Saha Sudipto, Raghava G P S
Institute of Microbial Technology, Chandigarh, India.
Proteins. 2006 Oct 1;65(1):40-8. doi: 10.1002/prot.21078.
B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/.
B细胞表位在肽疫苗的研发、疾病诊断以及过敏研究中都发挥着至关重要的作用。用于表征表位的实验方法耗时且需要大量资源。表位预测方法的可用性能够迅速帮助实验人员简化这一问题。本研究使用标准前馈神经网络（FNN）和递归神经网络（RNN）来预测抗原序列中的B细胞表位。这些网络在一个纯净数据集上进行训练和测试，该数据集由从Bcipep数据库获得的700个非冗余B细胞表位以及从Swiss-Prot数据库随机获取的相同数量的非表位组成。这些网络在不同的输入窗口长度和隐藏单元数量下进行训练和测试。对于窗口长度为16且具有35个隐藏单元的单个隐藏层的递归神经网络（Jordan网络），获得了最高准确率。通过五重交叉验证测试时，最终网络的总体预测准确率为65.93%。相应的灵敏度、特异性和阳性预测值分别为67.14%、64.71%和65.61%。据观察，在预测B细胞表位方面，RNN（JE）比FNN更成功。肽的长度在从抗原序列预测B细胞表位时也很重要。网络服务器ABCpred可在www.imtech.res.in/raghava/abcpred/上免费获取。
