Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Yang

Yang de Marinis

Associate professor

Yang

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

Author

  • Chuanyan Wu
  • Rui Gao
  • Yang De Marinis
  • Yusen Zhang

Summary, in English

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.

Department/s

  • Genomics, Diabetes and Endocrinology
  • EXODIAB: Excellence in Diabetes Research in Sweden

Publishing year

2018-06-07

Language

English

Pages

61-70

Publication/Series

Journal of Theoretical Biology

Volume

446

Document type

Journal article

Publisher

Academic Press

Topic

  • Bioinformatics and Systems Biology
  • Biochemistry and Molecular Biology

Keywords

  • Fluctuation complexity
  • Functional group
  • Protein sequence similarity analysis
  • Protein vector

Status

Published

Research group

  • Genomics, Diabetes and Endocrinology

ISBN/ISSN/Other

  • ISSN: 0022-5193