Prediction of disordered regions in proteins based on the meta approach
Prediction of disordered regions in proteins based on the meta approach
复制标题
DOI：
10.1093/bioinformatics/btn195
复制
发表时间：
2008-06-01
期刊：
影响因子：
5.8
通讯作者：
Kinoshita, Kengo
中科院分区：
文献类型：
--
作者：
Ishida, Takashi;Kinoshita, Kengo
动机：蛋白质的内在无序区域在没有其伴侣分子的情况下没有独特的稳定结构，因此这些区域有时会妨碍高质量的结构测定。此外，具有无序区域的蛋白质经常参与重要的生物过程，无序区域被认为在分子相互作用中起重要作用。因此，识别无序区域对于获得高分辨率的结构信息和了解这些蛋白质的功能方面非常重要。结果：我们建立了一种基于meta方法的蛋白质无序区预测方法，并实现了一个web服务器metaPrDOS。该方法利用支持向量机从7个独立预测器的预测结果中预测每个残差的无序趋势。使用CASP7预测靶标对meta方法进行评估，以避免由于在某些成分预测因子的训练集中包含了使用的蛋白质而导致的高估。因此，meta方法的预测精度高于所有参与CASP7的方法。
Motivation: Intrinsically disordered regions in proteins have no unique stable structures without their partner molecules, thus these regions sometimes prevent high-quality structure determination. Furthermore, proteins with disordered regions are often involved in important biological processes, and the disordered regions are considered to play important roles in molecular interactions. Therefore, identifying disordered regions is important to obtain high-resolution structural information and to understand the functional aspects of these proteins.Results: We developed a new prediction method for disordered regions in proteins based on the meta approach and implemented a web-server for this prediction method named metaPrDOS. The method predicts the disorder tendency of each residue using support vector machines from the prediction results of the seven independent predictors. Evaluation of the meta approach was performed using the CASP7 prediction targets to avoid an overestimation due to the inclusion of proteins used in the training set of some component predictors. As a result, the meta approach achieved higher prediction accuracy than all methods participating in CASP7.