Reduced bio basis function neural network for identification of protein phosphorylation sites: comparison with pattern recognition algorithms
Reduced bio basis function neural network for identification of protein phosphorylation sites: comparison with pattern recognition algorithms
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DOI：
10.1016/j.compbiolchem.2003.11.005
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发表时间：
2004-02-01
影响因子：
3.1
通讯作者：
Yang, ZR
中科院分区：
文献类型：
--
作者：
Berry, EA;Dalby, AR;Yang, ZR
蛋白质磷酸化是由一组称为蛋白激酶或磷酸转移酶（酶委员会分类2.7）的酶进行的翻译后修饰。它对蛋白质和细胞的正确功能至关重要，涉及酶控制，细胞信号传导和凋亡。试图预测这些位点时的主要问题是酶的广泛底物特异性。本研究采用反向传播神经网络（BPNNs），决策树算法C4.5和减少生物基函数神经网络（rBBFNN）预测磷酸化位点。目的是比较三种算法对该问题的预测效率，并检验知识提取能力。这三种算法对磷酸化位点的预测都是有效的。结果表明，rBBFNN是最快和最敏感的算法。BPNN具有最高的ROC曲线下面积，因此是最稳健的，C4.5具有最高的预测精度。C4.5还揭示了磷酸化位点上游的氨基酸2残基对于丝氨酸/苏氨酸磷酸化是重要的，而上游的氨基酸3残基对于酪氨酸磷酸化是重要的。(C)2003 Elsevier Ltd.保留所有权利。
Protein phosphorylation is a post-translational modification performed by a group of enzymes known as the protein kinases or phosphotransferases (Enzyme Commission classification 2.7). It is essential to the correct functioning of both proteins and cells, being involved with enzyme control, cell signalling and apoptosis. The major problem when attempting prediction of these sites is the broad substrate specificity of the enzymes. This study employs back-propagation neural networks (BPNNs), the decision tree algorithm C4.5 and the reduced bio-basis function neural network (rBBFNN) to predict phosphorylation sites. The aim is to compare prediction efficiency of the three algorithms for this problem, and examine knowledge extraction capability. All three algorithms are effective for phosphorylation site prediction. Results indicate that rBBFNN is the fastest and most sensitive of the algorithms. BPNN has the highest area under the ROC curve and is therefore the most robust, and C4.5 has the highest prediction accuracy. C4.5 also reveals the amino acid 2 residues upstream from the phosporylation site is important for serine/threonine phosphorylation, whilst the amino acid 3 residues upstream is important for tyrosine phosphorylation. (C) 2003 Elsevier Ltd. All rights reserved.