Intrinsic disorder prediction from the analysis of multiple protein fold recognition models
Intrinsic disorder prediction from the analysis of multiple protein fold recognition models
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DOI：
10.1093/bioinformatics/btn326
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发表时间：
2008-08-15
期刊：
影响因子：
5.8
通讯作者：
McGuffin, Liam J.
中科院分区：
文献类型：
--
作者：
McGuffin, Liam J.
动机：内在蛋白质紊乱在功能上与许多生物角色有关，因此，在所有三个生命王国的蛋白质中无处不在。确定蛋白质中的无序区域给实验方法带来了挑战，因此最近人们非常关注改进预测方法的发展。本文在分析多种蛋白质折叠识别模型的基础上，提出了一种新的无序预测技术--DISOCLUST。DISOCluust方法与CASP7实验中的top.ve方法进行了严格的基准比较。结果：即使在目标序列与已知结构没有同源性的情况下，DISOCLUST方法也能为简单的一致性方法增加最大的价值。包含DISOclust的简单方法共识可以显著优于之前测试的所有单独方法。
Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top.ve methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified.Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested.