Skip to main content
SpringerLink
Log in

PSO Based Neuro-fuzzy Model for Secondary Structure Prediction of Protein

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Proteins may be defined as one of the most prominent structural and functional units of life. The structure of a protein is as diverse as the function it sustains. Knowledge about the structural folding of protein serves to ascertain its function. Experimental determination of protein function via its structure is a tedious and gradual process. Protein structure prediction thus; seeks to cultivate adequate ways aimed at providing plausible models for proteins whose structures remain unexplored. Hence, there is a dire need of computational tools to predict, evaluate and visualize the structures of unknown proteins from their amino acid sequences. However, the existing computational tools suffer from various drawbacks that affect their performance substantially like—low prediction accuracy, inefficient modeling of sequence–structure relationship, local methods lacking global exploration, and inability to suffice to the dynamic and exponentially growing data. Accordingly, swarm Intelligence based particle swarm optimization in combination with fuzzy sets has been introduced to propose a neural network based model for secondary structure prediction of protein. The data from six standard datasets namely- RS126, EVA6, CB396, CB513, Manesh and PSS504 has been utilized for the training and testing of the neural network. The model is evaluated using performance measures like- sensitivity, fallout, false discovery rate, miss-rate, specificity, false omission rate, precision, negative predictive value, Q3 accuracy and Matthews correlation coefficient. Sensitivity analysis and 10, 20, 30 and 40 fold cross validation has been performed for further verification of results. The proposed model resolves most of the issues addressed above, thus achieving an average Q3 accuracy of above 90% which is better than the existing tools for secondary structure prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Schiffer M, Edmundson AB (1967) Use of helical wheels to represent the structures of proteins and to identify segments with helical potential. Biophys J 7:121

    Google Scholar 

  2. Garnier J (1990) Protein structure prediction. Biochimie 72(8):513–524

    Google Scholar 

  3. Jones DT (1997) Progress in protein structure prediction. Curr Opin Struct Biol 7(3):377–387

    Google Scholar 

  4. Finkelstein AV, Ptitsyn OB (1971) Statistical analysis of the correlation among amino acid residues in helical, β structural and non-regular regions of globular proteins. J Mol Biol 62:613–624

    Google Scholar 

  5. Robson B, Pain RH (1971) Analysis of the code relating sequence to conformation in proteins: possible implications for the mechanism of formation of helical regions. J Mol Biol 58:237–259

    Google Scholar 

  6. Pain RH, Robson B (1970) Analysis of the code relating sequence to secondary structure in proteins. Nature 227:62–63

    Google Scholar 

  7. Levin JM, Garnier J (1988) Improvements in a secondary structure prediction method based on a search for local sequence homologies and its use as a model building tool. Biochim Biophys Acta 955:283–295

    Google Scholar 

  8. Muggleton S, King RD, Stenberg MJ (1992) Protein secondary structure prediction using logic-based machine learning. Protein Eng Des Sel 5(7):647–657

    Google Scholar 

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

    Google Scholar 

  10. Bohr H et al (1988) Protein secondary structure and homology by neural networks. FEBS Lett 241:223–228

    Google Scholar 

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

    Google Scholar 

  12. Maclin R, Shavlik JW (1993) Using knowledge-based neural networks to improve algorithms: refining the Chou Fasman algorithm for protein folding. Mach Learn 11:195–215

    Google Scholar 

  13. Stolorz P, Lapedes A, Xia Y (1992) Predicting protein secondary structure using neural net and statistical methods. J Mol Biol 225:363–377

    Google Scholar 

  14. Zhang Y (2008) Progress and challenges in protein structure prediction. Curr Opin Struct Biol 18(3):342–348

    Google Scholar 

  15. Kloczkowsk A et al (2013) New methods to improve protein structure prediction and refinement. Biophys J 104(2):229A

    Google Scholar 

  16. Sternberg MJE et al (1999) Progress in protein structure prediction: assessment of CASP3. Curr Opin Struct Biol 9(3):368–373

    Google Scholar 

  17. Koswatta TJ, Samaraweera P, Sumanasinghe VA (2011) A simple comparison between specific protein secondary structure prediction tools. Postgraduate Institute of Agriculture University of Peradeniya, Peradeniya

