Predicting Flexible Length Linear B-cell Epitopes

Overview

Journal Comput Syst Bioinformatics Conf

Specialty Biology

Date 2009 Aug 1

PMID 19642274

Citations 88

Authors

Yasser El-Manzalawy

Drena Dobbs

Vasant Honavar

Affiliations

Soon will be listed here.

Abstract

Identifying B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes. The first approach utilizes four sequence kernels for determining a similarity score between any arbitrary pair of variable length sequences. The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector. Based on our empirical comparisons, we propose FBCPred, a novel method for predicting flexible length linear B-cell epitopes using the subsequence kernel. Our results demonstrate that FBCPred significantly outperforms all other classifiers evaluated in this study. An implementation of FBCPred and the datasets used in this study are publicly available through our linear B-cell epitope prediction server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.

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References

Chinnasamy A, Sung W, Mittal A . Protein structure and fold prediction using tree-augmented naive Bayesian classifier. Pac Symp Biocomput. 2004; :387-98. DOI: 10.1142/9789812704856_0037. View

Saha S, Bhasin M, Raghava G . Bcipep: a database of B-cell epitopes. BMC Genomics. 2005; 6:79. PMC: 1173103. DOI: 10.1186/1471-2164-6-79. View

Flower D . Immunoinformatics. Predicting immunogenicity in silico. Preface. Methods Mol Biol. 2008; 409:v-vi. DOI: 10.1007/978-1-60327-118-9. View

Luo R, Feng Z, Liu J . Prediction of protein structural class by amino acid and polypeptide composition. Eur J Biochem. 2002; 269(17):4219-25. DOI: 10.1046/j.1432-1033.2002.03115.x. View

Zaki N, Deris S, Illias R . Application of string kernels in protein sequence classification. Appl Bioinformatics. 2005; 4(1):45-52. DOI: 10.2165/00822942-200504010-00005. View

Walter G . Production and use of antibodies against synthetic peptides. J Immunol Methods. 1986; 88(2):149-61. DOI: 10.1016/0022-1759(86)90001-3. View

Hua S, Sun Z . Support vector machine approach for protein subcellular localization prediction. Bioinformatics. 2001; 17(8):721-8. DOI: 10.1093/bioinformatics/17.8.721. View

Alix A . Predictive estimation of protein linear epitopes by using the program PEOPLE. Vaccine. 1999; 18(3-4):311-4. DOI: 10.1016/s0264-410x(99)00329-1. View

Cui J, Han L, Lin H, Tang Z, Jiang L, Cao Z . MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties. Immunogenetics. 2006; 58(8):607-13. DOI: 10.1007/s00251-006-0117-2. View

10.

Leslie C, Eskin E, Cohen A, Weston J, Noble W . Mismatch string kernels for discriminative protein classification. Bioinformatics. 2004; 20(4):467-76. DOI: 10.1093/bioinformatics/btg431. View

11.

Larsen J, Lund O, Nielsen M . Improved method for predicting linear B-cell epitopes. Immunome Res. 2006; 2:2. PMC: 1479323. DOI: 10.1186/1745-7580-2-2. View

12.

Leslie C, Eskin E, Noble W . The spectrum kernel: a string kernel for SVM protein classification. Pac Symp Biocomput. 2002; :564-75. View

13.

Saha S, Raghava G . Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins. 2006; 65(1):40-8. DOI: 10.1002/prot.21078. View

14.

Pellequer J, Westhof E, Van Regenmortel M . Predicting location of continuous epitopes in proteins from their primary structures. Methods Enzymol. 1991; 203:176-201. DOI: 10.1016/0076-6879(91)03010-e. View

15.

El-Manzalawy Y, Dobbs D, Honavar V . Predicting linear B-cell epitopes using string kernels. J Mol Recognit. 2008; 21(4):243-55. PMC: 2683948. DOI: 10.1002/jmr.893. View

16.

Barlow D, Edwards M, Thornton J . Continuous and discontinuous protein antigenic determinants. Nature. 1986; 322(6081):747-8. DOI: 10.1038/322747a0. View

17.

Salomon J, Flower D . Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores. BMC Bioinformatics. 2006; 7:501. PMC: 1664591. DOI: 10.1186/1471-2105-7-501. View

18.

Bairoch A, Apweiler R . The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 1999; 28(1):45-8. PMC: 102476. DOI: 10.1093/nar/28.1.45. View

19.

Langeveld J, Boshuizen R, Meloen R, Casal J . Characterisation of a protective linear B cell epitope against feline parvoviruses. Vaccine. 2001; 19(17-19):2352-60. DOI: 10.1016/s0264-410x(00)00526-0. View

20.

Blythe M, Flower D . Benchmarking B cell epitope prediction: underperformance of existing methods. Protein Sci. 2004; 14(1):246-8. PMC: 2253337. DOI: 10.1110/ps.041059505. View