Fasnacht Marc, Zhu Jiang, Honig Barry
Howard Hughes Medical Institute at Columbia University, Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, New York, New York 10032, USA.
Protein Sci. 2007 Aug;16(8):1557-68. doi: 10.1110/ps.072856307. Epub 2007 Jun 28.
In this study, we address the problem of local quality assessment in homology models. As a prerequisite for the evaluation of methods for predicting local model quality, we first examine the problem of measuring local structural similarities between a model and the corresponding native structure. Several local geometric similarity measures are evaluated. Two methods based on structural superposition are found to best reproduce local model quality assessments by human experts. We then examine the performance of state-of-the-art statistical potentials in predicting local model quality on three qualitatively distinct data sets. The best statistical potential, DFIRE, is shown to perform on par with the best current structure-based method in the literature, ProQres. A combination of different statistical potentials and structural features using support vector machines is shown to provide somewhat improved performance over published methods.
在本研究中，我们探讨了同源模型中的局部质量评估问题。作为评估预测局部模型质量方法的前提条件，我们首先研究了测量模型与相应天然结构之间局部结构相似性的问题。对几种局部几何相似性度量进行了评估。发现基于结构叠加的两种方法能最好地重现人类专家对局部模型质量的评估。然后，我们在三个性质不同的数据集上检验了当前最先进的统计势在预测局部模型质量方面的性能。结果表明，最佳的统计势DFIRE与文献中当前最佳的基于结构的方法ProQres表现相当。使用支持向量机将不同的统计势和结构特征相结合，其性能比已发表的方法略有提高。
