Not in our library10.1093/bioinformatics/bth487

11. 11.

    Yoo P. D., Zhou B. B. & Zomaya A. Y. Machine learning techniques for protein secondary structure prediction: an overview and evaluation. Current Bioinformatics 3, 74–86 (2008 ).

    Not in our library

12. 12.

    Faraggi E., Zhang T., Yang Y., Kurgan L. & Zhou Y. SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J. Comp. Chem. 33, 259–267 (2012 ).

    Not in our library10.1002/jcc.21968

13. 13.

    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. USA 37, 205–211 (1951 ).

    Not in our library10.1073/pnas.37.4.205

14. 14.

    Kabsch W. & Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen - bonded and geometrical features. Biopolymers 22, 2577–2637 (1983 ).

    Not in our library10.1002/bip.360221211

15. 15.

    Myers J. K. & Oas T. G. Preorganized secondary structure as an important determinant of fast protein folding. Nat. Struct. Mol. Biol. 8, 552–558 (2001 ).

    Not in our library10.1038/88626

16. 16.

    Källberg M. et al. Template-based protein structure modeling using the RaptorX web server. Nature protocols 7, 1511–1522 (2012 ).

    Not in our library10.1038/nprot.2012.085

17. 17.
    I-TASSER server for protein 3D structure prediction · 2008

    No notice
18. 18.

    Simons K. T., Kooperberg C., Huang E. & Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. J. Mol. Biol. 268, 209–225 (1997 ).

    Not in our library10.1006/jmbi.1997.0959

19. 19.

    Holley L. H. & Karplus M. Protein secondary structure prediction with a neural network. Proc. Natl. Acad. Sci. USA 86, 152–156 (1989 ).

    Not in our library10.1073/pnas.86.1.152

20. 20.

    Qian N. & Sejnowski T. J. Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 202, 865–884 (1988 ).

    Not in our library10.1016/0022-2836(88)90564-5

21. 21.

    Kneller D., Cohen F. & Langridge R. Improvements in protein secondary structure prediction by an enhanced neural network. J. Mol. Biol. 214, 171–182 (1990 ).

    Not in our library10.1016/0022-2836(90)90154-e

22. 22.

    Asai K., Hayamizu S. & Handa K. I. Prediction of protein secondary structure by the hidden Markov model. Comput. Appl. Biosci. 9, 141–146 (1993 ).

    Not in our library10.1093/bioinformatics/9.2.141

23. 23.

    Rost B. & Sander C. Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol. 232, 584–599 (1993 ).

    Not in our library10.1006/jmbi.1993.1413

24. 24.

    Jones D. T. Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292, 195–202 (1999 ).

    Not in our library10.1006/jmbi.1999.3091

25. 25.

    Schmidler S. C., Liu J. S. & Brutlag D. L. Bayesian segmentation of protein secondary structure. J. Comput. Biol. 7, 233–248 (2000 ).

    Not in our library10.1089/10665270050081496

26. 26.

    Pollastri G., Przybylski D., Rost B. & Baldi P. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins: Struct. Funct. Bioinform. 47, 228–235 (2002 ).

    Not in our library10.1002/prot.10082

27. 27.

    Kim H. & Park H. Protein secondary structure prediction based on an improved support vector machines approach. Protein Eng. 16, 553–560 (2003 ).

    Not in our library10.1093/protein/gzg072

28. 28.

    Ward J. J., McGuffin L. J., Buxton B. F. & Jones D. T. Secondary structure prediction with support vector machines. Bioinformatics 19, 1650–1655 (2003 ).

    Not in our library10.1093/bioinformatics/btg223

29. 29.

    Chu W., Ghahramani Z. & Wild D. L. A graphical model for protein secondary structure prediction. Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada, July 4-8, 2004. ACM International Conference Proceeding Series 69, ACM 2004, 21 (2004 ).

    Not in our library

30. 30.

    Guo J., Chen H., Sun Z. & Lin Y. A novel method for protein secondary structure prediction using dual - layer SVM and profiles. Proteins: Struct. Funct. Bioinform. 54, 738–743 (2004 ).

    Not in our library10.1002/prot.10634

31. 31.

    Aydin Z., Altunbasak Y. & Borodovsky M. Protein secondary structure prediction for a single-sequence using hidden semi-Markov models. BMC Bioinformatics 7, 178 (2006 ).

    Not in our library10.1186/1471-2105-7-178

32. 32.

    Im I. G. Predicting Protein Secondary Structure Using Markov Chain Monte-Carlo Simulation. (ProQuest, 2008 ).

    Not in our library

33. 33.

    Wang Z., Zhao F., Peng J. & Xu J. Protein 8 - class secondary structure prediction using conditional neural fields. Proteomics 11, 3786–3792 (2011 ).

    Not in our library10.1002/pmic.201100196

34. 34.

    Magnan C. N. & Baldi P. SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics 30, 2592–2597 (2014 ).

    Not in our library10.1093/bioinformatics/btu352

35. 35.

    Zvelebil M. J., Barton G. J., Taylor W. R. & Sternberg M. J. Prediction of protein secondary structure and active sites using the alignment of homologous sequences. J. Mol. Biol. 195, 957–961 (1987 ).

    Not in our library10.1016/0022-2836(87)90501-8

36. 36.

    Zhou J. & Troyanskaya O. Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction. Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21-26 June 2014. JMLR Proceedings 32, 745-753 (2014 ).

    Not in our library

37. 37.

    Baldi P., Brunak S., Frasconi P., Soda G. & Pollastri G. Exploiting the past and the future in protein secondary structure prediction. Bioinformatics 15, 937–946 (1999 ).

    Not in our library10.1093/bioinformatics/15.11.937

38. 38.

    Higgins D. G. & Sharp P. M. CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene 73, 237–244 (1988 ).

    Not in our library10.1016/0378-1119(88)90330-7

39. 39.

    Edgar R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004 ).

    Not in our library10.1093/nar/gkh340

40. 40.

    Thompson J. D., Higgins D. G. & Gibson T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994 ).

    Not in our library10.1093/nar/22.22.4673

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