Improving protein secondary structure prediction: the evolutionary optimized classification algorithms

  • Cyrus Ahmadi Toussi
  • Javad HaddadniaEmail author
Original Research


Determining protein structures plays an important role in the field of drug design. Currently, the machine learning methods including artificial neural network (ANN) and support vector machine (SVM) have replaced the experimental techniques to determine these structures. However, as these predictions are increasingly becoming the workhorse for numerous methods aimed at predicting protein structure and function, it still needs to be improved. In this study, evolutionary optimized neural network (EONN) and evolutionary optimized support vector machine (EOSVM) were applied to predict protein secondary structure using GA, DE, and PSO. Despite the simplicity of the applied methods, the results are found to be superior to those achieved through other techniques. The EONN and EOSVM modestly improved the accuracy by 6% and 5% on the same database, respectively.


Protein secondary structure prediction (PSSP) Neural network (NN) Evolutionary algorithms (EA) Evolutionary neural network 



I would like to extend my sincere thanks to my dear friend Dr. Hamidreza Hashemi Moghadam for correcting the possible grammatical errors of different drafts of this article.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Lee J, Freddolino PL, Zhang Y (2017) Ab initio protein structure prediction. From protein structure to function with bioinformatics. Springer, pp, pp 3–35CrossRefGoogle Scholar
  2. 2.
    Borguesan B, Bohrer J, e Silva MB, et al (2016) Improving protein tertiary structure prediction with conformational propensities of amino acid residues. In: Evolutionary Computation (CEC), 2016 IEEE Congress on. IEEE, pp 9–15Google Scholar
  3. 3.
    Wang L, Duan C, Wang D, et al (2016) Prediction of protein tertiary structural classes based on ensemble learning. In: Informative and Cybernetics for Computational Social Systems (ICCSS), 2016 3rd International Conference on IEEE, pp 68–71Google Scholar
  4. 4.
    Toussi CA, Soheilifard R (2017) A better prediction of conformational changes of proteins using minimally connected network models. Phys Biol 13:66013CrossRefGoogle Scholar
  5. 5.
    Soheilifard R, Toussi CA (2016) On the contribution of normal modes of elastic network models in prediction of conformational changes. In: Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016 23rd Iranian Conference on IEEE, pp 263–266Google Scholar
  6. 6.
    Ahmadi TC, Soheilifard R (2016) Evaluating elastic network models in prediction of conformational changes of proteinsGoogle Scholar
  7. 7.
    Zhang Y (2008) Progress and challenges in protein structure prediction. Curr Opin Struct Biol 18:342–348CrossRefGoogle Scholar
  8. 8.
    Rost B, Sander C (1993) Improved prediction of protein secondary structure by use of sequence profiles and neural networks. Proc Natl Acad Sci 90:7558–7562CrossRefGoogle Scholar
  9. 9.
    Otero-Cruz JD, Torres-Núñez DA, Báez-Pagán CA, Lasalde-Dominicci JA (2008) Fourier transform coupled to tryptophan-scanning mutagenesis: lessons from its application to the prediction of secondary structure in the acetylcholine receptor lipid-exposed transmembrane domains. Biochim Biophys Acta (BBA)-Proteins Proteomics 1784:1200–1207CrossRefGoogle Scholar
  10. 10.
    Zhang G-Z, Huang D-S, Wang H-Q (2004) Protein secondary structure prediction based on the amino acids conformational classification and neural network technique. In: Acoustics, speech, and signal processing, 2004. Proceedings.(ICASSP’04). IEEE international conference on. IEEE, p V-573Google Scholar
  11. 11.
    Holley LH, Karplus M (1989) Protein secondary structure prediction with a neural network. Proc Natl Acad Sci 86:152–156CrossRefGoogle Scholar
  12. 12.
    Kapoor N, Ohri J (2014) Evolutionary optimized neural network (EONN) based motion control of manipulator. Int J Intell Syst Appl 6:10Google Scholar
  13. 13.
    Ghayoumi H (2016) Diagnosis of breast cancer and clustering technique using thermal indicators exposed by infrared imagesGoogle Scholar
  14. 14.
    Fiuzy JHHVM, Qarehkhani A, Haddadnia J, Varharam H (2013) Introduction of a method to diabetes diagnosis according to optimum rules in fuzzy systems based on combination of data mining algorithm (dt), evolutionary algorithms (aco) and artificial neural networks (nn). J Math Comput Sci 6:272–285CrossRefGoogle Scholar
  15. 15.
    Kim H, Park H (2017) Protein secondary structure prediction based on an improved support vector machines approach 16:553–560.
  16. 16.
    Guo J, Chen H, Sun Z, Lin Y (2004) A novel method for protein secondary structure prediction using dual-layer SVM and profiles. PROTEINS Struct Funct Bioinforma 54:738–743CrossRefGoogle Scholar
  17. 17.
    Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232:584–599CrossRefGoogle Scholar
  18. 18.
    Busia A, Jaitly N (2017) Next-step conditioned deep convolutional neural networks improve protein secondary structure prediction. arXiv Prepr arXiv170203865Google Scholar
