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.
|Original language||English (US)|
|Number of pages||22|
|Journal||Journal of Bioinformatics and Computational Biology|
|State||Published - 2009|
Bibliographical noteFunding Information:
We thank Björn Wallner and Arne Elofsson for providing us ProQres and Pro-Qprof programs, and Dongbo Bu, Xuefeng Cui, and William Wong for their thought-provoking discussions. We are grateful to Gloria Rose for proofreading the manuscript. This work is supported by the NSERC grant OGP0046506, the Canada Research Chair Program, an NSERC collaborative grant, CFI, MITACS, and an 863 Grant from the Ministry of Science and Technology of China.
- Local quality assessment
- Protein structure prediction
- SVM regression
- Sequence-structure alignment
ASJC Scopus subject areas
- Molecular Biology
- Computer Science Applications