Using Chou’s pseudo amino acid composition based on approximate entropy and an ensemble of AdaBoost classifiers to predict protein subnuclear location

Abstract

The knowledge of subnuclear localization in eukaryotic cells is essential for understanding the life function of nucleus. Developing prediction methods and tools for proteins subnuclear localization become important research fields in protein science for special characteristics in cell nuclear. In this study, a novel approach has been proposed to predict protein subnuclear localization. Sample of protein is represented by Pseudo Amino Acid (PseAA) composition based on approximate entropy (ApEn) concept, which reflects the complexity of time series. A novel ensemble classifier is designed incorporating three AdaBoost classifiers. The base classifier algorithms in three AdaBoost are decision stumps, fuzzy K nearest neighbors classifier, and radial basis-support vector machines, respectively. Different PseAA compositions are used as input data of different AdaBoost classifier in ensemble. Genetic algorithm is used to optimize the dimension and weight factor of PseAA composition. Two datasets often used in published works are used to validate the performance of the proposed approach. The obtained results of Jackknife cross-validation test are higher and more balance than them of other methods on same datasets. The promising results indicate that the proposed approach is effective and practical. It might become a useful tool in protein subnuclear localization. The software in Matlab and supplementary materials are available freely by contacting the corresponding author.

Predicting protein folding rates using the concept of Chou's pseudo amino acid composition

“…After these three screening procedures, we obtained a dataset of 99 proteins. The Protein Data Bank codes and the experimental folding rate values ln(k f ) are listed in Supporting Information 37 Ever since the concept of PseAAC was introduced, it has been widely used to study various problems in proteins and protein-related systems, such as predicting subcellular location of proteins, [38][39][40][41][42] subnuclear location of proteins, 43,44 structural classes of proteins, 45 submitochondria localization, 46 protein quaternary structure, 47 submitochondria locations, 60 among many other protein attributes and protein related features. However, so far no report whatsoever has been seen that the PseAAC was used for predicting the folding rates of proteins.…”

Predicting Protein Subcellular Localization Using the Algorithm of Increment Of Diversity Combined with Weighted K-Nearest Neighbor

“…The comparison of the results is shown in table 3. We compared our method for SNL9 with OET-KNN [13], PSSM [14], AdaBoost [15]. The comparison of the results is shown in table 4.…”

Recent progress in predicting protein sub-subcellular locations

“…Mei and Fei introduced their method for predicting protein subnuclear location by using an improved version of k-spectrum kernel and the amino acid category strategy [98]. Jiang et al proposed the AdaBoost fusion method to create an ensemble classifier based on three different machine learning algorithms, including decision stump, fuzzy K-NN and SVM [47]. Li and Li introduced the increment of diversity quadratic discriminant (IDQD) method to analyze the protein sequence with the hydropathy category of the amino acid composition [99].…”