By Topic

Fast Algorithm for Clustering a Large Number of Protein Structural Decoys

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

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 http://mufold.org/clustering.php.

Published in:

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

Date of Conference:

12-15 Nov. 2011