Improving sequence-based fold recognition by using 3D model quality assessment
Improving sequence-based fold recognition by using 3D model quality assessment
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DOI：
10.1093/bioinformatics/bti540
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发表时间：
2005-09-01
期刊：
影响因子：
5.8
通讯作者：
Jones, DT
中科院分区：
文献类型：
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作者：
Pettitt, CS;McGuffin, LJ;Jones, DT
动机：在彻底的基准分析中，测试了一种简单的方法(MODCHECK)确定通过折叠识别生成的一组结构模型的序列-结构兼容性的能力。四个模型质量评估程序(MQAP)在最新的LiveBch-9自动化结构评估实验中的188个目标上进行了测试。我们系统地测试和评估了MQAP方法是否能够成功地检测出与本地相似的模型。结果：与其他三种被测试的方法相比，MODCHECK是最可靠的方法，能够一致地执行最佳顶层模型选择和对模型进行排序。此外，我们还表明，用于评估模型与实验结构相似性的模型相似性分数的选择会影响这些工具的整体性能。虽然这些MQAP方法不能改善已经结合了蛋白质三维(3D)结构信息的方法的模型选择性能，但对于纯粹基于序列的方法，包括最佳轮廓-轮廓方法，可以观察到改进。这表明，即使是最好的基于序列的折叠识别方法，也可以通过考虑3D结构信息来改进。
Motivation: The ability of a simple method (MODCHECK) to determine the sequence-structure compatibility of a set of structural models generated by fold recognition is tested in a thorough benchmark analysis. Four Model Quality Assessment Programs (MQAPs) were tested on 188 targets from the latest LiveBench-9 automated structure evaluation experiment. We systematically test and evaluate whether the MQAP methods can successfully detect native-likemodels.Results: We show that compared with the other three methods tested MODCHECK is the most reliable method for consistently performing the best top model selection and for ranking the models. In addition, we show that the choice of model similarity score used to assess a model's similarity to the experimental structure can influence the overall performance of these tools. Although these MQAP methods fail to improve the model selection performance for methods that already incorporate protein three dimension (3D) structural information, an improvement is observed for methods that are purely sequence-based, including the best profile-profile methods. This suggests that even the best sequence-based fold recognition methods can still be improved by taking into account the 3D structural information.