The extracellular matrix (ECM) is a major component of tissues of multicellular organisms. It consists of secreted macromolecules, mainly polysaccharides and glycoproteins. Malfunctions of ECM proteins lead to severe disorders such as marfan syndrome, osteogenesis imperfecta, numerous chondrodysplasias, and skin diseases. In this work, we report a random forest approach, EcmPred, for the prediction of ECM proteins from protein sequences. EcmPred was trained on a dataset containing 300 ECM and 300 non-ECM and tested on a dataset containing 145 ECM and 4187 non-ECM proteins. EcmPred achieved 83% accuracy on the training and 77% on the test dataset. EcmPred predicted 15 out of 20 experimentally verified ECM proteins. By scanning the entire human proteome, we predicted novel ECM proteins validated with gene ontology and InterPro. The dataset and standalone version of the EcmPred software is available at http://www.inb.uni-luebeck.de/tools-demos/Extracellular_matrix_proteins/EcmPred. © 2012 Elsevier Ltd.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by Germany's Excellence Initiative [DFG GSC 235/1]. KKK acknowledges Dr. Bianca Habermann, Max Planck Institute for Biology of Ageing, Germany for her support.
ASJC Scopus subject areas
- General Agricultural and Biological Sciences
- General Biochemistry, Genetics and Molecular Biology
- Modeling and Simulation
- Applied Mathematics
- Statistics and Probability
- General Immunology and Microbiology
- General Medicine