Abstract
Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method.
Highlights
The protein universe is diverse for sequences, structures and functions, and classification of sequences may infer the structures and functions of proteins
LibD3C is an ensemble classifier which is based on clustering and parallel implementation [17]. nDNA-Prot proposed in [22], is a new predictor to accurately identify DNA-binding proteins when combines with an ensemble classifier
We model the protein universe as an undirected network and classify proteins according to their strength of connections
Summary
The protein universe is diverse for sequences, structures and functions, and classification of sequences may infer the structures and functions of proteins. Many compute the biological distances and classify proteins using polygenetic-trees, since sequence and structural similarity is considered to be closely related to protein homology [2]. Some representatives of these methods are the natural vector method [10, 13, 14], protein map [8,9], K-string dictionary [11], and Yau-Hausdorff distance [6]. An improved protein structural classes predictor is proposed in [23] which incorporates both sequence and structure information.
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