Graphical Representation Of Protein Sequences Research Articles

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

BackgroundFeature 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.ResultsIn 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.ConclusionThe 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.

A 2D Non-degeneracy Graphical Representation of Protein Sequence and Its Applications

Background: The comparison of the protein sequences is an important research filed in bioinformatics. Many alignment-free methods have been proposed. Objective: In order to mining the more information of the protein sequence, this study focus on a new alignment-free method based on physiochemical properties of amino acids. Methods: Average physiochemical value (Apv) has been defined. For a given protein sequence, a 2D curve was outlined based on Apv and position of the amino acid, and there is not loop and intersection on the curve. According to the curve, the similarity/dissimilarity of the protein sequences can be analyzed. Results and Conclusion: Two groups of protein sequences are taken as examples to illustrate the new methods, the protein sequences can be classified correctly, and the results are highly correlated with that of ClustalW. The new method is simple and effective.

Measuring Similarity among Protein Sequences Using a New Descriptor.

The comparison of protein sequences according to similarity is a fundamental aspect of today's biomedical research. With the developments of sequencing technologies, a large number of protein sequences increase exponentially in the public databases. Famous sequences' comparison methods are alignment based. They generally give excellent results when the sequences under study are closely related and they are time consuming. Herein, a new alignment-free method is introduced. Our technique depends on a new graphical representation and descriptor. The graphical representation of protein sequence is a simple way to visualize protein sequences. The descriptor compresses the primary sequence into a single vector composed of only two values. Our approach gives good results with both short and long sequences within a little computation time. It is applied on nine beta globin, nine ND5 (NADH dehydrogenase subunit 5), and 24 spike protein sequences. Correlation and significance analyses are also introduced to compare our similarity/dissimilarity results with others' approaches, results, and sequence homology.

A novel graphical representation and similarity analysis of protein sequences based on physicochemical properties

One of popular topic in bioinformatics is protein sequence analysis. The graphical representation of protein sequence is a simple and common way to visualize protein sequences. In this study, a numerical descriptive vector for a given protein sequence is calculated based on twelve physicochemical properties of amino acids (AAs) and principal component analysis (PCA). Each entry of the descriptive vector corresponds to one AA in the sequence. By this vector, an intuitive spectrum-like graphical representation of protein sequence is proposed. Squared correlation coefficient as well as moving window correlation coefficient, as a new similarity/dissimilarity measure, were used to compare different sequences. Applicability of the proposed method is assessed by analyzing the nine ND5 proteins. The results revealed the utility of the proposed method.

A novel model for protein sequence similarity analysis based on spectral radius

Advances in sequencing technologies led to rapid increase in the number and diversity of biological sequences, which facilitated development in the sequence research. In this paper, we present a new method for analyzing protein sequence similarity. We calculated the spectral radii of 20 amino acids (AAs) and put forward a novel 2-D graphical representation of protein sequences. To characterize protein sequences numerically, three groups of features were extracted and related to statistical, dynamics measurements and fluctuation complexity of the sequences. With the obtained feature vector, two models utilizing Gaussian Kernel similarity and Cosine similarity were built to measure the similarity between sequences. We applied our method to analyze the similarities/dissimilarities of four data sets. Both proposed models received consistent results with improvements when compared to that obtained by the ClustalW analysis. The novel approach we present in this study may therefore benefit protein research in medical and scientific fields.

Novel Method of 3-Dimensional Graphical Representation for Proteins and Its Application.

In this article, we propose a 3-dimensional graphical representation of protein sequences based on 10 physicochemical properties of 20 amino acids and the BLOSUM62 matrix. It contains evolutionary information and provides intuitive visualization. To further analyze the similarity of proteins, we extract a specific vector from the graphical representation curve. The vector is used to calculate the similarity distance between 2 protein sequences. To prove the effectiveness of our approach, we apply it to 3 real data sets. The results are consistent with the known evolution fact and show that our method is effective in phylogenetic analysis.

