Sage Journals: Discover world-class research

Abstract

Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. However, reliable prediction of linear B-cell epitope remains a formidable challenge. Earlier studies have reported discrepancy in amino acid composition between the epitopes and non-epitopes. Hence, this study proposed and developed a novel amino acid composition-based feature descriptor, Dipeptide Deviation from Expected Mean (DDE), to distinguish the linear B-cell epitopes from non-epitopes effectively. In this study, for the first time, only exact linear B-cell epitopes and non-epitopes have been utilized for developing the prediction method, unlike the use of epitope-containing regions in earlier reports. To evaluate the performance of the DDE feature vector, models have been developed with two widely used machine-learning techniques Support Vector Machine and AdaBoost-Random Forest. Five-fold cross-validation performance of the proposed method with error-free dataset and dataset from other studies achieved an overall accuracy between nearly 61% and 73%, with balance between sensitivity and specificity metrics. Performance of the DDE feature vector was better (with accuracy difference of about 2% to 12%), in comparison to other amino acid-derived features on different datasets. This study reflects the efficiency of the DDE feature vector in enhancing the linear B-cell epitope prediction performance, compared to other feature representations. The proposed method is made as a stand-alone tool available freely for researchers, particularly for those interested in vaccine design and novel molecular target development for systems therapeutics and diagnostics: https://github.com/brsaran/LBEEP

Get full access to this article

View all access options for this article.

References

Bhasin

, and Raghava

. (2004). ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST. Nucleic Acids Res, 32, W414–419.

Blythe

, and Flower

. (2005). Benchmarking B cell epitope prediction: Underperformance of existing methods. Protein Sci, 14, 246–248.

Breiman

. (2001). Random forests. Machine Learning, 45, 5–32.

Carr

, Murray

, Armah

, He

, and Yau

. (2010). A rapid method for characterization of protein relatedness using feature vectors. PLoS ONE, 5, e9550.

Chawla

. (2010) Data mining for imbalanced datasets: An overview. In: Data Mining and Knowledge Discovery Handbook. Springer, 875–886.

Chen

, Liu

, Yang

, and Chou

. (2007). Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids, 33, 423–428.

Chou

, and Fasman

. (1978). Prediction of the secondary structure of proteins from their amino acid sequence. Adv Enzymol Relat Areas Mol Biol, 47, 45–148.

Cortes

, and Vapnik

. (1995). Support-vector networks. Machine Learning, 20, 273–297.

Desai

, and Kulkarni-Kale

. (2014). T-cell epitope prediction methods: An overview. In: Immunoinformatics. Springer, pp. 333–364.

10.

Dhanda

, Gupta

, Vir

, and Raghava

. (2013). Prediction of IL4 inducing peptides. Clin Devel Immunol, 2013, 263952.

11.

Dudek

, Perlmutter

, Aguilar

, Croft

, and Purcell

. (2010). Epitope discovery and their use in peptide based vaccines. Curr Pharm Des, 16, 3149–3157.

12.

Dwivedi

, Muthusamy

, Kumar

, et al. (2014). Brain proteomics of Anopheles gambiae. OMICS, 18, 421–437.

13.

El-Manzalawy

, Dobbs

, and Honavar

. (2008). Predicting linear B-cell epitopes using string kernels. J Mol Recognit, 21, 243–255.

14.

El-Manzalawy

, and Honavar

(2005). WLSVM: Integrating libsvm into weka environment. Software available at http://www.cs.iastate.edu/yasser/wlsvm.

15.

Freund

, and Schapire

. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci, 55, 119–139.

16.

Gupta

, Ansari

, Gautam

, and Raghava

. (2013). Identification of B-cell epitopes in an antigen for inducing specific class of antibodies. Biol Direct, 8, 27.

17.

Hall

, Frank

, Holmes

, Pfahringer

, Reutemann

, and Witten

. (2009). The WEKA data mining software: An update. ACM SIGKDD Explor Newslett, 11, 10–18.

18.

Huang

, Niu

, Gao

, Fu

, and Li

. (2010). CD-HIT Suite: A web server for clustering and comparing biological sequences. Bioinformatics, 26, 680–682.

19.

Kolaskar

, and Tongaonkar

. (1990). A semi-empirical method for prediction of antigenic determinants on protein antigens. FEBS Lett, 276, 172–174.

20.

