Interaction prediction and classification of PDZ domains.
Interaction prediction and classification of PDZ domains.
复制标题
DOI：
10.1186/1471-2105-11-357
复制
发表时间：
2010-06-30
影响因子：
3
通讯作者：
Gursoy A
中科院分区：
文献类型：
--
作者：
Kalyoncu S;Keskin O;Gursoy A
PDZ结构域是一种高度保守的结构蛋白结构域，存在于数百种其他无关的信号蛋白中。PDZ结构域可以与不同蛋白质的C-末端肽结合，并作为胶水，将不同的蛋白质复合物聚集在一起，靶向特定的蛋白质并在信号通路中路由这些蛋白质。这些结构域根据其结合伴侣和形成的键的性质分为I、II和III类。PDZ结构域的结合特异性对于理解信号通路的复杂性是非常关键的。这些域如何识别和绑定它们的合作伙伴仍然是一个悬而未决的问题。目前研究的重点是两个方面：1）预测PDZ结构域将结合哪些肽，2）根据PDZ结构域的一级序列，将PDZ结构域分类为I类、II类或I-II类。三元组和二元组氨基酸频率被用作机器学习方法中的特征。使用85个PDZ结构域和181个肽，我们的模型达到了较高的预测精度（91.4%）的二元相互作用预测，优于以前研究的类似方法。此外，我们可以预测类PDZ域的准确率为90.7%。我们提出了三个关键的氨基酸序列基序，可能有重要作用的特异性模式的PDZ结构域。我们在PDZ交互数据集上的模型表明，我们的方法产生了令人鼓舞的结果。该方法可以进一步用作虚拟筛选技术，以减少PDZ结构域的推定候选靶蛋白和药物样分子的搜索空间。
PDZ domain is a well-conserved, structural protein domain found in hundreds of signaling proteins that are otherwise unrelated. PDZ domains can bind to the C-terminal peptides of different proteins and act as glue, clustering different protein complexes together, targeting specific proteins and routing these proteins in signaling pathways. These domains are classified into classes I, II and III, depending on their binding partners and the nature of bonds formed. Binding specificities of PDZ domains are very crucial in order to understand the complexity of signaling pathways. It is still an open question how these domains recognize and bind their partners. The focus of the current study is two folds: 1) predicting to which peptides a PDZ domain will bind and 2) classification of PDZ domains, as Class I, II or I-II, given the primary sequences of the PDZ domains. Trigram and bigram amino acid frequencies are used as features in machine learning methods. Using 85 PDZ domains and 181 peptides, our model reaches high prediction accuracy (91.4%) for binary interaction prediction which outperforms previously investigated similar methods. Also, we can predict classes of PDZ domains with an accuracy of 90.7%. We propose three critical amino acid sequence motifs that could have important roles on specificity pattern of PDZ domains. Our model on PDZ interaction dataset shows that our approach produces encouraging results. The method can be further used as a virtual screening technique to reduce the search space for putative candidate target proteins and drug-like molecules of PDZ domains.
登录
查看更多内容
影响因子：
1.5
作者：
通讯作者：
--
影响因子：
5.7
作者：
Kang, BS;Cooper, DR;Derewenda, ZS
通讯作者：
Derewenda, ZS
影响因子：
9.2
作者：
Fanning, AS;Anderson, JM
通讯作者：
Anderson, JM
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
影响因子：
7.5
作者：
Friedman, N;Geiger, D;Goldszmidt, M
通讯作者：
Goldszmidt, M