A Novel 2D Graphical Representation and Its Application in the Similarities/Dissimilarities Analysis of Protein Sequences

In this study, a novel 2D graphical representation of protein sequences is proposed based on the physicochemical feature pK2 of amino acids first and then, on the basis of the newly given 2D graphical representation, a new concept of feature appearance model is introduced to analyze the similarity/dissimilarity of protein sequences. Finally, Theoretical and simulation results show that the newly proposed method is effective in similarities/dissimilarities analysis of protein sequences.

A new graphical representation of protein sequences and its applications

Sequence analysis is one of the foundations in bioinformatics for the abundant information hidden in the sequences. It is helpful for scientists’ study on the function of DNA, proteins and cells. In this paper, we outline a novel method for protein sequences similarity analysis based on the physical–chemical properties of amino acids. We consider the protein sequence as a rigid-body with mass. Then we introduce the moment of inertia to the calculation of similarity of sequences and the sequences are transformed into vectors by the tensor for moment of inertia. The Euclidean distance is employed as a measurement of the similarities. At last, the comparison with other references’ results shows our approach is reasonable and effective.

A 2D Graphical Representation of Protein Sequence and Its Application

A 2D Graphical Representation of Protein Sequence and Its Application

A new 2D graphical representation of protein sequence and its application

Graphical representation is a very efficient tool for visual analysis of protein sequences. In this paper, a novel 2D graphical representation scheme is proposed on the basis of a newly introduced concept, named characteristic model of the protein sequences. After obtaining the 2D graphics of protein sequences, two numerical characterizations of them is designed as descriptors to analyze the nine DN5 protein sequences, simulation and analysis results show that, comparing with existing methods, our method is not only visible, intuitional, and simple, but also has no circuit or degeneracy, and even more important, since the storage space required by our method is constant and has nothing to do with the length of protein sequences, then it can keep excellent visual inspection for long protein sequences.

An efficient numerical method for protein sequences similarity analysis based on a new two-dimensional graphical representation

A new two-dimensional graphical representation of protein sequences is introduced. Twenty concentric evenly spaced circles divided by n radial lines into equal divisions are selected to represent any protein sequence of length n. Each circle represents one of the different 20 amino acids, and each radial line represents a single amino acid of the protein sequence. An efficient numerical method based on the graph is proposed to measure the similarity between two protein sequences. To prove the accuracy of our approach, the method is applied to NADH dehydrogenase subunit 5 (ND5) proteins of nine different species and 24 transferrin sequences from vertebrates. High values of correlation coefficient between our results and the results of ClustalW are obtained (approximately perfect correlations). These values are higher than the values obtained in many other related works.

Novel numerical characterization of protein sequences based on individual amino acid and its application.

The hydrophobicity and hydrophilicity of amino acids play a very important role in protein folding and its interaction with the environment and other molecules, as well as its catalytic mechanism. Based on the two physicochemical indexes, a 2D graphical representation of protein sequences is introduced; meanwhile, a new numerical characteristic has been proposed to compute the distance of different sequences for analysis of sequence similarity/dissimilarity on the basis of this graphical representation. Furthermore, we apply the new distance in the similarities/dissimilarities of ND5 proteins of nine species and predict the four major classes based on the dataset containing 639 domains. The results show that the method is simple and effective.

3D Graphical Representation of Protein Sequences Based on Conformational Parameters of Amino Acids

Based on the helix and-sheet and the-turn conformational parameters, and and , of the 20 amino acids, we propose a new 3D graphical representation of protein sequence without circuit or degeneracy, which may reflect the innate structure of the protein sequence. Then the numerical characterizations of protein graphs, the leading eigenvalues of the L/L matrices associated with the graphical curves for protein sequences, was utilized as descriptors to analyze the similarity/dissimilarity of the nine ND5 protein sequences.

