The authors state no conflict of interest.
Prediction Methods and Reports
A multilayer evaluation approach for protein structure prediction and model quality assessment †
Article first published online: 14 OCT 2011
Copyright © 2011 Wiley-Liss, Inc.
Proteins: Structure, Function, and Bioinformatics
Volume 79, Issue Supplement S10, pages 172–184, 2011
How to Cite
Zhang, J., Wang, Q., Vantasin, K., Zhang, J., He, Z., Kosztin, I., Shang, Y. and Xu, D. (2011), A multilayer evaluation approach for protein structure prediction and model quality assessment . Proteins, 79: 172–184. doi: 10.1002/prot.23184
- Issue published online: 9 NOV 2011
- Article first published online: 14 OCT 2011
- Accepted manuscript online: 14 SEP 2011 03:56PM EST
- Manuscript Accepted: 5 SEP 2011
- Manuscript Revised: 26 AUG 2011
- Manuscript Received: 1 APR 2011
- National Institutes of Health. Grant Number: R21/R33-GM078601
- protein structure prediction;
- structural model quality assessment;
- consensus quality assessment;
Protein tertiary structures are essential for studying functions of proteins at molecular level. An indispensable approach for protein structure solution is computational prediction. Most protein structure prediction methods generate candidate models first and select the best candidates by model quality assessment (QA). In many cases, good models can be produced, but the QA tools fail to select the best ones from the candidate model pool. Because of incomplete understanding of protein folding, each QA method only reflects partial facets of a structure model and thus has limited discerning power with no one consistently outperforming others. In this article, we developed a set of new QA methods, including two QA methods for evaluating target/template alignments, a molecular dynamics (MD)-based QA method, and three consensus QA methods with selected references to reveal new facets of protein structures complementary to the existing methods. Moreover, the underlying relationship among different QA methods were analyzed and then integrated into a multilayer evaluation approach to guide the model generation and model selection in prediction. All methods are integrated and implemented into an innovative and improved prediction system hereafter referred to as MUFOLD. In CASP8 and CASP9, MUFOLD has demonstrated the proof of the principles in terms of both QA discerning power and structure prediction accuracy. Proteins 2011; © 2011 Wiley-Liss, Inc.