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Clustering 100,000 Protein Structure Decoys in Minutes

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3 Author(s)
Shuai Cheng Li ; Dept. of Comput. Sci., City Univ. of Hong Kong, Kowloon, China ; Dongbo Bu ; Ming Li

Ab initio protein structure prediction methods first generate large sets of structural conformations as candidates (called decoys), and then select the most representative decoys through clustering techniques. Classical clustering methods are inefficient due to the pairwise distance calculation, and thus become infeasible when the number of decoys is large. In addition, the existing clustering approaches suffer from the arbitrariness in determining a distance threshold for proteins within a cluster: a small distance threshold leads to many small clusters, while a large distance threshold results in the merging of several independent clusters into one cluster. In this paper, we propose an efficient clustering method through fast estimating cluster centroids and efficient pruning rotation spaces. The number of clusters is automatically detected by information distance criteria. A package named ONION, which can be downloaded freely, is implemented accordingly. Experimental results on benchmark data sets suggest that ONION is 14 times faster than existing tools, and ONION obtains better selections for 31 targets, and worse selection for 19 targets compared to SPICKER's selections. On an average PC, ONION can cluster 100,000 decoys in around 12 minutes.

Published in:

Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:9 ,  Issue: 3 )