SEPPA 2.0—more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen

Qi, Tao; Qiu, Tianyi; Zhang, Qingchen; Tang, Kailin; Fan, Yangyang; Qiu, Jingxuan; Wu, Dingfeng; Zhang, Wei; Chen, Yanan; Gao, Jun; Zhu, Ruixin; Cao, Zhiwei

doi:10.1093/nar/gku395

Cited by 50 publications

(53 citation statements)

References 18 publications

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“…We selected four conformational and four linear epitope predictors as our base learners. The conformational predictors were DiscoTope 2.0 [9], ElliPro [10], SEPPA 2.0 [11], and Bpredictor [18], and the linear epitope predictors were BepiPred [5], ABCpred [6], AAP [7], and BCPREDS [8]. We calculated the Pearson’s correlation coefficients for the prediction scores produced by the base prediction tools.…”

Section: Resultsmentioning

confidence: 99%

“…We conducted the experiments on several representative epitope predictors released in 2008–2014: SEPPA 2.0 (2014), DiscoTope 2.0 (2012), Bpredictor (2011), CBTOPE (2010), and ElliPro (2008). Each of them had been trained and tested by different data sets [9–11,18,24]. In each experiment, we selected one epitope predictor for comparison.…”

Section: Resultsmentioning

confidence: 99%

“…ElliPro uses Thornton’s propensities and applies residue clustering to identify epitopes [10]. SEPPA 2.0 predicts conformational epitopes based on the unit patches of residue triangles, and the clustering coefficient for describing local spatial context and compactness with two new parameters appended, ASA (Accessible Surface Area) propensity, and consolidated amino acid index [11]. EPITOPIA combines structural and physiochemical features, and adopts a Bayesian classifier to predict epitopes [12].…”

Section: Introductionmentioning

confidence: 99%

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A meta-learning approach for B-cell conformational epitope prediction

Lin

et al. 2014

BMC Bioinformatics

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A meta-learning approach for B-cell conformational epitope prediction

Lin

et al. 2014

BMC Bioinformatics

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“…Three web servers SEPPA [52], Epitopia [49], and EPCES [53] were published in 2009. SEPPA employs two concepts, "unit patch of residue triangle" and "clustering coefficient" to describe the local spatial context and spatial compactness, and combines them to predict epitopes.…”

Section: B-cell Epitopesmentioning

confidence: 99%

An Overview of Bioinformatics Tools and Resources in Allergy

Lin

2017

Methods in Molecular Biology

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The rapidly increasing number of characterized allergens has created huge demands for advanced information storage, retrieval, and analysis. Bioinformatics and machine learning approaches provide useful tools for the study of allergens and epitopes prediction, which greatly complement traditional laboratory techniques. The specific applications mainly include identification of B- and T-cell epitopes, and assessment of allergenicity and cross-reactivity. In order to facilitate the work of clinical and basic researchers who are not familiar with bioinformatics, we review in this chapter the most important databases, bioinformatic tools, and methods with relevance to the study of allergens.

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“…Number of methods that utilize 3D structure of antigens for discontinuous epitope prediction have also been developed. These methods use different approaches for prediction such as solvent accessibility of surface residues [123,124], solvent accessibility with propensity scores [125], and propensity scores with packing density of amino acids [126]. An account of major tools/servers that are involved in conformational epitope prediction is provided in Studies have shown that the analysis of antigen-antibody complex structures is very useful for the characterization of conformational epitopes [134].…”

Section: Computational Prediction Of Allergen Epitopesmentioning

confidence: 99%

Databases and Algorithms in Allergen Informatics

Kadam¹,

Sawant²,

Jayaraman³

et al. 2016

Bioinformatics - Updated Features and Applications

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Allergic diseases are considered as one of the major health problems worldwide due to their increasing prevalence. Advancements in genomic, proteomic, and analytical techniques have resulted in considerable progress in the field of allergology, which has led to accumulation of huge amount of data. Allergen bioinformatics comprises allergenrelated data resources and computational methods/tools, which deal with an efficient archival, management, and analysis of allergological data. Significant work has been done in the area of allergen bioinformatics that has proven pivotal for the development and progress of this field. In this chapter, we describe the current status of databases and algorithms, encompassing the field of allergen bioinformatics by examining work carried out thus far with respect to features such as allergens and allergenicity, allergen databases, algorithms/tools for allergen/allergenicity prediction, allergen epitope prediction, and allergenic cross-reactivity assessment. This chapter illustrates concepts and algorithms in allergen bioinformatics, as well as it outlines the key areas for potential development in allergology field.

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SEPPA 2.0—more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen

Cited by 50 publications

References 18 publications

A meta-learning approach for B-cell conformational epitope prediction

A meta-learning approach for B-cell conformational epitope prediction

An Overview of Bioinformatics Tools and Resources in Allergy

Databases and Algorithms in Allergen Informatics

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