Application of wavelet transform for PDZ domain classification

Khaled Daqrouq, Rami Alhmouz, Ahmed Balamesh, Adnan Memic

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


PDZ domains have been identified as part of an array of signaling proteins that are often unrelated, except for the well-conserved structural PDZ domain they contain. These domains have been linked to many disease processes including common Avian influenza, as well as very rare conditions such as Fraser and Usher syndromes. Historically, based on the interactions and the nature of bonds they form, PDZ domains have most often been classified into one of three classes (class I, class II and others - class III), that is directly dependent on their binding partner. In this study, we report on three unique feature extraction approaches based on the bigram and trigram occurrence and existence rearrangements within the domain's primary amino acid sequences in assisting PDZ domain classification. Wavelet packet transform (WPT) and Shannon entropy denoted by wavelet entropy (WE) feature extraction methods were proposed. Using 115 unique human and mouse PDZ domains, the existence rearrangement approach yielded a high recognition rate (78.34%), which outperformed our occurrence rearrangements based method. The recognition rate was (81.41%) with validation technique. The method reported for PDZ domain classification from primary sequences proved to be an encouraging approach for obtaining consistent classification results. We anticipate that by increasing the database size, we can further improve feature extraction and correct classification.

Original languageEnglish
Article numbere0122873
JournalPLoS One
Issue number4
Publication statusPublished - Apr 10 2015

ASJC Scopus subject areas

  • General Biochemistry,Genetics and Molecular Biology
  • General Agricultural and Biological Sciences
  • General


Dive into the research topics of 'Application of wavelet transform for PDZ domain classification'. Together they form a unique fingerprint.

Cite this