A graphical representation of protein based on a novel iterated function system

In this article, a novel family of iterated function system (IFS) was introduced to outline a 2D graphical representation of protein sequences, which incorporates with various physicochemical properties of amino acids. Then a mathematical description was suggested to quantificationally compare the similarities and dissimilarities of protein sequences from their 2D curves. Based on this method, similarities/dissimilarities were compared among sequences of the ND5 proteins of nine different species, as well as sequences of eight ND6 proteins. The phylogenetic tree of the nine ND5 proteins was constructed according to Fuzzy cluster analysis. By correlation analysis, the ClustalW results were compared with our similarity/dissimilarity results and other graphical representation results to demonstrate the effectiveness of our approach.

A novel descriptor of protein sequences and its application

In this paper, a dynamic 3-D graphical representation of protein sequences is introduced based on three physical–chemical properties of amino acids. The coordinates of the graph have direct biological significance, which could reflect the innate structure of the proteins. The information of principal moments of inertia and range of axis coordinate are extracted as a novel mixed descriptor and proposed for the comparison of protein primary sequences. Meanwhile, the Euclidean distance of the normalized descriptor vectors which avoid the influence of the difference in length of protein sequences under consideration is employed as a quantitative measurement of the similarity of proteins. Finally, we take the nine ND5 (NADH dehydrogenase subunit 5) proteins for example and illustrate the effectiveness of our approach.

ADLD: a novel graphical representation of protein sequences and its application.

To facilitate the intuitional analysis of protein sequences, a novel graphical representation of protein sequences called ADLD (Alignment Diagonal Line Diagram) is introduced in this paper first, and then a new ADLD based method is proposed and utilized to analyze the similarity/dissimilarity of protein sequences. Comparing with existing methods, our ADLD based method is proved to be effective in the similarity/dissimilarity analysis of protein sequences and have the merits of good intuition, visuality, and simplicity. The examinations of the similarities/dissimilarities for both the 16 different ND5 proteins and the 29 different spike proteins illustrate the utility of our ADLD based approach.

Similarity/Dissimilarity analysis of protein sequences based on a new spectrum-like graphical representation.

Sequence comparison is one of the foundations in bioinformatics, which can be used to study evolutionary relations among the sequences. In this study, a 2D spectrum-like graphical representation of protein sequences is presented based on the hydrophobicity scale of amino acids. The frequencies of amplitudes of 4-subsequences are adopted to characterize a spectrum-like graph, and a 17D vector is used as the descriptor of protein sequence. The χ2 value of compatibility test is performed. New similarity analysis approach is illustrated on the all protein sequences, which are encoded by the mitochondrion genome of 20 different species. Finally, comparison with the ClustalW method shows the utility of our method.

UC-Curve: A highly compact 2D graphical representation of protein sequences

A highly compact two-dimensional graphical representation of protein sequences, namely, UC-Curve, is presented by assigning amino acids to the circumference of a unit circle with a cyclic order. UC-Curves can visually reveal general composition features of protein sequences, and roughly exhibit major differences among similar protein sequences. Geometric center vectors of UC-Curves and Euclidean distances are extracted, respectively, to analyze pairwise similarities/dissimilarities between two different families of proteins. Comparative results demonstrate the robustness of the technique and show that UC-Curves could help to inference reasonable phylogenetic relationships with relatively less computational cost. © 2013 Wiley Periodicals, Inc.

HR-Curve: A Novel 2D Graphical Representation of Protein Sequence and Its Multi-Application

HR-Curve: A Novel 2D Graphical Representation of Protein Sequence and Its Multi-Application

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

Based on the order of 6-bit binary Gray code, a cyclic order of 20 amino acids is introduced. A novel 3D graphical representation of protein sequences is proposed according to the CGR of DNA sequences. Furthermore, the mathematical descriptor is suggested to characterize the graphical representation curve. The efficiency of our approach can be illustrated by performing the comparison of similarities/dissimilarities among sequences of the ND5 proteins of nine different species. With the correlation and significance analysis, the comparisons of both our results and results of other graphical representation with the ClustalW's results can show the utility of our approach.