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Current Bioinformatics


ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Research Article

Co-Clustering Analysis of Protein Secondary Structures

Author(s): Lichun Ma*, Debby D. Wang, Xinyu Liu, Bin Zou and Hong Yan

Volume 12 , Issue 3 , 2017

Page: [213 - 224] Pages: 12

DOI: 10.2174/1574893612666170111145319

Price: $65


Background: The protein secondary structure provides a crucial link between a protein sequence and its final 3D structure. Thus, accurate prediction of protein secondary structure becomes very important.

Objective: In this study, we try to obtain a subset of highly regular features of the protein secondary structures. Then these features can be used in the prediction of other chains’ secondary structures.

Method: The experiment data was obtained from the Dictionary of Protein Secondary Structure (DSSP), in which eight types of secondary structures are defined. We carried out statistical analysis of the amino acids for each type of secondary structure and then concentrated our attention on α-helix and β-strand, the two most common regular secondary structures. The features of amino acids, neighbors, and hydrogen bonds (α-helix) were extracted. Then a co-clustering based method was conducted to analyze α-helix and β-strand chain-feature matrices, respectively.

Results and Conclusion: By using the features obtained from the co-clustering process, we are able to predict other chains’ structures. The prediction performs well for β-strands and long α-helices but poorly for short α-helices. Then, we further represented the features of each short α-helix by a vector. Afterwards, the prediction was made by comparing the testing vector and the training vectors in coclusters. Results show that the testing accuracy for short α-helices can reach 96% when using amino acid features as a vector. Therefore, the secondary structure of a protein sequence can be predicted with a high accuracy by using the co-clustering based method.

Keywords: Protein secondary structure, α-helix, β-strand, co-clustering, clustering.

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