Research article

Improving the accuracy of template-based predictions by mixing and matching between initial models

Tianyun Liu , Michal Guerquin and Ram Samudrala

BMC Structural Biology 2008, 8:24doi:10.1186/1472-6807-8-24

Published:	5 May 2008

Abstract (provisional)

Background

Comparative modeling is a technique to predict the three dimensional structure of a given protein sequence based primarily on its alignment to one or more proteins with experimentally determined structures. A major bottleneck of current comparative modeling methods is the lack of methods to accurately refine a starting initial model so that it approaches the resolution of the corresponding experimental structure. We investigate the effectiveness of a graph-theoretic clique finding approach to solve this problem.

Results

Our refinement method takes into account the information presented in multiple templates/alignments at the three-dimensional level by mixing and matching regions between different initial comparative models. This method enables us to obtain an optimized conformation ensemble representing the best combination of secondary structures, resulting in the refined models of higher quality. In addition, the process of refinement accumulates near-native conformations, resulting in discriminating the native-like conformation in a more effective manner. In the CASP7 experiment, the refined models produced are more accurate than the starting initial models.

Conclusions

This novel approach can be applied without any manual intervention to improve the quality of comparative predictions where multiple template/alignment combinations are available for modeling, producing conformational models of higher quality than the starting initial predictions.