Abstract

BackgroundOne of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.ResultsWe developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor.ConclusionsComputational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0378-y) contains supplementary material, which is available to authorized users.

Highlights

  • One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses

  • To further analyze the correlations among predictions based on the score rankings, we sorted the prediction scores of all protein residues provided by each base learner and conducted a Spearman’s rank correlation analysis

  • The results indicated that support vector machine (SVM) was the bestperforming meta learner when compared with C4.5, k-NN, and artificial neural networks (ANN)

Read more

Summary

Introduction

One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. The ability of an antibody to respond to an antigen, such as a virus capsid protein fragment, depends on the antibody’s specific recognition of an epitope, which is the antigenic site to which an antibody binds Based on their structure and interaction with antibodies, epitopes can Several different approaches exist for predicting linear and conformational epitopes. Previous studies relied on the varying physicochemical properties of amino acids to predict linear epitopes [1,2,3]. A study on 484 amino acid scales revealed that predictions based on the bestperforming scales poorly correlated with experimentally confirmed epitopes [4]. This result prompted the development of machine-learning methods to improve prediction. BCPREDS uses SVM combined with a variety of kernel methods, including string kernels, radial basis kernels, and subsequence kernels, to predict linear B-cell epitopes [8]

Objectives
Methods
Results
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.