3D graphical representation of protein sequences and their statistical characterization

2010 ◽  
Vol 389 (21) ◽  
pp. 4668-4676 ◽  
Author(s):  
Moheb I. Abo el Maaty ◽  
Mervat M. Abo-Elkhier ◽  
Marwa A. Abd Elwahaab
Keyword(s):  
Graphical Representation ◽  
Protein Sequences ◽  
Statistical Characterization ◽  
3D Graphical Representation

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

2014 ◽  
Vol 989-994 ◽  
pp. 3599-3604
Author(s):  
Qian Jun Xiao ◽  
Zong Gang Deng
Keyword(s):  
Amino Acids ◽  
Protein Sequence ◽  
Graphical Representation ◽  
Protein Sequences ◽  
3D Graphical Representation ◽  
Protein Graphs

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

2012 ◽  
Vol 304 ◽  
pp. 81-87 ◽  
Author(s):  
Ping-an He ◽  
Dan Li ◽  
Yanping Zhang ◽  
Xin Wang ◽  
Yuhua Yao
Keyword(s):  
Graphical Representation ◽  
Protein Sequences ◽  
Gray Code ◽  
3D Graphical Representation

scholarly journals Similarity/Dissimilarity Analysis of Protein Sequences Based on a New Spectrum-Like Graphical Representation

2014 ◽  
Vol 10 ◽  
pp. EBO.S14713 ◽  
Author(s):  
Yuhua Yao ◽  
Shoujiang Yan ◽  
Huimin Xu ◽  
Jianning Han ◽  
Xuying Nan ◽  
...  
Keyword(s):  
Graphical Representation ◽  
Protein Sequences

scholarly journals Measuring Similarity among Protein Sequences Using a New Descriptor

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Mervat M. Abo-Elkhier ◽  
Marwa A. Abd Elwahaab ◽  
Moheb I. Abo El Maaty
Keyword(s):  
Protein Sequence ◽  
Nadh Dehydrogenase ◽  
Graphical Representation ◽  
Protein Sequences ◽  
Computation Time ◽  
Fundamental Aspect ◽  
Beta Globin ◽  
Nadh Dehydrogenase Subunit ◽  
The Public ◽  
Sequencing Technologies

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.

scholarly journals Protein Sequence Comparison and DNA-binding Protein Identification with Generalized PseAAC and Graphical Representation

2018 ◽  
Vol 21 (2) ◽  
pp. 100-110 ◽  
Author(s):  
Chun Li ◽  
Jialing Zhao ◽  
Changzhong Wang ◽  
Yuhua Yao
Keyword(s):  
Dna Binding ◽  
Protein Sequence ◽  
Protein Identification ◽  
Binding Proteins ◽  
Graphical Representation ◽  
Sequence Data ◽  
Protein Sequences ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Letter Sequence

Aim and Objective: The rapid increase in the amount of protein sequence data available leads to an urgent need for novel computational algorithms to analyze and compare these sequences. This study is undertaken to develop an efficient computational approach for timely encoding protein sequences and extracting the hidden information. Methods: Based on two physicochemical properties of amino acids, a protein primary sequence was converted into a three-letter sequence, and then a graph without loops and multiple edges and its geometric line adjacency matrix were obtained. A generalized PseAAC (pseudo amino acid composition) model was thus constructed to characterize a protein sequence numerically. Results: By using the proposed mathematical descriptor of a protein sequence, similarity comparisons among β-globin proteins of 17 species and 72 spike proteins of coronaviruses were made, respectively. The resulting clusters agreed well with the established taxonomic groups. In addition, a generalized PseAAC based SVM (support vector machine) model was developed to identify DNA-binding proteins. Experiment results showed that our method performed better than DNAbinder, DNA-Prot, iDNA-Prot and enDNA-Prot by 3.29-10.44% in terms of ACC, 0.056-0.206 in terms of MCC, and 1.45-15.76% in terms of F1M. When the benchmark dataset was expanded with negative samples, the presented approach outperformed the four previous methods with improvement in the range of 2.49-19.12% in terms of ACC, 0.05-0.32 in terms of MCC, and 3.82- 33.85% in terms of F1M. Conclusion: These results suggested that the generalized PseAAC model was very efficient for comparison and analysis of protein sequences, and very competitive in identifying DNA-binding proteins.

The graphical representation of protein sequences based on the physicochemical properties and its applications

2010 ◽  
Vol 31 (11) ◽  
pp. 2136-2142 ◽  
Author(s):  
Ping-An He ◽  
Yan-Ping Zhang ◽  
Yu-Hua Yao ◽  
Yi-Fa Tang ◽  
Xu-Ying Nan
Keyword(s):  
Physicochemical Properties ◽  
Graphical Representation ◽  
Protein Sequences
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