Length-dependent prediction of protein intrinsic disorder.
Length-dependent prediction of protein intrinsic disorder.
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DOI：
10.1186/1471-2105-7-208
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发表时间：
2006-04-17
影响因子：
3
通讯作者：
Obradovic, Zoran
中科院分区：
文献类型：
--
作者：
Peng, Kang;Radivojac, Predrag;Vucetic, Slobodan;Dunker, A. Keith;Obradovic, Zoran
由于内在无序蛋白质或蛋白质区域的功能重要性，从氨基酸序列预测内在无序蛋白质已成为一个活跃的研究领域，正如第6次蛋白质结构预测技术关键评估实验（CASP6）所见证的那样。自从Romero等人（从氨基酸序列中识别蛋白质中的无序区域）的最初工作以来，IEEE Int。神经网络；， 1997)，我们的小组已经开发了几个预测优化的长无序区域（bbb30残基），预测精度超过85%。然而，这些预测在短无序区域（≤30个残基）上不太成功。一个可能的原因是长度依赖的氨基酸组成和无序区域的序列特性。我们提出了两个新的预测模型，VSL2-M1和VSL2-M2，以解决这一长度依赖问题，在预测内在的蛋白质紊乱。这两个预测因子与CASP6实验中使用的原始VSL1预测因子相似。在这两个模型中，首先分别针对短（≤30个残基）和长无序区（bbb30个残基）构建和优化了两个专门的预测因子。然后训练一个元预测器，将专门的预测器整合到最终的预测器模型中。10倍交叉验证结果显示，VSL2预测因子在短、长无序区均达到了81%的平衡预测精度。通过10倍交叉验证和一组不相关的近期PDB链的盲测，对VSL2训练数据集的比较表明，VSL2预测因子比几种现有的内在蛋白质紊乱预测因子要准确得多。VSL2预测器适用于任意长度的无序区域，能够准确识别出我们以前的无序预测器经常错误分类的短无序区域。VSL2预测因子的成功进一步证实了之前观察到的氨基酸组成和序列特性在短和长无序区之间的差异，并证明了我们分别建模短和长无序区的方法是正确的。VSL2预测器可免费用于非商业用途
Due to the functional importance of intrinsically disordered proteins or protein regions, prediction of intrinsic protein disorder from amino acid sequence has become an area of active research as witnessed in the 6th experiment on Critical Assessment of Techniques for Protein Structure Prediction (CASP6). Since the initial work by Romero et al. (Identifying disordered regions in proteins from amino acid sequences, IEEE Int. Conf. Neural Netw., 1997), our group has developed several predictors optimized for long disordered regions (>30 residues) with prediction accuracy exceeding 85%. However, these predictors are less successful on short disordered regions (≤30 residues). A probable cause is a length-dependent amino acid compositions and sequence properties of disordered regions. We proposed two new predictor models, VSL2-M1 and VSL2-M2, to address this length-dependency problem in prediction of intrinsic protein disorder. These two predictors are similar to the original VSL1 predictor used in the CASP6 experiment. In both models, two specialized predictors were first built and optimized for short (≤30 residues) and long disordered regions (>30 residues), respectively. A meta predictor was then trained to integrate the specialized predictors into the final predictor model. As the 10-fold cross-validation results showed, the VSL2 predictors achieved well-balanced prediction accuracies of 81% on both short and long disordered regions. Comparisons over the VSL2 training dataset via 10-fold cross-validation and a blind-test set of unrelated recent PDB chains indicated that VSL2 predictors were significantly more accurate than several existing predictors of intrinsic protein disorder. The VSL2 predictors are applicable to disordered regions of any length and can accurately identify the short disordered regions that are often misclassified by our previous disorder predictors. The success of the VSL2 predictors further confirmed the previously observed differences in amino acid compositions and sequence properties between short and long disordered regions, and justified our approaches for modelling short and long disordered regions separately. The VSL2 predictors are freely accessible for non-commercial use at
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影响因子：
5.6
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