    Google Scholar 

  18. Sen T, Jernigan R, Garnier J, Kloczkowski A (2005) GOR V server for protein secondary structure prediction. Bioinformatics 21(11):2787–2788

    Google Scholar 

  19. Lin K, Simossis VA, Taylor WR (2005) A simple and fast secondary structure prediction method using hidden neural networks. Bioinformatics 21(2):152–159

    Google Scholar 

  20. Naderi-Manesh H, Sadeghi M, Araf S, Movahedi AAM (2001) Predicting of protein surface accessibility with information theory. Proteins 42:452–459

    Google Scholar 

  21. Rost B (1996) PHD: predicting one-dimensional protein structure by profile based neural networks. Methods Enzymol 266:525–539

    Google Scholar 

  22. Garnier J, Gibrat JF, Robson B (1996) GOR method for predicting protein secondary structure from amino acid sequence. Methods Enzymol 266:540–553

    Google Scholar 

  23. Altschul S, Gish W, Miller W, Myers E, Lipman D (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Google Scholar 

  24. Rost B, Sander C (1994) Combining evolutionary information and neural networks to predict protein secondary structure. Proteins 19(1):55–72

    Google Scholar 

  25. Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70% accuracy. J Mol Biol 232(2):584–599

    Google Scholar 

  26. Chou PY, Fasman UD (1974) Prediction of protein conformation. Biochemistry 13:211–215

    Google Scholar 

  27. Livingstone D (2008) Artificial neural networks: methods and applications. Springer, Berlin

    Google Scholar 

  28. Min S et al (2017) Deep learning in bioinformatics. Brief Bioinform 18(5):851–869

    Google Scholar 

  29. Lancashire LJ et al (2009) An introduction to artificial neural networks in bioinformatics—application to complex microarray and mass spectrometry datasets in cancer studies. Brief Bioinform 10(3):315–329

    Google Scholar 

  30. Hering JA, Innocent PR, Haris PI (2003) Neuro-fuzzy structural classification of proteins for improved protein secondary structure prediction. Proteomics 3:1464–1475

    Google Scholar 

  31. Bondugula R, Xu D (2007) MUPRED: a tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction. Proteins 66(3):664–670

    Google Scholar 

  32. Salamov AA, Solovyev VV (1995) Prediction of protein secondary structure by combining nearest-neighbor algorithms and multiple sequence alignments. J Mol Biol 247(1):11–15

    Google Scholar 

  33. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Google Scholar 

  34. Zhang X et al (2007) Improved particle swarm optimization algorithm for 2D protein folding prediction. In: 1st International conference on bioinformatics and biomedical engineering. IEEE

  35. Wu H et al (2011) A fuzzy adaptive particle swarm optimization for RNA secondary structure prediction. In: International conference on information science and technology, China

  36. Zheng W et al (2016) Protein secondary structure prediction via pigeon-inspired optimization. In: 2016 IEEE Chinese guidance, navigation and control conference (CGNCC)

  37. Li B et al (2014) (2014) Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model. Eng Appl Artif Intell 27:70–79

    Google Scholar 

  38. Chu D et al (2005) Parallel ant colony optimization for 3D protein structure prediction using the HP lattice model. In: Proceedings of the 19th IEEE international parallel and distributed processing symposium (IPDPS’05). IEEE

  39. Mansour N et al (2012) Particle swarm optimization approach for protein structure prediction in the 3D HP model. Interdiscip Sci Comput Life Sci 4(3):190–200

    Google Scholar 

  40. Khakzad H et al (2015) Accelerating protein structure prediction using particle swarm optimization on GPU. Biorxiv. https://doi.org/10.1101/022434

    Article  Google Scholar 

  41. Ahmed H, Glasgow J (2012) Swarm intelligence: concepts, models and applications. School of Computing, Queen’s University Kingston, Ontario

    Google Scholar 

  42. Yang XS et al (2013) Swarm intelligence and bio-inspired computation: theory and applications. Elsevier insights, Newnes, London

    Google Scholar 

  43. van der Merwe DW (2003) Data clustering using particle swarm optimization. In: The 2003 congress on evolutionary computation, CEC '03. IEEE