Kotsiantis

, Kanellopoulos

, and Pintelas

. (2006). Handling imbalanced datasets: A review. GESTS Intl Transact Computer Sci Engineer, 30, 25–36.

21.

Kumar

, and Raghava

. (2013). Hybrid approach for predicting coreceptor used by HIV-1 from its V3 loop amino acid sequence. PLoS ONE, 8, e61437.

22.

Lian

, Ge

, and Pan

X-M

. (2014). EPMLR: Eequence-based linear B-cell epitope prediction method using multiple linear regression. BMC Bioinformat, 15, 1–6.

23.

Longuespée

, Fléron

, Pottier

, et al. (2014). Tissue proteomics for the next decade? Towards a molecular dimension in histology. Omics, 18, 539–552.

24.

Parker

, Guo

, and Hodges

. (1986). New hydrophilicity scale derived from high-performance liquid chromatography peptide retention data: Correlation of predicted surface residues with antigenicity and X-ray-derived accessible sites. Biochemistry, 25, 5425–5432.

25.

Pellequer

, Westhof

, and Van Regenmortel

. (1991). Predicting location of continuous epitopes in proteins from their primary structures. Methods Enzymol, 203, 176–201.

26.

Ponomarenko

, and Van Regenmortel

. (2009). B cell epitope prediction. Struct Bioinformatics, 849–879.

27.

Saha

, and Raghava

GPS

. (2006). Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins, 70, 311–319.

28.

Saravanan

, and Lakshmi

. (2013). SCLAP: An adaptive boosting method for predicting subchloroplast localization of plant proteins. Omics, 17, 106–115.

29.

Saravanan

, and Lakshmi

. (2014). Fuzzy logic for personalized healthcare and diagnostics: FuzzyApp—A fuzzy logic based allergen-protein predictor. Omics, 18, 570–581.

30.

Schlessinger

, Ofran

, Yachdav

, and Rost

. (2006). Epitome: Database of structure-inferred antigenic epitopes. Nucleic Acids Res, 34, D777–780.

31.

Schölkopf

, Tsuda

, and Vert

J-P

. (2004). Kernel Methods in Computational Biology. MIT Press.

32.

Singh

, Ansari

, and Raghava

GPS

. (2013). Improved method for linear B-cell epitope prediction using antigen's primary sequence. PLoS ONE, 8, 1–8.

33.

Stambuk

, and Konjevoda

. (2011). The role of independent test set in modeling of protein folding kinetics. Adv Exp Med Biol, 696, 279–284.

34.

Van Regenmortel

. (2006). Immunoinformatics may lead to a reappraisal of the nature of B cell epitopes and of the feasibility of synthetic peptide vaccines. J Mol Recognit, 19, 183–187.

35.

Van Regenmortel

. (2009). What is a B-cell epitope? In: Epitope Mapping Protocols. Springer, New York. pp. 3–20.

36.

Vijayakumar

, and Lakshmi

. (2014). ACPP: A web server for prediction and design of anti-cancer peptides. Int J Pept Res Ther, 21, 99–106.

37.

Vita

, Overton

, Greenbaum

, et al. (2015). The immune epitope database (IEDB) 3.0. Nucleic Acids Res, 43, D405–412.

38.

Vita

, Zarebski

, Greenbaum

, et al. (2010). The immune epitope database 2.0. Nucleic Acids Res, 38, D854–862.

39.

Yadav

, Liebau

, Haldar

, and Rathaur

. (2011). Identification of major antigenic peptide of filarial glutathione-S-transferase. Vaccine, 29, 1297–1303.

40.

Yao

, Zhang

, Liang

, and Zhang

. (2012). SVMTriP: A method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity. PLoS ONE, 7, 5–9.

41.

Zhang

, and Gu

. (2008). Support vector machines versus Boosting. Electrical Engineering UC, Berkeley.

42.

Zhang

, Zhao

, Sun

, Gao

, and Ma

. (2014). Conformational B-cell epitopes prediction from sequences using cost-sensitive ensemble classifiers and spatial clustering. Biomed Res Int, 2014, 12.

43.

Zhang

, and Xie

. (2010). Research on adaboost. m1 with random forest. In: Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, 2010. IEEE, V1-647-V641-652.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.41 MB

0.00 MB

Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor

Abstract

Abstract

Get full access to this article

References

Supplementary Material