Novel Algorithm for Improved Protein Classification Using Graph Similarity

Hsin Hung Chou, Ching Tien Hsu, Chin Wei Hsu, Kai Hsun Yao, Hao Ching Wang, Sun Yuan Hsieh

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Considerable sequence data are produced in genome annotation projects that relate to molecular levels, structural similarities, and molecular and biological functions. In structural genomics, the most essential task involves resolving protein structures efficiently with hardware or software, understanding these structures, and assigning their biological functions. Understanding the characteristics and functions of proteins enables the exploration of the molecular mechanisms of life. In this paper, we examine the problems of protein classification. Because they perform similar biological functions, proteins in the same family usually share similar structural characteristics. We employed this premise in designing a classification algorithm. In this algorithm, auxiliary graphs are used to represent proteins, with every amino acid in a protein to a vertex in a graph. Moreover, the links between amino acids correspond to the edges between the vertices. The proposed algorithm classifies proteins according to the similarities in their graphical structures. The proposed algorithm is efficient and accurate in distinguishing proteins from different families and outperformed related algorithms experimentally.

Original languageEnglish
Pages (from-to)3135-3143
Number of pages9
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
Publication statusPublished - Nov 2022


  • Amino acids
  • B-factors
  • bioinformatic algorithms
  • Biology
  • Computer science
  • protein classification
  • protein structures
  • Proteins
  • Roads
  • structural similarities
  • Support vector machines
  • Three-dimensional displays

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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