Predicting Protein Subcellular Location Using Chous Pseudo Amino Acid Composition and Improved Hybrid Approach

Title: Predicting Protein Subcellular Location Using Chous Pseudo Amino Acid Composition and Improved Hybrid Approach

Volume: 15 Issue: 6

Author(s): Feng-Min Li and Qian-Zhong Li

Affiliation:

Keywords: Subcellular location, increment of diversity, support vector machine, Chou's pseudo amino acid composition

Abstract: The location of a protein in a cell is closely correlated with its biological function. Based on the concept that the protein subcellular location is mainly determined by its amino acid and pseudo amino acid composition (PseAA), a new algorithm of increment of diversity combined with support vector machine is proposed to predict the protein subcellular location. The subcellular locations of plant and non-plant proteins are investigated by our method. The overall prediction accuracies in jackknife test are 88.3% for the eukaryotic plant proteins and 92.4% for the eukaryotic non-plant proteins, respectively. In order to estimate the effect of the sequence identity on predictive result, the proteins with sequence identity 40% are selected. The overall success rates of prediction are 86.2% and 92.3% for plant and non-plant proteins in jackknife test, respectively.

Cite this article as:

Li Feng-Min and Li Qian-Zhong, Predicting Protein Subcellular Location Using Chous Pseudo Amino Acid Composition and Improved Hybrid Approach, Protein & Peptide Letters 2008; 15 (6) . https://dx.doi.org/10.2174/092986608784966930