NDLI: A new clustering-based method for protein structure selection

Content Provider	IEEE Xplore Digital Library
Author	Qingguo Wang Yi Shang Dong Xu
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Missouri, Columbia, MO (Qingguo Wang; Yi Shang; Dong Xu)
Abstract	In protein tertiary structure prediction, it is a crucial step to select near-native structures from a large number of candidate structural models. Despite much effort to tackle the problem of protein structure selection, the discerning power of current scoring functions is still unsatisfactory. In this paper, we developed a new clustering-based method for selecting near-native protein structures. Our method consists of three phases: filtering, clustering and cluster reduction, and centroid construction. Given a set of $C_{alpha}$ protein structures, we apply one or multiple existing scoring functions to filter out bad structures. Then, we group the remaining structures into clusters based on pair-wise similarity measured by RMSD. Each cluster is reduced iteratively to remove outliers and bad structures. Finally, we construct a centroid for each cluster by applying multi-dimensional scale techniques. The centroids are the final models. In experiments, we applied our method to a test set of representative proteins and obtained significant improvement over existing methods.
Starting Page	2891
Ending Page	2898
File Size	1887396
Page Count	8
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4634205
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Distance measurement Filtering Construction industry Databases Matrix decomposition Artificial neural networks
Content Type	Text
Resource Type	Article

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