Prediction of extracellular matrix proteins based on distinctive sequence and domain characteristics

Juhyun Jung, Tae Woo Ryu, Yongdeuk Hwang, Eunjung Lee, Doheon Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Extracellular matrix (ECM) proteins are secreted to the exterior of the cell, and function as mediators between resident cells and the external environment. These proteins not only support cellular structure but also participate in diverse processes, including growth, hormonal response, homeostasis, and disease progression. Despite their importance, current knowledge of the number and functions of ECM proteins is limited. Here, we propose a computational method to predict ECM proteins. Specific features, such as ECM domain score and repetitive residues, were utilized for prediction. Based on previously employed and newly generated features, discriminatory characteristics for ECM protein categorization were determined, which significantly improved the performance of Random Forest and support vector machine (SVM) classification. We additionally predicted novel ECM proteins from non-annotated human proteins, validated with gene ontology and earlier literature. Our novel prediction method is available at biosoft.kaist.ac.kr/ecm.

Original languageEnglish (US)
Pages (from-to)97-105
Number of pages9
JournalJournal of Computational Biology
Volume17
Issue number1
DOIs
StatePublished - Jan 1 2010

Keywords

  • ECM
  • Extracellular matrix proteins
  • Protein localization
  • Random Forest
  • Support vector machine

ASJC Scopus subject areas

  • Modeling and Simulation
  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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