Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information

EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

doi:10.1109/bibm.2008.80

Cited by 14 publications

(16 citation statements)

References 34 publications

(45 reference statements)

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“…These findings also corroborate previous studies on the use of feature selection methods to improve prediction performance [15-17]. Interestingly, feature extraction using the Bayes Feature Extraction approach as implemented here is intuitively similar to the application of position-specific scoring matrix (PSSM) profiles for predicting protective linear B-cell epitopes by EL-Manzalawy et al [21]. In that study, it was shown that the Naive Bayes classifier trained using PSSM profiles significantly outperformed the propensity scale-based methods and simple binary encoding with SVM in predicting protective linear B-cell epitopes.…”

Section: Resultssupporting

confidence: 87%

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

et al. 2010

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BackgoundThe identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naïve modeling methods difficult to apply.ResultsWe analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and AROC of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes.ConclusionWe developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://www.immunopred.org/bayesb/index.html.

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Section: Resultssupporting

confidence: 87%

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

et al. 2010

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“…This suggests that discriminating protective antigens from non-antigens may be much easier than discriminating antigens from non-antigens. A similar observation has been reported in [57], where we showed that classifiers trained to predict protective linear B-cell epitopes have better predictive performance than classifiers trained to predict linear Bcell epitopes. We conjecture that protective antigens can be discriminated from antigens on the basis of some sequence features.…”

Section: Resultssupporting

confidence: 67%

Predicting protective bacterial antigens using random forest classifiers

EL-Manzalawy

Dobbs

Honavar

2012

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine

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Identifying protective antigens from bacterial pathogens is important for developing vaccines. Most computational methods for predicting protein antigenicity rely on sequence similarity between a query protein sequence and at least one known antigen. Such methods limit our ability to predict novel antigens (i.e., antigens that are not homologous to any known antigen). Therefore, there is an urgent need for alignment-free computational methods for reliable prediction of protective antigens.We evaluated the discriminative power of four different amino acid composition derived feature representations using three classification methods (Logistic Regression, Support Vector Machine, and Random Forest) on a cross validation data set of 193 protective bacterial antigens and 193 non-antigenic bacterial proteins. Our results show that, with all four data representations, Random Forest classifiers consistently outperform other classifiers. We compared HRF50, one of the best performing Random Forest classifiers with VaxiJen and SignalP on independent test sets derived from the Chlamydia trachomatis and Bartonella proteomes. Our results show that our HRF50 predictor outperforms VaxiJen and is competitive with SignalP and ANTIGENpro in predicting protective antigens. We further showed that when we combine SignalP with HRF50, the resulting method, which we call BacGen, yields performance that is comparable to or better than that of ANTIGENpro in predicting antigens in bacterial sequences. We conclude that amino acid sequence composition derived features can be effectively used to design alignment-free methods for predicting protein antigenicity using Random Forest classifiers. BacGen is available as an online server at

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“…The existing methods which rely on propensity scale approach are Parker et al [5], Karp lus et al [7], Emin i et al [8], PREDITOP [9], PEOPLE [10], BEPITOPE [11] and BcePred [12]; wh ile the recent existing methods which rely on machine learn ing approach are BepiPred [13], A BCPred [14], Söllner and Mayer [15], Chen et al [16], Söllner et al [17], BCPred [18], FBCPred [19], El-Man zalawy et al [20] and COBEpro [21].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Antigenic Epitope Patches on Protein Surface Using Antigen Structure Information and Support Vector Machine

Hassan¹,

Badr²,

Abdel-Azim³

2012

BIOINFORMATICS

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Identification of antigen-antibody interacting sites is an important task for vaccine design, and hence reliable computer based prediction methods are highly desirable. The prediction performances of the current existing methods to predict the conformational B-cell ep itope residues are still not satisfying and remain far fro m ideal. This is a new approach in the area of vaccine develop ment to predict the antigenic surface patches that hold the majority number of epitope residues in the surface of the antigen protein structure. The proposed method is a support vector machine based model to predict the epitope patches in the antigen structures by combining the accessible surface area and B-factor structural features. The Predictions are made for the known structures of benchmark dataset after removing antigens sequence redundancy where no two antigen sequences have more than 40% sequence identity. The predictions are successful for 70% of the antigen structure chains of the benchmark dataset. We compared the predict ion performance of our model with a proteinprotein interaction predict ion server "Sharp2" using the same antigen structures of the benchmark dataset and observed that our model outperforms on Sharp2 by more than 40% accuracy. Th is paper demonstrates that the identification of the antigenic determinant sites in the protein surface using the antigen structural information outperforms the tradit ional protein-protein interaction algorith ms to predict the interacting sites in the antigen protein surface. It p rovides a new approach for the scientists to only use the predicted antigenic epitope surface patch from the target antigen structure in vaccine development rather than using the predicted epitope residues. A web server "PatchTope" has been developed for predicting antigenic epitope surface patches on an antigen protein structure surface and is available at

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Predicting Protective Linear B-Cell Epitopes Using Evolutionary Information

Abstract: Mapping B-cell epitopes plays an important role in vac-

Cited by 14 publications

References 34 publications

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

Predicting protective bacterial antigens using random forest classifiers

Prediction of Antigenic Epitope Patches on Protein Surface Using Antigen Structure Information and Support Vector Machine

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