Primary structure similarity analysis of proteins sequences by a new graphical representation

Xu, S.C.; Li, Z.; Zhang, S.P.; Hu, Ji

doi:10.1080/1062936x.2014.955055

Cited by 6 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several computing methods measure the distance between multidimensional vectors, such as Hamming distance (HD), Euclidean distance, Elastic-matching distance, Jeffrey and Matusita distance, Manhattan distance, and Minkowski norm. Reportedly, these methods have little effect on the vector similarity [38]. The HD between two binary strings is defined by the number of bits in which they vary [39,40].…”

Section: Hamming Distance Of the Sars-cov-2mentioning

confidence: 99%

“…ContinuedSeq % A % T % C % G Seq % A % T % C % G Seq % A % T % C % G S16 29.89 32.11 18 38. 19.62 S46 29.87 32.11 18.39 19.62 S76 29.86 32.12 18.39 19.63 S17 29.89 32.11 18.38 19.62 S47 29.87 32.12 18.40 19.62 S77 29.86 32.12 18.39 19.63 S18 29.89 32.11 18.38 19.62 S48 29.89 32.11 18.38 19.62 S78 29.92 32.10 18.37 19.61 S19 29.89 32.11 18.38 19.62 S49 29.90 32.11 18.38 19.61 S79 29.85 32.13 18.38 19.63 S20 29.89 32.12 18.37 19.62 S50 29.89 32.11 18.38 19.62 S80 29.90 32.11 18.37 19.62 S21 29.89 32.11 18.38 19.62 S51 29.89 32.11 18.38 19.62 S81 29.89 32.11 18.38 19.62 S22 29.89 32.11 18.38 19.62 S52 29.87 32.11 18.39 19.63 S82 29.89 32.11 18.38 19.62 S23 29.91 32.10 18.38 19.61 S53 29.90 32.10 18.38 19.62 S83 29.89 32.11 18.38 19.62 S24 29.90 32.11 18.38 19.62 S54 29.86 32.12 18.39 19.63 S84 29.89 32.11 18.38 19.61 S25 29.89 32.11 18.38 19.62 S55 29.86 32.12 18.38 19.63 S85 29.89 32.10 18.37 19.62 S26 29.89 32.11 18.38 19.62 S56 29.87 32.12 18.39 19.63 S86 29.86 32.13 18.38 19.64 S27 29.89 32.11 18.38 19.62 S57 29.95 32.08 18.37 19.60 S87 29.89 32.11 18.38 19.62 S28 29.86 32.13 18.38 19.62 S58 29.90 32.10 18.39 19.62 S88 29.92 32.10 18.37 19.61 S29 29.90 32.11 18.37 19.62 S59 29.90 32.11 18.38 19.62 S89 29.94 32.08 18.37 19.61 S30 30.04 32.05 18.33 19.58 S60 29.95 32.10 18.36 19.60…”

unclassified

See 1 more Smart Citation

A Vicenary Analysis of SARS-CoV-2 Genomes

Hassan¹,

Rout²,

Sahoo³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Coronaviruses are responsible for various diseases ranging from the common cold to severe infections like the Middle East syndromes and the severe acute respiratory syndrome. However, a new coronavirus strain known as COVID-19 developed into a pandemic resulting in an ongoing global public health crisis. Therefore, there is a need to understand the genomic transformations that occur within this family of viruses in order to limit disease spread and develop new therapeutic targets. The nucleotide sequences of SARS-CoV-2 are consist of several bases. These bases can be classified into purines and pyrimidines according to their chemical composition. Purines include adenine (A) and guanine (G), while pyrimidines include cytosine (C) and tyrosine (T). There is a need to understand the spatial distribution of these bases on the nucleotide sequence to facilitate the development of antivirals (including neutralizing antibodies) and epitomes necessary for vaccine development. This study aimed to evaluate all the purine and pyrimidine associations within the SARS-CoV-2 genome sequence by measuring mathematical parameters including; Shannon entropy, Hurst exponent, and the nucleotide guanine-cytosine content. The Shannon entropy is used to identify closely associated sequences. Whereas Hurst exponent is used to identifying the auto-correlation of purine-pyrimidine bases even if their organization differs. Different frequency patterns can be used to determine the distribution of all four proteins and the density of each base. The GC-content is used to understand the stability of the DNA. The relevant genome sequences were extracted from the National Center for Biotechnology Information (NCBI) virus database. Furthermore, the phylogenetic properties of the COVID-19

show abstract

Section: Hamming Distance Of the Sars-cov-2mentioning

confidence: 99%

unclassified

A Vicenary Analysis of SARS-CoV-2 Genomes

Hassan¹,

Rout²,

Sahoo³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…For the widespread application of the moment of inertia in many gene numerical representation method 10 , 11 , 15 , 16 , the normalized moments of inertia for each massive sub-curve of Fermat spiral are calculated as the numerical representation of formal DNA sequence in this paper. To the convenience of calculation, the transformation of polar coordinates to plane coordinates is performed: …”

Section: Numerical Representationmentioning

confidence: 99%

One novel representation of DNA sequence based on the global and local position information

Zhu

Sun

et al. 2018

Sci Rep

View full text Add to dashboard Cite

One novel representation of DNA sequence combining the global and local position information of the original sequence has been proposed to distinguish the different species. First, for the sufficient exploitation of global information, one graphical representation of DNA sequence has been formulated according to the curve of Fermat spiral. Then, for the consideration of local characteristics of DNA sequence, attaching each point in the curve of Fermat spiral with the related mass has been applied based on the relationships of neighboring four nucleotides. In this paper, the normalized moments of inertia of the curve of Fermat spiral which composed by the points with mass has been calculated as the numerical description of the corresponding DNA sequence on the first exons of beta-global genes. Choosing the Euclidean distance as the measurement of the numerical descriptions, the similarity between species has shown the performance of proposed method.

show abstract

“…The protein shape analysis plays an important role in medical research, computer aided molecular design, protein structure retrieval and prediction, among others. However, the analysis is highly challenging due to the complexity of a protein’s three-dimensional surface shape, which can deform significantly enough to change the topological structure during molecular interactions [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Three-dimensional protein model similarity analysis based on salient shape index

Yao

Ding

et al. 2016

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundProteins play a special role in bioinformatics. The surface shape of a protein, which is an important characteristic of the protein, defines a geometric and biochemical domain where the protein interacts with other proteins. The similarity analysis among protein models has become an important topic of protein analysis, by which it can reveal the structure and the function of proteins.ResultsIn this paper, a new protein similarity analysis method based on three-dimensional protein models is proposed. It constructs a feature matrix descriptor for each protein model combined by calculating the shape index (SI) and the related salient geometric feature (SGF), and then analyzes the protein model similarity by using this feature matrix and the extended grey relation analysis.ConclusionsWe compare our method to the Multi-resolution Reeb Graph (MRG) skeleton method, the L1-medial skeleton method and the local-diameter descriptor method. Experimental results show that our protein similarity analysis method is accurate and reliable while keeping the high computational efficiency.

show abstract

Primary structure similarity analysis of proteins sequences by a new graphical representation

Cited by 6 publications

References 24 publications

A Vicenary Analysis of SARS-CoV-2 Genomes

A Vicenary Analysis of SARS-CoV-2 Genomes

One novel representation of DNA sequence based on the global and local position information

Three-dimensional protein model similarity analysis based on salient shape index

Contact Info

Product

Resources

About