...
首页> 外文期刊>Proteins: Structure, Function, and Genetics >Assessment of template based protein structure predictions in CASP9
【24h】

Assessment of template based protein structure predictions in CASP9

机译:评估CASP9中基于模板的蛋白质结构预测

获取原文
获取原文并翻译 | 示例
           

摘要

In the Ninth Edition of the Critical Assessment of Techniques for Protein Structure Prediction (CASP9), 61,665 models submitted by 176 groups were assessed for their accuracy in the template based modeling category. The models were evaluated numerically in comparison to their experimental control structures using two global measures (GDT and GDC), and a novel local score evaluating the correct modeling of local interactions (1DDT). Overall, the state of the art of template based modeling in CASP9 is high, with many groups performing well. Among the methods registered as prediction "servers", six independent groups are performing on average better than the rest. The submissions by "human" groups are dominated by mcta-predic-tors, with one group performing noticeably better than the others. Most of the participating groups failed to assign realistic confidence estimates to their predictions, and only a very small fraction of the assessed methods have provided highly accurate models and realistic error estimates at the same time. Also, the accuracy of predictions for homo-oli-gomeric assemblies was overall poor, and only one group performed better than a naive control predictor. Here, we present the results of our assessment of the CASP9 predictions in the category of template based modeling, documenting the state of the art and highlighting areas for future developments.
机译:在第九版《蛋白质结构预测技术的关键评估》(CASP9)中,对176个小组提交的61,665个模型在基于模板的建模类别中的准确性进行了评估。与模型的实验控制结构相比,使用两个全局量度(GDT​​和GDC)对模型进行了数值评估,并使用新颖的局部评分评估了局部相互作用的正确建模(1DDT)。总体而言,CASP9中基于模板的建模的技术水平很高,许多小组的表现都很好。在注册为预测“服务器”的方法中,六个独立组的平均表现要好于其他组。 mcta-predic-tor主导了“人类”小组的提交,其中一个小组的表现明显优于其他小组。大多数参与小组未能为他们的预测分配现实的置信度估计,并且只有极少数的评估方法同时提供了高度准确的模型和现实的误差估计。同样,对同源寡聚体装配体的预测准确性总体较差,只有一组比幼稚的对照预测子表现更好。在这里,我们将介绍基于模板的建模类别中对CASP9预测的评估结果,记录最新技术并突出显示未来的发展领域。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号