A hidden Markov model for predicting protein interfaces.
A hidden Markov model for predicting protein interfaces.
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DOI：
10.1142/s0219720007002722
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发表时间：
2007-06-01
影响因子：
1
通讯作者：
Cios, Krzysztof J
中科院分区：
文献类型：
--
作者：
Nguyen, Cao;Gardiner, Katheleen J;Cios, Krzysztof J
蛋白质之间的相互作用在蛋白质功能中起着决定性的作用。识别蛋白质中的相互作用部位是理解其作用机制以及药物设计的关键问题。为了预测蛋白质链中参与蛋白质复合体的位置，我们发展了一种基于隐马尔可夫模型的新方法，该方法结合了目标残基附近序列的几个生物学特征：结构信息、可访问表面积和氨基酸之间的转移概率。我们对139个独特的蛋白质进行了5次交叉验证，结果表明该方法识别界面的准确率为66%，召回率为61%。这些结果比其他用于识别界面的方法所获得的结果要好。
Protein-protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.