天然未折叠蛋白质的组合二分预测因子的性能
On the performance of combined dichotomic predictors of natively unfolded proteins
摘要：
单折叠预测和组合评分的性能进行了严格的评估。我们测试平均包装，平均成对能量和新的指数gVSL 2的743折叠蛋白质和81天然未折叠蛋白质的数据集。这些预测器具有与其他提出的方法相当甚至更好的单独性能。我们在这里介绍一个严格一致的分数S_{SU}，结合它们，但留下未定的那些序列不同的两个单一的预测分类。单个预测因子在从S_{SU}留下的未分类蛋白质中清除的数据集上的性能显著增加，表明未分类蛋白质主要是错误预测。氨基酸组成是这些预测因子考虑的主要决定因素，因此未分类的蛋白质具有与折叠和未折叠状态相容的组成。这就是为什么从这些模糊的蛋白质中清除数据集可以提高单个预测器的性能。通过S_{SU}预测，在三个王国中天然未折叠的蛋白质百分比为：细菌为4.1%，细菌为1.0%，真核生物为20.0%;与先前的测定结果一致。有证据表明，天然未折叠蛋白质的数量与基因组中蛋白质的总数之间存在一个比例律，临界指数的第一次估计为1.95 +-0.21
英文摘要：
The performance of single folding predictors and combination scores is critically evaluated. We test mean packing, mean pairwise energy and the new index gVSL2 on a dataset of 743 folded proteins and 81 natively unfolded proteins. These predictors have an individual performance comparable or even better than other proposed methods. We introduce here a strictly unanimous score S_{SU} that combines them but leaves undecided those sequences differently classified by two single predictors. The performance of the single predictors on a dataset purged from the proteins left unclassified by S_{SU}, significantly increases, indicating that unclassified proteins are mainly false predictions. Amino acid composition is the main determinant considered by these predictors, therefore unclassified proteins have a composition compatible with both folded and unfolded status. This is why purging a dataset from these ambiguous proteins increases the performance of single predictors. The percentage of proteins predicted as natively unfolded by S_{SU} in the three kingdoms are: 4.1% for Bacteria, 1.0% for Archaea and 20.0% for Eukarya; compatible with previous determinations. Evidence is given of a scaling law relating the number of natively unfolded proteins with the total number of proteins in a genome; a first estimate of the critical exponent is 1.95 +- 0.21
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