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Using the Concept of Chous Pseudo Amino Acid Composition to Predict Apoptosis Proteins Subcellular Location: An Approach by Approximate Entropy

[ Vol. 15 , Issue. 4 ]


Xiaoying Jian, Rong Wei, Tongliang Zhan and Quan Gu   Pages 392 - 396 ( 5 )


The function of protein is closely correlated with it subcellular location. Prediction of subcellular location of apoptosis proteins is an important research area in post-genetic era because the knowledge of apoptosis proteins is useful to understand the mechanism of programmed cell death. Compared with the conventional amino acid composition (AAC), the Pseudo Amino Acid composition (PseAA) as originally introduced by Chou can incorporate much more information of a protein sequence so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, a novel approach is presented to predict apoptosis protein solely from sequence based on the concept of Chous PseAA composition. The concept of approximate entropy (ApEn), which is a parameter denoting complexity of time series, is used to construct PseAA composition as additional features. Fuzzy K-nearest neighbor (FKNN) classifier is selected as prediction engine. Particle swarm optimization (PSO) algorithm is adopted for optimizing the weight factors which are important in PseAA composition. Two datasets are used to validate the performance of the proposed approach, which incorporate six subcellular location and four subcellular locations, respectively. The results obtained by jackknife test are quite encouraging. It indicates that the ApEn of protein sequence could represent effectively the information of apoptosis proteins subcellular locations. It can at least play a complimentary role to many of the existing methods, and might become potentially useful tool for protein function prediction. The software in Matlab is available freely by contacting the corresponding author.


Apoptosis protein subcellular localization, pseudo amino acid composition, approximate entropy, fuzzy K nearest neighbors classifier


College of Information Sciences and Technology Donghua University Shanghai 201620, P.R. China.

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