The work was performed at the University of Missouri-Columbia.
Prediction Methods and Reports
Article first published online: 14 OCT 2011
Copyright © 2011 Wiley-Liss, Inc.
Proteins: Structure, Function, and Bioinformatics
Volume 79, Issue Supplement S10, pages 185–195, 2011
How to Cite
Wang, Q., Vantasin, K., Xu, D. and Shang, Y. (2011), MUFOLD-WQA: A new selective consensus method for quality assessment in protein structure prediction. Proteins, 79: 185–195. doi: 10.1002/prot.23185
The authors state no conflict of interest.
- Issue published online: 9 NOV 2011
- Article first published online: 14 OCT 2011
- Accepted manuscript online: 14 SEP 2011 03:56PM EST
- Manuscript Accepted: 27 AUG 2011
- Manuscript Revised: 25 AUG 2011
- Manuscript Received: 2 FEB 2011
- National Institutes of Health. Grant Number: R21/R33-GMO78601
- protein tertiary structure prediction;
- protein model quality assessment;
- protein model selection;
- consensus method;
- critical assessment of techniques for protein structure prediction
Assessing the quality of predicted models is essential in protein tertiary structure prediction. In the past critical assessment of techniques for protein structure prediction (CASP) experiments, consensus quality assessment (QA) methods have shown to be very effective, outperforming single-model methods and other competing approaches by a large margin. In the consensus QA approach, the quality score of a model is typically estimated based on pair-wise structure similarity of it to a set of reference models. In CASP8, the differences among the top QA servers were mostly in the selection of the reference models. In this article, we present a new consensus method “SelCon” based on two key ideas: (1) to adaptively select appropriate reference models based on the attributes of the whole set of predicted models and (2) to weigh different reference models differently, and in particular not to use models that are too similar or too different from the candidate model as its references. We have developed several reference selection functions in SelCon and obtained improved QA results over existing QA methods in experiments using CASP7 and CASP8 data. In the recently completed CASP9 in 2010, the new method was implemented in our MUFOLD-WQA server. Both the official CASP9 assessment and our in-house evaluation showed that MUFOLD-WQA performed very well and achieved top performances in both the global structure QA and top-model selection category in CASP9. Proteins 2011; © 2011 Wiley-Liss, Inc.