Predicting linear B-cell epitopes using string kernels.
Predicting linear B-cell epitopes using string kernels.
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DOI：
10.1002/jmr.893
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发表时间：
2008-07
影响因子：
2.7
通讯作者：
Honavar, Vasant
中科院分区：
文献类型：
--
作者：
El-Manzalawy, Yasser;Dobbs, Drena;Honavar, Vasant
B细胞表位的鉴定和表征在疫苗设计、免疫诊断试验和抗体生产中起着重要作用。因此，非常需要用于可靠地预测线性B-细胞表位的计算工具。我们评估了利用五种不同的核方法训练的支持向量机（SVM）分类器，使用五重交叉验证对从Bcipep数据库中提取的701个线性B细胞表位和从SwissProt序列中随机提取的701个非表位的同源性简化数据集进行了验证。基于我们的计算实验的结果，我们提出了BCPred，一种使用子序列核预测线性B细胞表位的新方法。我们表明，BCPred（AUC = 0.758）的预测性能优于我们的实验中开发和评估的11个基于SVM的分类器以及我们的AAP（AUC = 0.7）实现，AAP是最近提出的一种使用氨基酸对抗原性预测线性B细胞表位的方法。此外，我们将BCPred与AAP和ABCPred进行了比较，这是一种使用递归神经网络的方法，使用了先前用于评估ABCPred的独特B细胞表位的两个数据集。对所用数据集和该比较结果的分析表明，基于使用独特B细胞表位数据集的实验得出的关于不同B细胞表位预测方法的相对性能的结论可能会对评价方法的性能产生过于乐观的估计。这就要求在比较B细胞表位预测方法时使用仔细同源性简化的数据集，以避免关于不同方法如何相互比较的误导性结论。我们的同源性简化数据集和BCPred的实现以及APP方法可通过我们的基于Web的服务器BCPREDS公开获得：http://ailab.cs.iastate.edu/bcpreds/。版权所有© 2008约翰威利父子有限公司。
The identification and characterization of B‐cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting linear B‐cell epitopes are highly desirable. We evaluated Support Vector Machine (SVM) classifiers trained utilizing five different kernel methods using fivefold cross‐validation on a homology‐reduced data set of 701 linear B‐cell epitopes, extracted from Bcipep database, and 701 non‐epitopes, randomly extracted from SwissProt sequences. Based on the results of our computational experiments, we propose BCPred, a novel method for predicting linear B‐cell epitopes using the subsequence kernel. We show that the predictive performance of BCPred (AUC = 0.758) outperforms 11 SVM‐based classifiers developed and evaluated in our experiments as well as our implementation of AAP (AUC = 0.7), a recently proposed method for predicting linear B‐cell epitopes using amino acid pair antigenicity. Furthermore, we compared BCPred with AAP and ABCPred, a method that uses recurrent neural networks, using two data sets of unique B‐cell epitopes that had been previously used to evaluate ABCPred. Analysis of the data sets used and the results of this comparison show that conclusions about the relative performance of different B‐cell epitope prediction methods drawn on the basis of experiments using data sets of unique B‐cell epitopes are likely to yield overly optimistic estimates of performance of evaluated methods. This argues for the use of carefully homology‐reduced data sets in comparing B‐cell epitope prediction methods to avoid misleading conclusions about how different methods compare to each other. Our homology‐reduced data set and implementations of BCPred as well as the APP method are publicly available through our web‐based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/. Copyright © 2008 John Wiley & Sons, Ltd.
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影响因子：
64.8
作者：
Li W;Moore MJ;Vasilieva N;Sui J;Wong SK;Berne MA;Somasundaran M;Sullivan JL;Luzuriaga K;Greenough TC;Choe H;Farzan M
通讯作者：
Farzan M
影响因子：
64.5
作者：
Dimitrov DS
通讯作者：
Dimitrov DS
影响因子：
5.5
作者：
Alix, AJP
通讯作者：
Alix, AJP
影响因子：
14.9
作者：
Bairoch, A;Apweiler, R
通讯作者：
Apweiler, R
影响因子：
--
作者：
Friedman, M
通讯作者：
Friedman, M