Skip to main content
SpringerLink
Log in

Linear B-cell epitopes prediction using bagging based proposed ensemble model

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Vaccine design through experimental analysis is too costly and time taking process. By using computational intelligence approaches, cost and time can be reduced. Identification of linear B-cell epitopes is the main concern of peptide vaccine designs, immunodiagnosis, and antibody productions. It can be performed by developing a suitable machine learning model. In this paper, prediction of linear B-cell epitopes has been performed by using a bagging-based proposed ensemble model. The goal of using a bagging-based ensemble approach is to improve the prediction performance of various base classifiers. Here, five existing base classifiers are combined for ensembling to predict the B-cell epitopes or non-epitopes. This prediction is based on a bagging-based voting system, which is performed under binary class classification. The proposed ensemble model achieved 82.06% accuracy, which is better than to some existing models. Finally, the proposed ensemble model has been tested using sevenfold cross-validation, and where it provided almost consistent performance.

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

Similar content being viewed by others

References

  1. Khanna D, Rana PS (2019) Improvement in prediction of antigenic epitopes using stacked generalization: an ensemble approach. IET Sys Bio 14(1):1–7. https://doi.org/10.1049/iet-syb.2018.5083

    Article  Google Scholar 

  2. Jose L, Marta Pedro A (2017) Fundamentals and methods for T-and B-cell epitope prediction. J Immunol Res 2017:1–14. https://doi.org/10.1155/2017/2680160

    Article  Google Scholar 

  3. Reth M, Hombach J, Wienands J, Campbell KS, Chien N, Justement LB, Cambier JC (1991) The B-cell antigen receptor complex. Immunol Today 12(6):196–201. https://doi.org/10.1016/0167-5699(91)90053-V

    Article  Google Scholar 

  4. Saha S, Raghava GPS (2004) Bcepred: prediction of continuous b-cell epitopes in antigenic sequences using physicochemical properties. In: International Conference on Artificial Immune Systems, Catania, pp 197–204

  5. Odorico M, Jean-Luc P (2003) Bepitope: predicting the location of continuous epitopes and patterns in proteins. J Mol Recognit 16(1):20–22. https://doi.org/10.1002/jmr.602

    Article  Google Scholar 

  6. Pellequer JL, Westhof E, Van Regenmortel MHV (1993) Correlation between the location of antigenic sites and the prediction of turns in proteins. Immunol Lett 36(1):83–99

    Article  Google Scholar 

  7. Alix AJP (1999) Predictive estimation of protein linear epitopes by using the program people. Vaccine 18(3):311–314

    Article  Google Scholar 

  8. Pellequer JL, Westhof E, Van Regenmortel MH (2004) Predicting location of continuous epitopes in proteins from their primary structures. Method Enzymol 203:176–201. https://doi.org/10.1016/0076-6879(91)03010-E

    Article  Google Scholar 

  9. Saha S, Raghava GPS (2006) Prediction of continuous b-cell epitopes in an antigen using recurrent neural network. Proteins: Structure Function Bioinformatics 65(1):40–48. https://doi.org/10.1002/prot.21078

    Article  Google Scholar 

  10. El-Manzalawy Y, Dobbs D, Honavar V (2008) Predicting linear b-cell epitopes using string kernels. J Mol Recognit 21(4):243–255. https://doi.org/10.1002/jmr.893

    Article  Google Scholar 

  11. Sun P, Ju H, Liu Z et al (2013) Bioinformatics resources and tools for conformational B-cell epitope prediction. Comput Math Methods Med 13(1):943636–943636

    Google Scholar 

  12. Greenbaum JA, Andersen PH, Blythe M et al (2007) Towards a consensus on datasets and evaluation metrics for developing b-cell epitope prediction tools. J Mol Recognit 20(2):75–82. https://doi.org/10.1002/jmr.815

    Article  Google Scholar 

  13. Yao B, Zheng D, Liang S et al (2013) Conformational B-cell epitope prediction on antigen protein structures: a review of current algorithms and comparison with common binding site prediction methods. PLoS ONE 8(4):e62249. https://doi.org/10.1371/journal.pone.0062249

    Article  Google Scholar 

  14. Luscombe NM, Greenbaum D, Gerstein M (2001) What is bioinformatics? A proposed definition and overview of the field. Method Inform Med 40(4):346–358

    Article  Google Scholar 

  15. Larranaga P, Calvo B, Santana R et al (2006) Machine learning in bioinformatics. Brief Bioinf 7(1):86–112

    Article  Google Scholar 

  16. Chen J, Liu H, Yang J, Chou KC (2007) Prediction of linear bcell epitopes using amino acid pair antigenicity scale. Amino Acids 33(3):423–428. https://doi.org/10.1007/s00726-006-0485-9

    Article  Google Scholar 

  17. Yao B, Zhang L, Liang S, Zhang C (2012) SVMTrip: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PLoS ONE 7(9):e45152. https://doi.org/10.1371/journal.pone.0045152

    Article  Google Scholar 

  18. Wee LJK, Simarmata D, Kam YW, Lisa FP, Tong JC (2010) SVM-based prediction of linear B-cell epitopes using bayes feature extraction. BMC Genomics 11(4):S21

