Identify High-Quality Protein Structural Models by Enhanced K-Means
Joint Authors
Wu, Hongjie
Li, Haiou
Jiang, Min
Chen, Cheng
Wu, Chuang
Lü, Qiang
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-22
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Background.
One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys.
Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases.
Results.
Here, we proposed two enhanced K-means clustering algorithms capable of robustly identifying high-quality protein structural models.
The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++).
Our results showed that SK-means and K-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER.
Conclusions.
We observed that the classic K-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification.
Both SK-means and K-means++ demonstrated substantial improvements relative to results from SPICKER and classical K-means.
American Psychological Association (APA)
Wu, Hongjie& Li, Haiou& Jiang, Min& Chen, Cheng& Lü, Qiang& Wu, Chuang. 2017. Identify High-Quality Protein Structural Models by Enhanced K-Means. BioMed Research International،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1138495
Modern Language Association (MLA)
Wu, Hongjie…[et al.]. Identify High-Quality Protein Structural Models by Enhanced K-Means. BioMed Research International No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1138495
American Medical Association (AMA)
Wu, Hongjie& Li, Haiou& Jiang, Min& Chen, Cheng& Lü, Qiang& Wu, Chuang. Identify High-Quality Protein Structural Models by Enhanced K-Means. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1138495
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138495