An improved prediction of catalytic residues in enzyme structures
An improved prediction of catalytic residues in enzyme structures
复制标题
酶结构中催化残基的改进预测
DOI：
10.1093/protein/gzn003
复制
发表时间：
2008-05-01
影响因子：
2.4
通讯作者：
Zhang, Ziding
中科院分区：
文献类型：
--
作者：
Tang, Yu-Rong;Sheng, Zhi-Ya;Zhang, Ziding
蛋白质数据库包含大量功能未知的蛋白质，包括许多具有结构基因组学项目新确定的3D结构的蛋白质。为了加速基于实验的功能分配，蛋白质功能位点（如酶的活性位点）的从头预测变得越来越重要。在这里，我们试图通过寻找和改进不同的编码（即残基性质）以及采用新的机器学习算法来改进酶结构中催化残基的预测。特别是，考虑到催化残基在将酶结构表示为残基接触网络时通常可以显示特定的网络中心性，因此在我们的新预测器中，相应的测量（即接近中心性）被用作最重要的编码之一。同时，还采用了遗传算法集成神经网络（GANN）。由于上述策略，我们的GANN预测器在基于平衡数据集（即催化残基与非催化残基1:1的比例）的催化残基预测中显示出91.2%的高精度。当GANN方法最优地应用于实际酶结构时，73.9%的测试结构正确定位了活性位点。与已有的两种方法相比，本文提出的GANN方法也表现出了更好的性能。
The protein databases contain a huge number of function unknown proteins, including many proteins with newly determined 3D structures resulted from the Structural Genomics Projects. To accelerate experiment-based assignment of function, de novo prediction of protein functional sites, like active sites in enzymes, becomes increasingly important. Here, we attempted to improve the prediction of catalytic residues in enzyme structures by seeking and refining different encodings (i.e. residue properties) as well as employing new machine learning algorithms. In particular, considering that catalytic residues can often reveal specific network centrality when representing enzyme structure as a residue contact network, the corresponding measurement (i.e. closeness centrality) was used as one of the most important encodings in our new predictor. Meanwhile, a genetic algorithm integrated neural network (GANN) was also employed. Thanks to the above strategies, our GANN predictor demonstrated a high accuracy of 91.2% in the prediction of catalytic residues based on balanced datasets (i.e. the 1: 1 ratio of catalytic to non-catalytic residues). When the GANN method was optimally applied to real enzyme structures, 73.9% of the tested structures had the active site correctly located. Compared with two existing methods, the proposed GANN method also demonstrated a better performance.