  44. Esmin AAA et al (2015) A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data. Artif Intell Rev 44(1):23–45

    Google Scholar 

  45. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, Perth, Australia. pp 1942–1948

  46. Saraswathi S et al (2012) Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction. J Mol Model 18(9):4275–4289

    Google Scholar 

  47. Makolo AU et al (2018) Protein secondary structure prediction using deep neural network and particle swarm optimization algorithm. Int J Comput Appl 181(2):975–8887

    Google Scholar 

  48. http://www.ebi.ac.uk/

  49. Alberts B, Bray D, Johnson A, Lewis J, Raff M, Roberts K, Walter P (2004) Essential cell biology, 2nd edn. Garland Science, New York

    Google Scholar 

  50. Branden C, Tooze J (1999) Introduction to protein structure. Garland Pub, New York

    Google Scholar 

  51. https://openstax.org/

  52. https://www.oracle.com/technetwork/java/javase & https://www.eclipse.org/luna/

  53. https://www.heatonresearch.com/encog/

  54. https://www.cs.waikato.ac.nz/ml/weka/

  55. Foundation TAS (2015) Apache commons CSV

  56. Dongardive J, Abraham S (2017) Reaching optimized parameter set: protein secondary structure prediction using neural network. Neural Comput Appl 28:1947

    Google Scholar 

  57. Reynolds CW (1987) Flocks, herds, and schools: a distributed behavioural model. In: Computer graphics (ACM SIGGRAPH ‘87 conference proceedings) 21(4):25–34

  58. Shi Y (2004) Feature article on particle swarm optimization. IEEE Neural Netw Soc 2:8–13

    Google Scholar 

  59. Parsopoulos KE, Vrahatis MN (2010) Particle swarm optimization and intelligence: advances and applications. Information Science Reference, Hershey

    Google Scholar 

  60. Rost B, Eyrich VA (2001) EVA: large-scale analysis of secondary structure prediction. Proteins 45(Suppl 5):192

    Google Scholar 

  61. Cuff JA, Barton GJ (1999) Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. Proteins 34:508

    Google Scholar 

  62. Gubbi J et al (2006) Protein secondary structure prediction using support vector machines and a new feature representation. In: 6th International journal of computational intelligence and applications. p 551

  63. Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

    MATH  Google Scholar 

  64. Pearson K (1904) Mathematical contribution to the theory of evolution. F.R.S., vol 3.

  65. Lee J (2006) Measures for the assessment of fuzzy predictions of protein secondary structure. Proteins Struct Funct Bioinform 65(2):453–462

    Google Scholar 

  66. Wang S, Peng J, Ma J et al (2016) Protein secondary structure prediction using deep convolutional neural fields. Sci Rep 6:1–11

    Google Scholar 

  67. Akbar S, Pardasani KR, Khan F (2021) Swarm optimization-based neural network model for secondary structure prediction of proteins. Netw Model Anal Health Inform Bioinform 10(1):1–9

    Google Scholar 

  68. McGuffin LJ, Bryson K, Jones DT (2000) The PSIPRED protein structure prediction server. Bioinformatics 16(4):404–405

    Google Scholar 

  69. Fai CY et al (2011) Optimized local protein structure with support vector machine to predict protein secondary structure. In: Proceedings of the 3rd Malaysian joint conference on artificial lntelligence, 20th–22nd July

  70. Magnan CN et al (2014) SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity. Bioinformatics 30(18):2592–2597

    Google Scholar 

  71. Heider D, Bartenhagen C, Dybowski JN, Hauke S, Pyka M, Hoffmann D (2014) Unsupervised dimension reduction methods for protein sequence classification. In: Spiliopoulou M, Schmidt-Thieme B, Janning R (eds) Data analysis, machine learning and knowledge discovery. Springer, Cham, pp 295–302

    Google Scholar 

  72. Bardini R, Politano G, Benso A, Di Carlo S (2017) Multi-level and hybrid modelling approaches for systems biology. Comput Struct Biotechnol J 15:396–402

