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

Wu, Chuanyan; Gao, Rui; De Marinis, Yang LU and Zhang, Yusen (2018) In Journal of Theoretical Biology 446. p.61-70
Abstract

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... (More)

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.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Fluctuation complexity, Functional group, Protein sequence similarity analysis, Protein vector
in
Journal of Theoretical Biology
volume
446
pages
10 pages
publisher
Academic Press
external identifiers
  • scopus:85043366133
ISSN
0022-5193
DOI
10.1016/j.jtbi.2018.03.001
language
English
LU publication?
yes
id
ba8cb81d-51a0-4dd8-b18f-b85a1ea3cc06
date added to LUP
2018-03-19 14:43:00
date last changed
2019-01-06 13:48:04
@article{ba8cb81d-51a0-4dd8-b18f-b85a1ea3cc06,
  abstract     = {<p>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.</p>},
  author       = {Wu, Chuanyan and Gao, Rui and De Marinis, Yang and Zhang, Yusen},
  issn         = {0022-5193},
  keyword      = {Fluctuation complexity,Functional group,Protein sequence similarity analysis,Protein vector},
  language     = {eng},
  month        = {06},
  pages        = {61--70},
  publisher    = {Academic Press},
  series       = {Journal of Theoretical Biology},
  title        = {A novel model for protein sequence similarity analysis based on spectral radius},
  url          = {http://dx.doi.org/10.1016/j.jtbi.2018.03.001},
  volume       = {446},
  year         = {2018},
}