  19. 19.
    Rost B, Sander C (1994) Combining evolutionary information and neural networks to predict protein secondary structure 72:55–72Google Scholar
  20. 20.
    Riis SK, Krogh A (1996) Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. J Comput Biol 3:163–183CrossRefGoogle Scholar
  21. 21.
    Chandonia J, Karplus M (1996) The importance of larger data sets for protein secondary structure prediction with neural networks. Protein Sci 5:768–774CrossRefGoogle Scholar
  22. 22.
    Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297Google Scholar
  23. 23.
    Hu H-J, Pan Y, Harrison R, Tai PC (2004) Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier. IEEE Trans Nanobioscience 3:265–271CrossRefGoogle Scholar
  24. 24.
    Hossain A, Zaman F, Nasser M, Islam MM (2009) Comparison of GARCH, neural network and support vector machine in financial time series prediction. International conference on pattern recognition and machine intelligence. Springer, pp 597–602Google Scholar
  25. 25.
    Liong S, Sivapragasam C (2002) Flood stage forecasting with support vector machines. JAWRA. J Am Water Resour Assoc 38:173–186CrossRefGoogle Scholar
  26. 26.
    Reyaz-ahmed AB (2007) Protein secondary structure prediction using support vector machines. Neural networks and genetic algorithmsGoogle Scholar
  27. 27.
    Ghosh A, Parai B (2008) Protein secondary structure prediction using distance based classifiers. Int J Approx Reason 47:37–44CrossRefGoogle Scholar
  28. 28.
    Spencer M, Eickholt J, Cheng J (2015) A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Trans Comput Biol Bioinforma 12:103–112CrossRefGoogle Scholar
  29. 29.
    Ibrahim AA, Yasseen IS (2017) Using neural networks to predict secondary structure for protein folding. J Comput Commun 5:1–8CrossRefGoogle Scholar
  30. 30.
    Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637CrossRefGoogle Scholar
  31. 31.
    Hendy H, Khalifa W, Roushdy M, Salem AB (2016) The effect of using different neural networks architectures on the protein secondary structure prediction. 58–71Google Scholar
  32. 32.
    Hua S, Sun Z (2001) A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 308:397–407CrossRefGoogle Scholar
  33. 33.
    Tsilo LC (2008) Protein secondary structure prediction using neural networks. Rhodes UnivGoogle Scholar
  34. 34.
    Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ijcai. Montreal, Canada, pp 1137–1145Google Scholar
  35. 35.
    Grimm KJ, Mazza GL, Davoudzadeh P (2017) Model selection in finite mixture models: a k-fold cross-validation approach. Struct Equ Model A Multidiscip J 24:246–256CrossRefGoogle Scholar
  36. 36.
    Mondal NI, Mamun A, Saha S (2015) Study of protein secondary structure prediction using support vector machine 8801912744:5–9Google Scholar
  37. 37.
    Xiong H, Buckwalter BL, Shieh H-M, Hecht MH (1995) Periodicity of polar and nonpolar amino acids is the major determinant of secondary structure in self-assembling oligomeric peptides. Proc Natl Acad Sci 92:6349–6353CrossRefGoogle Scholar
  38. 38.
    Huang Y-F, Chen S-Y (2013) Extracting physicochemical features to predict protein secondary structure. Sci World J 2013Google Scholar
  39. 39.
    Dongardive J, Abraham S (2017) Reaching optimized parameter set: protein secondary structure prediction using neural network. Neural Comput Appl 28:1947–1974. CrossRefGoogle Scholar
  40. 40.
    Li Z-C, Zhou X-B, Dai Z, Zou X-Y (2009) Prediction of protein structural classes by Chou’s pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis. Amino Acids 37:415CrossRefGoogle Scholar
  41. 41.
    Garnier J, Osguthorpe DJ, Robson B (1978) Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 120:97–120CrossRefGoogle Scholar
  42. 42.
    Pollastri G, Przybylski D, Rost B, Baldi P (2002) Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins Struct Funct Bioinforma 47:228–235CrossRefGoogle Scholar
  43. 43.
    Frishman D, Argos P (1997) Seventy-five percent accuracy in protein secondary structure prediction. Proteins-Structure Funct Genet 27:329–335CrossRefGoogle Scholar
  44. 44.
    Bystroff C, Thorsson V, Baker D (2000) HMMSTR: a hidden Markov model for local sequence-structure correlations in proteins. J Mol Biol 301:173–190CrossRefGoogle Scholar
  45. 45.
    Wei Y, Thompson J, Floudas CA (2011) CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization. In: Proc. R. Soc. A. The Royal Society, p rspa20110514Google Scholar
  46. 46.
    Chou PY, Fasman GD (1974) Prediction of protein conformation. Biochemistry 13:222–245CrossRefGoogle Scholar
  47. 47.
    Mount DW (2004) Bioinformatics: sequence and genome analysis. Bioinforma Seq Genome AnalGoogle Scholar
  48. 48.
    Nishikawa K (1983) Assessment of secondary-structure prediction of proteins comparison of computerized Chou-Fasman method with others. Biochim Biophys Acta (BBA)-Protein Struct Mol Enzymol 748:285–299CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringHakim Sabzevari UniversitySabzevarIran

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