    Article  Google Scholar 

  19. Lin SY, Cheng CW, Chia-Yu SE (2013) Prediction of B-cell epitopes using evolutionary information and propensity scales. BMC Bioinf 14(2):S10

    Article  Google Scholar 

  20. Wang Y, Wu W, Nicolas N, Negre NN et al (2011) Determinants of antigenicity and specificity in immune response for protein sequences. BMC Bioinf 12(1):251. https://doi.org/10.1186/1471-2105-12-251

    Article  Google Scholar 

  21. Haung JH, Wen M, Li-Juan T, Hua-Lin X et al (2014) Using random forest to classify linear b-cell epitopes based on amino acid properties and molecular features. Biochimie 103:1–6. https://doi.org/10.1016/j.biochi.2014.03.016

    Article  Google Scholar 

  22. Gupta VK, Rana PS (2021) Ensemble technique for toxicity prediction of small drug molecules of the antioxidant response element signaling pathway. Comput J. https://doi.org/10.1093/comjnl/bxaa001

    Article  Google Scholar 

  23. Singh H, Ansari HR, Raghava GPS (2013) Improved method for linear B-cell epitope prediction using antigen’s primary sequence. PLoS ONE. https://doi.org/10.1371/journal.pone.0062216

    Article  Google Scholar 

  24. Singh H, Ansari HR, Raghava GPS (2020) Lbtope: linear b-cell epitope prediction server. http://crdd.osdd.net/raghava/lbtope/data.php. Accessed 06 Dec 2020

  25. Gupta VK, Rana PS (2019) Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model. J Bioinf Comput Biol 17(5):1950033. https://doi.org/10.1142/S0219720019500331

    Article  Google Scholar 

  26. Khanna D, Rana PS (2019) Ensemble technique for prediction of T-cell mycobacterium tuberculosis epitopes. Interdiscip Sci 11(4):611–627. https://doi.org/10.1007/s12539-018-0309-0

    Article  Google Scholar 

  27. Terry T, Beth A, Brian R (2020) rpart, Package ‘rpart’—The R Project for Statistical Computing’, https://cran.rproject.org/web/packages/rpart/rpart.pdf. Accessed 12 Oct 2020

  28. Andy L, Matthew W (2021) randomForest, Package ‘random-Forest’ - The R Project for Statistical Computing. https://cran.r-project.org/web/packages/randomForest/randomForest.pdf. Accessed 18 Oct 2020

  29. Brian R, William V (2020) Package ‘nnet’- R Project for Statistical Computing. https://cran.rproject.org/web/packages/nnet/nnet.pdf. Accessed 12 Oct 2021

  30. Mark C, Kjell J, George M (2020) Ada, The R Package ‘Ada’ for Stochastic Boosting. https://cran.rproject.org/web/packages/ada/ada.pdf. Accessed 15 Sept 2020

  31. David M, Evgenia D (2019) e1071, The R Package for ‘support vector machine’. https://cran.r-project.org/web/packages/e1071/e1071.pdf. Accessed 12 Nov 2019

  32. Gupta VK, Rana PS (2019) Activity assessment of small drug molecules in estrogen receptor using multilevel prediction model. IET sys bio. 13(3):147–158. https://doi.org/10.1049/iet-syb.2018.5068

    Article  Google Scholar 

  33. Gupta VK, Rana PS (2020) Toxicity prediction of pre-clinical trial drugs using physicochemical properties and computational intelligence approaches. Dissertation, Thapar Institute of Engineering and Technology, Patiala

  34. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques, 3rd edn. Elsevier, USA

    MATH  Google Scholar 

  35. Gupta VK, Rana PS (2021) Toxicity prediction of small drug molecules of aryl hydrocarbon receptor using a proposed ensemble model. Turk J Electr Eng Co 24(4):2833–2849. https://doi.org/10.3906/elk-1809-9

    Article  Google Scholar 

  36. Achuthsankar SN, Aswathi B (2020) ‘Sensitivity, specificity, accuracy and the relationship between them’, http://www.lifenscience.com/bioinformatics/sensitivity-specificity-accuracy-and. Accessed 7 Jan 2020

Download references

Acknowledgements

We are very much thankful to the LBtope server for making the data, which is available to the public. We are also thankful for the various web servers, which have been developed to predict B-cell epitopes or non-epitopes in an antigenic sequence.

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Graphic Era Deemed to be University, Dehradun, India

    Vishan Kumar Gupta, Avdhesh Gupta, Paras Jain & Pankaj Kumar

  2. Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

    Vishan Kumar Gupta & Pankaj Kumar

  3. IMS Engineering College, Ghaziabad, UP, India

    Avdhesh Gupta

  4. VIT Bhopal University, Bhopal, MP, India

    Paras Jain

Authors
  1. Vishan Kumar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Avdhesh Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paras Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pankaj Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vishan Kumar Gupta.

Ethics declarations

Conflict of interest

The authors declared no conflicts of interest with respect to the authorship, research, and publication of this manuscript.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gupta, V.K., Gupta, A., Jain, P. et al. Linear B-cell epitopes prediction using bagging based proposed ensemble model. Int. j. inf. tecnol. 14, 3517–3526 (2022). https://doi.org/10.1007/s41870-022-00951-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-022-00951-8

Keywords

Search

Navigation