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Fast Algorithm for Clustering a Large Number of Protein Structural Decoys

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2 Author(s)
Jingfen Zhang ; Dept. of Comput. Sci., Univ. of Missouri, Columbia, MO, USA ; Dong Xu

Current protein structure prediction methods often generate a large number of structural candidates (decoys), and then select near-native decoys through clustering. Classical clustering methods for decoys are time consuming due to the pair-wise distance calculation between decoys. In this study, we developed a novel method for very fast decoy clustering. Instead of the commonly used pair-wise RMSD (pRMSD) values, we propose a new distance measure C-score based on contact maps of decoys. The analysis indicates that C-score and pRMSD are highly correlated and the clusters obtained from pRMSD and C-score are highly similar. Our C-score based clustering achieves a calculation time linearly proportional to the number of decoys while obtaining almost the same accuracy for near-native model selection in comparison to existing methods such as SPICKER and Calibur with calculation time quadratic to the number of decoys. Our method has been implemented in a package named MUFOLD-CL, available at

Published in:

Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on

Date of Conference:

12-15 Nov. 2011