Linear B-cell epitopes are critically important for immunological applications,such as vaccine design,immunodiagnostic test,and antibody production,as well as disease diagnosis and therapy.The accurate identification ...Linear B-cell epitopes are critically important for immunological applications,such as vaccine design,immunodiagnostic test,and antibody production,as well as disease diagnosis and therapy.The accurate identification of linear B-cell epitopes remains challenging despite several decades of research.In this work,we have developed a novel predictor,Identification of Linear B-cell Epitope(i LBE),by integrating evolutionary and sequence-based features.The successive feature vectors were optimized by a Wilcoxon-rank sum test.Then the random forest(RF)algorithm using the optimal consecutive feature vectors was applied to predict linear B-cell epitopes.We combined the RF scores by the logistic regression to enhance the prediction accuracy.iLBE yielded an area under curve score of 0.809 on the training dataset and outperformed other prediction models on a comprehensive independent dataset.iLBE is a powerful computational tool to identify the linear B-cell epitopes and would help to develop penetrating diagnostic tests.A web application with curated datasets for iLBE is freely accessible at http://kurata14.bio.kyutech.ac.jp/iLBE/.展开更多
基金supported by the Grant-in-Aid for Challenging Exploratory Research with Japan Society of Promotion of Science(Grant No.17K20009)partially supported by the Ministry of Economy,Trade and Industry,Japan(METI)the Japan Agency for Medical Research and Development(AMED)。
文摘Linear B-cell epitopes are critically important for immunological applications,such as vaccine design,immunodiagnostic test,and antibody production,as well as disease diagnosis and therapy.The accurate identification of linear B-cell epitopes remains challenging despite several decades of research.In this work,we have developed a novel predictor,Identification of Linear B-cell Epitope(i LBE),by integrating evolutionary and sequence-based features.The successive feature vectors were optimized by a Wilcoxon-rank sum test.Then the random forest(RF)algorithm using the optimal consecutive feature vectors was applied to predict linear B-cell epitopes.We combined the RF scores by the logistic regression to enhance the prediction accuracy.iLBE yielded an area under curve score of 0.809 on the training dataset and outperformed other prediction models on a comprehensive independent dataset.iLBE is a powerful computational tool to identify the linear B-cell epitopes and would help to develop penetrating diagnostic tests.A web application with curated datasets for iLBE is freely accessible at http://kurata14.bio.kyutech.ac.jp/iLBE/.
