DISOPRED3: precise disordered region predictions with annotated protein-binding activity.
DISOPRED3: precise disordered region predictions with annotated protein-binding activity.
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DOI：
10.1093/bioinformatics/btu744
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发表时间：
2015-03-15
期刊：
影响因子：
--
通讯作者：
Cozzetto D
中科院分区：
文献类型：
--
作者：
Jones DT;Cozzetto D
动机：相当大一部分真核蛋白含有内在无序区（idr），它们以未折叠状态或在结构化和非结构化构象之间发生转变。随着时间的推移，基于序列的idr分类器已经变得相当准确，目前的一个主要挑战是从分子水平到系统水平将idr与其生物学作用联系起来。结果：我们描述了DISOPRED3，它扩展了它的前身，使用新的模块来预测idr和它们中的蛋白质结合位点。根据最近的CASP评估结果，DISOPRED3可以被认为是idr识别的最新技术，我们的自我评估表明，它比DISOPRED2有显著改进，因为它的预测更具体，对超过20个氨基酸的idr更敏感。通过一种新的基于支持向量机的分类器，将预测的idr注释为蛋白质结合，该分类器使用剖面数据和额外的序列衍生特征。基于充分交叉验证的基准实验，我们表明该预测器生成无序蛋白质结合区域的精确分配，并且与其他公开可用的工具相比效果良好。可用性和实施：http://bioinf.cs.ucl.ac.uk/disopred联系方式：d.t.jones@ucl.ac.uk补充信息：补充数据可在Bioinformatics在线获取。
Motivation: A sizeable fraction of eukaryotic proteins contain intrinsically disordered regions (IDRs), which act in unfolded states or by undergoing transitions between structured and unstructured conformations. Over time, sequence-based classifiers of IDRs have become fairly accurate and currently a major challenge is linking IDRs to their biological roles from the molecular to the systems level. Results: We describe DISOPRED3, which extends its predecessor with new modules to predict IDRs and protein-binding sites within them. Based on recent CASP evaluation results, DISOPRED3 can be regarded as state of the art in the identification of IDRs, and our self-assessment shows that it significantly improves over DISOPRED2 because its predictions are more specific across the whole board and more sensitive to IDRs longer than 20 amino acids. Predicted IDRs are annotated as protein binding through a novel SVM based classifier, which uses profile data and additional sequence-derived features. Based on benchmarking experiments with full cross-validation, we show that this predictor generates precise assignments of disordered protein binding regions and that it compares well with other publicly available tools. Availability and implementation: http://bioinf.cs.ucl.ac.uk/disopred Contact: d.t.jones@ucl.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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影响因子：
3.7
作者：
Minneci F;Piovesan D;Cozzetto D;Jones DT
通讯作者：
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作者：
Cozzetto, Domenico;Jones, David T.
通讯作者：
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