Model quality assessment using distance constraints from alignments.
Model quality assessment using distance constraints from alignments.
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DOI：
10.1002/prot.22262
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发表时间：
2009-05-15
影响因子：
2.9
通讯作者：
Karplus, Kevin
中科院分区：
文献类型：
--
作者：
Paluszewski, Martin;Karplus, Kevin
给定特定蛋白质序列的一组替代模型，模型质量评估 (MQA) 问题要求为该组中的每个模型分配分数。一个好的 MQA 程序会分配这些分数，使它们与模型的真实质量很好地相关，理想情况下，最接近真实结构的模型得分最高。在本文中，我们提出了一种解决 MQA 问题的新方法。它基于从已知结构模板的比对中提取的距离约束，并在用于蛋白质结构预测的 Undertaker 程序中实现。一项新颖的功能是我们提取非接触约束和接触约束。我们描述了如何完成距离约束提取，并展示了如何使用它们来解决 MQA 问题。我们在 CASP7 目标上比较了我们的方法，结果表明我们的方法至少与 CASP7 评估的最佳 MQA 方法具有可比性。我们还提出了一种新的评估指标 Kendall's τ，它比用于评估 MQA 方法的传统指标（Pearson's r 和 Spearman's ρ）更具可解释性。我们展示了清晰的示例，其中 Kendall 的 τ 与我们对正确 MQA 的直觉更加一致，因此我们建议将 Kendall 的 τ 用于未来的 CASP MQA 评估。
Given a set of alternative models for a specific protein sequence, the model quality assessment (MQA) problem asks for an assignment of scores to each model in the set. A good MQA program assigns these scores such that they correlate well with real quality of the models, ideally scoring best that model which is closest to the true structure. In this paper, we present a new approach for addressing the MQA problem. It is based on distance constraints extracted from alignments to templates of known structure, and is implemented in the Undertaker program for protein structure prediction. One novel feature is that we extract non-contact constraints as well as contact constraints. We describe how the distance constraint extraction is done and we show how they can be used to address the MQA problem. We have compared our method on CASP7 targets and the results show that our method is at least comparable with the best MQA methods that were assessed at CASP7. We also propose a new evaluation measure, Kendall's τ, that is more interpretable than conventional measures used for evaluating MQA methods (Pearson's r and Spearman's ρ). We show clear examples where Kendall's τ agrees much more with our intuition of a correct MQA and we therefore propose that Kendall's τ be used for future CASP MQA assessments.
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影响因子：
2.9
作者：
Karplus, K;Katzman, S;Hughey, R
通讯作者：
Hughey, R
影响因子：
2.9
作者：
Wallner, Bjorn;Elofsson, Arne
通讯作者：
Elofsson, Arne
影响因子：
2.9
作者：
Cozzetto, Domenico;Kryshtafovych, Andriy;Tramontano, Anna
通讯作者：
Tramontano, Anna
DOI：
10.1073/pnas.85.8.2444
发表时间：
1988-04-01
影响因子：
11.1
作者：
PEARSON, WR;LIPMAN, DJ
通讯作者：
LIPMAN, DJ
影响因子：
3
作者：
McGuffin LJ
通讯作者：
McGuffin LJ