Gao Xin, Xu Jinbo, Li Shuai Cheng, Li Ming
David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada.
J Bioinform Comput Biol. 2009 Oct;7(5):789-810. doi: 10.1142/s0219720009004345.
Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to identify some well-predicted regions of the model, so that the structural biology community can benefit from the automated structure prediction. It is also important to identify badly-predicted regions in a model so that some refinement measurements can be applied to it. We present two complementary techniques, FragQA and PosQA, to accurately predict local quality of a sequence-structure (i.e. sequence-template) alignment generated by comparative modeling (i.e. homology modeling and threading). FragQA and PosQA predict local quality from two different perspectives. Different from existing methods, FragQA directly predicts cRMSD between a continuously aligned fragment determined by an alignment and the corresponding fragment in the native structure, while PosQA predicts the quality of an individual aligned position. Both FragQA and PosQA use an SVM (Support Vector Machine) regression method to perform prediction using similar information extracted from a single given alignment. Experimental results demonstrate that FragQA performs well on predicting local fragment quality, and PosQA outperforms two top-notch methods, ProQres and ProQprof. Our results indicate that (1) local quality can be predicted well; (2) local sequence evolutionary information (i.e. sequence similarity) is the major factor in predicting local quality; and (3) structural information such as solvent accessibility and secondary structure helps to improve the prediction performance.
尽管近年来蛋白质结构预测取得了很大进展，但通过自动预测方法得到的蛋白质模型仍存在各种误差。随着结构预测方法的发展，一个持续存在的问题是如何评估蛋白质模型的质量，特别是识别模型中一些预测良好的区域，以便结构生物学界能够从自动结构预测中受益。识别模型中预测不佳的区域也很重要，这样就可以对其应用一些优化措施。我们提出了两种互补技术，FragQA和PosQA，以准确预测通过比较建模（即同源建模和穿线法）生成的序列-结构（即序列-模板）比对的局部质量。FragQA和PosQA从两个不同的角度预测局部质量。与现有方法不同，FragQA直接预测比对确定的连续比对片段与天然结构中相应片段之间的cRMSD，而PosQA预测单个比对位置的质量。FragQA和PosQA都使用支持向量机（SVM）回归方法，利用从单个给定比对中提取的相似信息进行预测。实验结果表明，FragQA在预测局部片段质量方面表现良好，而PosQA优于两种一流方法ProQres和ProQprof。我们的结果表明：（1）局部质量可以得到很好的预测；（2）局部序列进化信息（即序列相似性）是预测局部质量的主要因素；（3）诸如溶剂可及性和二级结构等结构信息有助于提高预测性能。
