Tang Yu-Rong, Chen Yong-Zi, Canchaya Carlos A, Zhang Ziding
Bioinformatics Center, College of Biological Sciences, China Agricultural University, Beijing 100094, China.
Protein Eng Des Sel. 2007 Aug;20(8):405-12. doi: 10.1093/protein/gzm035. Epub 2007 Jul 24.
With the advance of modern molecular biology it has become increasingly clear that few cellular processes are unaffected by protein phosphorylation. Therefore, computational identification of phosphorylation sites is very helpful to accelerate the functional understanding of huge available protein sequences obtained from genomic and proteomic studies. Using a genetic algorithm integrated neural network (GANN), a new bioinformatics method named GANNPhos has been developed to predict phosphorylation sites in proteins. Aided by a genetic algorithm to optimize the weight values within the network, GANNPhos has demonstrated a high accuracy of 81.1, 76.7 and 73.3% in predicting phosphorylated S, T and Y sites, respectively. When benchmarked against Back-Propagation neural network and Support Vector Machine algorithms, GANNPhos gives better performance, suggesting the GANN program can be used for other prediction tasks in the field of protein bioinformatics.
随着现代分子生物学的发展，越来越清楚的是，很少有细胞过程不受蛋白质磷酸化的影响。因此，磷酸化位点的计算识别对于加速从基因组和蛋白质组研究中获得的大量可用蛋白质序列的功能理解非常有帮助。利用遗传算法集成神经网络（GANN），开发了一种名为GANNPhos的新生物信息学方法来预测蛋白质中的磷酸化位点。在遗传算法的辅助下优化网络内的权重值，GANNPhos在预测磷酸化的S、T和Y位点时分别表现出81.1%、76.7%和73.3%的高精度。与反向传播神经网络和支持向量机算法进行基准测试时，GANNPhos表现出更好的性能，这表明GANN程序可用于蛋白质生物信息学领域的其他预测任务。
