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Evolutionary Bioinformatics

On the basis of three kinds of indices of physicochemical properties of twenty amino acids, a novel 3D graphical representation of protein sequences is presented. Then, the improved cumulative distance of the 3D graphical representations is defined in order to compare the proteins for similarity. Furthermore, the efficiency of our approach is illustrated by performing a comparison of similarities/dissimilarities among sequences of the ND5 proteins of nine different species. A correlation and significance analysis is provided to compare our results on similarities/dissimilarities and some other graphical representation results with the ClustalW results on similarities/dissimilarities. The comparison results show that our approach has better correlations with ClustalW for all nine species than other approaches.

A novel graphical representation of proteins and its application

A 3D graphical representation of protein sequences based on the Gray code.

Feature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions In this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods The FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses

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FEGS: a novel feature extraction model for protein sequences and its applications.

Protein feature extraction plays an important role in the areas of similarity analysis of protein sequences and prediction of protein structures, functions and interactions. The feature extraction based on graphical representation is one of the most effective and efficient ways. However, most existing methods suffer limitations from their method design. We introduce DCGR, a novel method for extracting features from protein sequences based on the chaos game representation, which is developed by constructing CGR curves of protein sequences according to physicochemical properties of amino acids, followed by converting the CGR curves into multi-dimensional feature vectors by using the distributions of points in CGR images. Tested on five data sets, DCGR was significantly superior to the state-of-the-art feature extraction methods. The DCGR is practically powerful for extracting effective features from protein sequences, and therefore important in similarity analysis of protein sequences, study of protein-protein interactions and prediction of protein functions. It is freely available at https://sourceforge.net/projects/transcriptomeassembly/files/Feature%20Extraction
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DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information.

Based on chaos game representation, a two-dimensional-graphical representation of protein sequences is described in which 20 amino acids are rearranged in a cyclic order using a PAM250 substitution matrix. A numerical characterisation has been developed as a descriptor to compare protein sequences. Finally, an example is given in which the dehydrogenase subunit 5 (ND5) protein sequences of nine species are compared.

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Papers

A new graphical representation of similarity/dissimilarity studies of protein sequences