How accurate and statistically robust are catalytic site predictions based on closeness centrality?
How accurate and statistically robust are catalytic site predictions based on closeness centrality?
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DOI：
10.1186/1471-2105-8-153
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发表时间：
2007-05-11
影响因子：
3
通讯作者：
Livesay DR
中科院分区：
文献类型：
--
作者：
Chea E;Livesay DR
我们根据蛋白质结构的网络表示检查酶催化残基预测的准确性。在此模型中，氨基酸 α-碳指定图中的顶点，边连接结构上邻近的顶点。紧密中心性在之前的调查中已显示出前景，用于识别网络中的重要位置。紧密度中心性是网络中心性的全局度量，计算为顶点 i 与所有其他顶点之间的平均距离的倒数。我们针对 Catalytic Site Atlas 中 283 个结构独特的蛋白质对该方法进行了基准测试。我们的结果与之前对较小数据集的调查一致，表明接近中心性预测具有统计显着性。然而，与以前的方法不同，我们特别关注得分最高的残基。在前五个紧密度中心性得分中，我们观察到平均真假阳性率为 6.8 比 1。如前所述，添加溶剂可及性过滤器可显着提高预测能力；平均比率增加到 15.3 比 1。我们还（首次）证明，通过残基身份过滤预测比可访问性过滤更能改善结果。在这里，我们简单地排除了那些不太可能符合催化要求的理化性质的残留物。残留身份过滤将平均真假阳性率比率提高到 26.3 比 1。将两个过滤器组合在一起对结果影响很小。三种预测方案的计算 p 值范围从 2.7E-9 到小于 8.8E-134。最后，检查了预测对结构选择和轻微扰动的敏感性。我们的结果坚决证实，紧密度中心性是一种可行的预测方案，其预测具有统计显着性。简单的过滤方案大大提高了该方法的预测能力。此外，在比较连接和未连接的结构时，没有观察到对性能的明显影响。同样，CC 预测结果对于分子动力学模拟中的轻微结构扰动也是稳健的。
We examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid α-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex i and all other vertices. We benchmark the approach against 283 structurally unique proteins within the Catalytic Site Atlas. Our results, which are inline with previous investigations of smaller datasets, indicate closeness centrality predictions are statistically significant. However, unlike previous approaches, we specifically focus on residues with the very best scores. Over the top five closeness centrality scores, we observe an average true to false positive rate ratio of 6.8 to 1. As demonstrated previously, adding a solvent accessibility filter significantly improves predictive power; the average ratio is increased to 15.3 to 1. We also demonstrate (for the first time) that filtering the predictions by residue identity improves the results even more than accessibility filtering. Here, we simply eliminate residues with physiochemical properties unlikely to be compatible with catalytic requirements from consideration. Residue identity filtering improves the average true to false positive rate ratio to 26.3 to 1. Combining the two filters together has little affect on the results. Calculated p-values for the three prediction schemes range from 2.7E-9 to less than 8.8E-134. Finally, the sensitivity of the predictions to structure choice and slight perturbations is examined. Our results resolutely confirm that closeness centrality is a viable prediction scheme whose predictions are statistically significant. Simple filtering schemes substantially improve the method's predicted power. Moreover, no clear effect on performance is observed when comparing ligated and unligated structures. Similarly, the CC prediction results are robust to slight structural perturbations from molecular dynamics simulation.
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影响因子：
5.6
作者：
Innis, CA;Anand, AP;Sowdhamini, R
通讯作者：
Sowdhamini, R
DOI：
10.1016/s1387-2656(03)09010-0
发表时间：
2003-01-01
期刊：
BIOTECHNOLOGY ANNUAL REVIEW, VOL 9
影响因子：
--
作者：
Neves-Petersen, MT;Petersen, SB
通讯作者：
Petersen, SB
影响因子：
5.6
作者：
Lichtarge, O;Bourne, HR;Cohen, FE
通讯作者：
Cohen, FE
影响因子：
5.6
作者：
Landgraf, R;Xenarios, I;Eisenberg, D
通讯作者：
Eisenberg, D
影响因子：
5.6
作者：
Elcock, AH
通讯作者：
Elcock, AH