Paper Info
Title
Clustering 100,000 Protein Structure Decoys in Minutes
Abstract
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.
Year
DOI
Venue
2012
10.1109/TCBB.2011.142
IEEE/ACM Trans. Comput. Biology Bioinform.
Keywords
Field
DocType
bioinformatics,proteins,protein structure prediction,computational biology,molecular biophysics,density functional theory,clustering,clustering algorithms,protein structure,ab initio calculations
k-medians clustering,Protein structure prediction,Cluster (physics),Pairwise comparison,Complete-linkage clustering,Computer science,Information distance,Bioinformatics,Cluster analysis,Centroid
Journal
Volume
Issue
ISSN
9
3
1545-5963
Citations 
PageRank 
References 
2
0.37
12
Authors
3
Name
Order
Citations
PageRank
Shuai Cheng Li118430.25
Dongbo Bu215721.54
Ming Li35595829.00