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

2020 ◽  
Vol 15 (7) ◽  
pp. 758-766
Author(s):  
Xiaoli Xie ◽  
Yunxiu Zhao
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Protein Sequence ◽  
Graphical Representation ◽  
Protein Sequences ◽  
Important Research ◽  
Physiochemical Properties ◽  
New Methods ◽  
Alignment Free ◽  
Highly Correlated

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.

scholarly journals DV-Curve Representation of Protein Sequences and Its Application

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wei Deng ◽  
Yihui Luan
Keyword(s):  
Amino Acids ◽  
Protein Sequence ◽  
Graphical Representation ◽  
Protein Sequences ◽  
Quantitative Comparison ◽  
The Other ◽  
Hp Model ◽  
Dual Vector ◽  
Numerical Characterization ◽  
Curve Representation

Based on the detailed hydrophobic-hydrophilic(HP) model of amino acids, we propose dual-vector curve (DV-curve) representation of protein sequences, which uses two vectors to represent one alphabet of protein sequences. This graphical representation not only avoids degeneracy, but also has good visualization no matter how long these sequences are, and can reflect the length of protein sequence. Then we transform the 2D-graphical representation into a numerical characterization that can facilitate quantitative comparison of protein sequences. The utility of this approach is illustrated by two examples: one is similarity/dissimilarity comparison among different ND6 protein sequences based on their DV-curve figures the other is the phylogenetic analysis among coronaviruses based on their spike proteins.

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.

scholarly journals Protein sequence profile prediction using ProtAlbert transformer1

2021 ◽  
Author(s):  
Fatemeh Zare-Mirakabad ◽  
Armin Behjati ◽  
Seyed Shahriar Arab ◽  
Abbas Nowzari-Dalini
Keyword(s):  
Amino Acids ◽  
Protein Sequence ◽  
Nearest Neighbor ◽  
Tertiary Structure ◽  
Query Sequence ◽  
Protein Secondary Structure ◽  
Protein Sequences ◽  
Family Characteristics ◽  
Sequence Profile ◽  
Protein Sequence Profile

Protein sequences can be viewed as a language; therefore, we benefit from using the models initially developed for natural languages such as transformers. ProtAlbert is one of the best pre-trained transformers on protein sequences, and its efficiency enables us to run the model on longer sequences with less computation power while having similar performance with the other pre-trained transformers. This paper includes two main parts: transformer analysis and profile prediction. In the first part, we propose five algorithms to assess the attention heads in different layers of ProtAlbert for five protein characteristics, nearest-neighbor interactions, type of amino acids, biochemical and biophysical properties of amino acids, protein secondary structure, and protein tertiary structure. These algorithms are performed on 55 proteins extracted from CASP13 and three case study proteins whose sequences, experimental tertiary structures, and HSSP profiles are available. This assessment shows that although the model is only pre-trained on protein sequences, attention heads in the layers of ProtAlbert are representative of some protein family characteristics. This conclusion leads to the second part of our work. We propose an algorithm called PA_SPP for protein sequence profile prediction by pre-trained ProtAlbert using masked-language modeling. PA_SPP algorithm can help the researchers to predict an HSSP profile while there are no similar sequences to a query sequence in the database for making the HSSP profile.

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

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zengchao Mu ◽  
Ting Yu ◽  
Xiaoping Liu ◽  
Hongyu Zheng ◽  
Leyi Wei ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Protein Sequence ◽  
Graphical Representation ◽  
Sequence Data ◽  
Protein Sequences ◽  
Statistical Features ◽  
Research Areas ◽  
Protein Functions ◽  
Protein Sequence Data ◽  
Extraction Model

Abstract Background 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. Results 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. Conclusion 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.

scholarly journals A sequential development towards a unified approach to protein sequence comparison based on classified groups of amino acids

2018 ◽  
Vol 7 (2) ◽  
pp. 678
Author(s):  
Soumen Ghosh ◽  
Jayanta Pal ◽  
Bansibadan Maji ◽  
Dilip Kumar Bhattacharya
Keyword(s):  
Amino Acids ◽  
Protein Sequence ◽  
Sequence Comparison ◽  
Protein Sequences ◽  
Group Classification ◽  
Unified Approach ◽  
The Unified Method ◽  
Protein Sequence Comparison ◽  
Unified Method

The methods of comparison of protein sequences based on different classified groups of amino acids add a significant contribution to the literature of protein sequence comparison. But the methods vary with choice of different classified groups. Therefore, the purpose of the paper is to develop a unified approach towards the analysis of protein sequence comparison based on classification of amino acids in different groups of different cardinality. The paper considers 4 group classification, 5 group classification and 6 group classifications of amino acids, and in each case it applies the unified method for comparing two types of protein sequences, viz., 9 proteins of ND5 category and 50 Corona virus Spike Proteins. The results agree with those, which were obtained earlier by other methods based on classified groups of amino acids. An-yway it is found that the present unified formula is relatively simpler and fundamentally different from the earlier ones. Further, it can be applied conveniently in comparison of protein sequences based on all different types of classified groups of amino acids.

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

2015 ◽  
Vol 08 (05) ◽  
pp. 1550063
Author(s):  
Lei Wang ◽  
Hui Peng ◽  
Jinhua Zheng ◽  
Yanzi Qiu
Keyword(s):  
Protein Sequence ◽  
Visual Inspection ◽  
Visual Analysis ◽  
Graphical Representation ◽  
Protein Sequences ◽  
Storage Space ◽  
Efficient Tool ◽  
Characteristic Model ◽  
Representation Scheme

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.

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