Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.
Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.
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DOI：
10.3390/ijms18020312
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发表时间：
2017-02-01
影响因子：
5.6
通讯作者：
Woelk CH
中科院分区：
文献类型：
--
作者：
Heinson AI;Gunawardana Y;Moesker B;Hume CC;Vataga E;Hall Y;Stylianou E;McShane H;Williams A;Niranjan M;Woelk CH
反向疫苗学（RV）是一种生物信息学方法，可以从细菌病原体的蛋白编码基因组中预测具有保护潜力的抗原，用于亚单位疫苗的设计。随着针对脑膜炎奈瑟菌血清群B的BEXSERO®疫苗的开发，RV已得到牢固确立。RV研究已经开始采用机器学习（ML）技术来区分细菌保护性抗原（BPA）和非BPA。这项研究通过使用排列分析来证明保护性抗原的信号可以从已发表的数据中策划，从而对RV领域做出了重大贡献。此外，还评估了以下对RV的ML方法的影响：嵌套交叉验证，平衡亚细胞定位的非BPA选择，增加训练数据，并结合更多的蛋白质注释工具用于特征生成。这些增强产生了支持向量机（SVM）分类器，其可以区分BPA（n = 200）与非BPA（n = 200），曲线下面积（AUC）为0.787。此外，层次聚类的BPA显示，细胞内的BPA聚类分开从细胞外的BPA。然而，没有直接的好处时，得到训练SVM分类器的数据集只包含内部或细胞外的BPA。总之，这项工作表明，ML分类器在RV方法中具有很大的实用性，并将在未来产生新的亚单位疫苗。
Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.