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首页> 外文期刊>BMC Bioinformatics >SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence
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SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence

机译：SEPIa，一种知识驱动算法，可从氨基酸序列预测构象B细胞表位

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Background The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale. Results We developed SEPIa, a B-cell epitope predictor from the protein sequence, which is sufficiently fast to be applicable on a large scale. The originality of SEPIa lies in the combination of two classifiers, a na?ve Bayesian and a random forest classifier, through a voting algorithm that exploits the advantages of both. It is based on 13 sequence-based features, whose values in a 9-residue sequence window are compiled to predict the epitopeon-epitope state of the central residue. The features are related to the type of amino acid, its conservation in homologous proteins, and its tendency of being exposed to the solvent, soluble, flexible, and disordered. The highest signal is obtained from statistical amino acid preferences, but all 13 features contribute non-negligibly in the predictor. SEPIa’s average prediction accuracy is limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.65 both in 10-fold cross-validation and on an independent test set. It is nevertheless slightly higher than that of other methods evaluated on the same test set. Conclusions SEPIa was applied to a test protein whose epitopes are known, human β2 adrenergic G-protein-coupled receptor, with promising results. Although the actual AUC score is rather low, many of the predicted epitopes cluster together and overlap the experimental epitope region. The reasons underlying the limitations of SEPIa and of all other B-cell epitope predictors are discussed.
机译：背景技术鉴定能够被抗体识别并触发免疫应答的抗原表面上的免疫原性区域，是设计新的有效疫苗的主要挑战。通过计算免疫学技术对这些区域进行预测是一个具有挑战性的目标，最终将导致对验证其效率所需的实验测试的严格限制。但是，当前的方法远未足够可靠和/或可大规模应用。结果我们开发了SEPIa，它是一种从蛋白质序列预测B细胞表位的方法，其速度足够快，可以大规模应用。 SEPIa的独创性在于通过利用两者优点的投票算法，将两个分类器（朴素贝叶斯分类器）和随机森林分类器结合在一起。它基于13个基于序列的特征，将其在9个残基的序列窗口中的值进行编译，以预测中央残基的表位/非表位状态。这些特征与氨基酸的类型，其在同源蛋白质中的保守性及其与溶剂，可溶，柔性和无序暴露的趋势有关。最高信号来自统计氨基酸偏爱，但所有13个特征在预测变量中的贡献均不可忽略。 SEPIa的平均预测准确性受到限制，在10倍交叉验证和独立测试中，AUC得分（接收器工作特性曲线下的面积）均达到0.65。但是，它比在同一测试集上评估的其他方法的方法略高。结论SEPIa已应用于表位已知为人β2肾上腺素G蛋白偶联受体的测试蛋白，并获得了可喜的结果。尽管实际的AUC评分较低，但是许多预测的表位聚集在一起并与实验性表位区域重叠。讨论了SEPIa和所有其他B细胞表位预测因子局限性的原因。

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