Prediction of non-classical secreted proteins using informative physicochemical properties

Chiung Hui Hung, Hui Ling Huang, Kai Ti Hsu, Shinn Jang Ho, Shinn-Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The prediction of non-classical secreted proteins is a significant problem for drug discovery and development of disease diagnosis. The characteristic of non-classical secreted proteins is they are leaderless proteins without signal peptides in N-terminal. This characteristic makes the prediction of non-classical proteins more difficult and complicated than the classical secreted proteins. We identify a set of informative physicochemical properties of amino acid indices cooperated with support vector machine (SVM) to find discrimination between secreted and non-secreted proteins and to predict non-classical secreted proteins. When the sequence identity of dataset was reduced to 25%, the prediction accuracy on training dataset is 85% which is much better than the traditional sequence similarity-based BLAST or PSI-BLAST tool. The accuracy of independent test is 82%. The most effective features of prediction revealed the fundamental differences of physicochemical properties between secreted and non-secreted proteins. The interpretable and valuable information could be beneficial for drug discovery or the development of new blood biochemical examinations.

Original languageEnglish
Pages (from-to)263-270
Number of pages8
JournalInterdisciplinary Sciences: Computational Life Sciences
Volume2
Issue number3
DOIs
StatePublished - 1 Sep 2010

Keywords

  • amino acid index
  • non-classical secreted protein
  • SVM prediction

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