Prediction of non-classical secreted proteins using informative physicochemical properties

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.

DOI: 10.1007/s12539-010-0023-z

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Cite this paper

@article{Hung2010PredictionON, title={Prediction of non-classical secreted proteins using informative physicochemical properties}, author={Chiung-Hui Hung and Hui-Ling Huang and Kai-Ti Hsu and Shinn-Jang Ho and Shinn-Ying Ho}, journal={Interdisciplinary Sciences: Computational Life Sciences}, year={2010}, volume={2}, pages={263-270} }