    Google Scholar 

  73. Li Z, Yu Y (2016) Protein secondary structure prediction using cascaded convolutional and recurrent neural networks. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, IJCAI’16

  74. Heffernan R et al (2017) Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility. Bioinformatics 33(18):2842–2849

    Google Scholar 

  75. Wang Y et al (2017) Protein secondary structure prediction by using deep learning method. Knowl Based Syst 118(Suppl. C):115–123

    Google Scholar 

  76. Busia A (2017) Next-step conditioned deep convolutional neural networks improve protein secondary structure prediction. arXiv https://arxic.org/abs/1702.03865v1 [cs.LG]

  77. Cao C et al (2018) Deep learning and its applications in biomedicine. Genom Proteom Bioinform 16:17–32

    Google Scholar 

  78. Wardah W et al (2019) Protein secondary structure prediction using neural networks and deep learning: a review. Comput Biol Chem 81:1–8

    Google Scholar 

  79. Löchel HF, Eger D, Sperlea T, Heider D (2019) Deep learning on chaos game representation for proteins. Bioinformatics. https://doi.org/10.1101/575324

    Article  Google Scholar 

  80. Guo Y et al (2019) DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction. BMC Bioinform 20(1):1–12

    Google Scholar 

  81. Asgar E et al (2019) DeepPrime2Sec: deep learning for protein secondary structure prediction from the primary sequences. Bioinformatics. https://doi.org/10.1101/705426

    Article  Google Scholar 

  82. Shi K, Wang J, Tang Y, Zhong S (2020) Reliable asynchronous sampled-data filtering of T-S fuzzy uncertain delayed neural networks with stochastic switched topologies. Fuzzy Sets Syst 381:1–25

    MathSciNet  MATH  Google Scholar 

  83. Cai X, Zhong S, Wang J, Shi K (2020) Robust H∞ control for uncertain delayed TS fuzzy systems with stochastic packet dropouts. Appl Math Comput 385:125432

    MathSciNet  MATH  Google Scholar 

  84. Cai X, Wang J, Zhong S, Shi K, Tang Y (2021) Fuzzy quantized sampled-data control for extended dissipative analysis of T-S fuzzy system and its application to WPGSs. J Frankl Inst 358(2):1350–1375

    MathSciNet  MATH  Google Scholar 

  85. Wang J, Xia J, Shen H, Xing M, Park JH (2020) H∞ synchronization for fuzzy markov jump chaotic systems with piecewise-constant transition probabilities subject to PDT Switching rule. IEEE Trans Fuzzy Systems. https://doi.org/10.1109/TFUZZ.2020.3012761

    Article  Google Scholar 

  86. Liu X, Xia J, Wang J et al (2021) Interval type-2 fuzzy passive filtering for nonlinear singularly perturbed PDT-switched systems and its application. J Syst Sci Complex. https://doi.org/10.1007/s11424-020-0106-9

    Article  Google Scholar 

  87. AlGhamdi R, Aziz A, Alshehri M et al (2020) Deep learning model with ensemble techniques to compute the secondary structure of proteins. J Supercomput. https://doi.org/10.1007/s11227-020-03467-9

    Article  Google Scholar 

  88. Ray SS, Pal SK (2012) RNA secondary structure prediction using soft computing. IEEE/ACM Trans Comput Biol Bioinform 10(1):2–17

    Google Scholar 

  89. Abbasi E, Ghatee M, Shiri ME (2013) FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds. Comput Biol Med 43(9):1182–1191

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, MANIT, Bhopal, 462003, India

    Sana Akbar

  2. Department of Mathematics, MANIT, Bhopal, 462003, India

    Kamal Raj Pardasani & Nihar Ranjan Panda

Authors
  1. Sana Akbar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamal Raj Pardasani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nihar Ranjan Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sana Akbar.

Ethics declarations

Conflict of interest

We wish to confirm that there are no known conflicts of interest associated with this publication.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akbar, S., Pardasani, K.R. & Panda, N.R. PSO Based Neuro-fuzzy Model for Secondary Structure Prediction of Protein. Neural Process Lett 53, 4593–4612 (2021). https://doi.org/10.1007/s11063-021-10615-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-021-10615-6

Keywords

Search

Navigation