Reliable prediction of T-cell epitopes using neural networks with novel sequence representations
Reliable prediction of T-cell epitopes using neural networks with novel sequence representations
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DOI：
10.1110/ps.0239403
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发表时间：
2003-05-01
期刊：
影响因子：
8
通讯作者：
Lund, O
中科院分区：
文献类型：
--
作者：
Nielsen, M;Lundegaard, C;Lund, O
在本文中，我们描述了一种改进的神经网络方法来预测 T 细胞 I 类表位。一种新颖的输入表示形式已经被开发出来，它由稀疏编码、Blosum 编码和从隐马尔可夫模型导出的输入的组合组成。我们证明，使用不同序列编码方案派生的多个神经网络的组合具有优于使用单一序列编码方案派生的神经网络的性能。事实证明，新方法的性能明显高于其他方法。通过使用互信息计算，我们表明与 HLA A*0204 复合物结合的肽显示出更高阶序列相关性的信号。神经网络非常适合在预测结合亲和力时整合此类高阶相关性。正是这一特征与使用源自不同且新颖的序列编码方案的多个神经网络以及神经网络在由连续结合亲和力组成的数据上进行训练的能力相结合，使得新方法具有改进的性能。发现神经网络方法和矩阵驱动方法之间的预测性能差异对于与 HLA 分子强烈结合的肽最为显着，这证实了高阶序列相关性的信号在高结合肽中最为强烈。最后，我们使用该方法预测丙型肝炎病毒基因组的T细胞表位，并讨论该预测方法在指导合理疫苗设计过程中的可能应用。
In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.