Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.
Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.
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DOI：
10.1186/1471-2105-11-407
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发表时间：
2010-07-31
影响因子：
3
通讯作者：
Hirst JD
中科院分区：
文献类型：
--
作者：
Kountouris P;Hirst JD
β匝是二级结构元件，通常被归类为线圈。它们的预测是重要的，因为它们在蛋白质折叠中的作用和它们在蛋白质链中的频繁出现。我们开发了一种新的方法，利用来自多个序列比对的信息预测β-转弯及其类型，预测二级结构，并首次预测二面角。我们的方法使用支持向量机（一种监督分类技术），并在426、547和823个蛋白质链的三个已建立的数据集上进行了训练和测试。在预测β-转弯的位置时，我们获得了高达0.49的马修斯相关系数，这是迄今为止报道的最高值。此外，额外的二面体信息提高了对β-turn类型I、II、IV、VIII和“非特异性”的预测，相关系数分别达到0.39、0.33、0.27、0.14和0.38。我们的结果比其他方法更准确。我们已经创建了β-转变及其类型的准确预测器。我们的方法叫做DEBT，可以在http://comp.chem.nottingham.ac.uk/debt/上找到。
β-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts β-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of β-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of β-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of β-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.
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DOI：
10.1073/pnas.89.22.10915
发表时间：
1992-11-15
影响因子：
11.1
作者：
HENIKOFF, S;HENIKOFF, JG
通讯作者：
HENIKOFF, JG
影响因子：
2.9
作者：
Dor, Ofer;Zhou, Yaoqi
通讯作者：
Zhou, Yaoqi
影响因子：
5.8
作者：
Kaur, H;Raghava, GPS
通讯作者：
Raghava, GPS
影响因子：
--
作者：
Cherkassky, V
通讯作者：
Cherkassky, V
影响因子：
5.8
作者：
Asgary, Mehdi Poursheikhali;Jahandideh, Samad;Kazemnejad, Anoshirvan
通讯作者：
Kazemnejad, Anoshirvan