Pettitt Chris S, McGuffin Liam J, Jones David T
Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
Bioinformatics. 2005 Sep 1;21(17):3509-15. doi: 10.1093/bioinformatics/bti540. Epub 2005 Jun 14.
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-like models.
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.
在一项全面的基准分析中，测试了一种简单方法（MODCHECK）确定由折叠识别生成的一组结构模型的序列-结构兼容性的能力。在最新的LiveBench-9自动结构评估实验中的188个目标上测试了四个模型质量评估程序（MQAP）。我们系统地测试和评估MQAP方法是否能够成功检测出类似天然结构的模型。
我们表明，与测试的其他三种方法相比，MODCHECK是始终能选出最佳顶级模型并对模型进行排名的最可靠方法。此外，我们表明，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。尽管这些MQAP方法未能提高已纳入蛋白质三维（3D）结构信息的方法的模型选择性能，但对于纯基于序列的方法（包括最佳的profile-profile方法），观察到了性能提升。这表明，即使是最佳的基于序列的折叠识别方法，通过考虑3D结构信息仍可得到改进。
