A complexity-based method for predicting protein subcellular location

ML-rRBF-ECOC: A Multi-Label Learning Classifier for Predicting Protein Subcellular Localization with Both Single and Multiple Sites

Background:

The subcellular localization of a protein is closely related with its functions and interactions. More and more evidences show that proteins may simultaneously exist at, or move between, two or more different subcellular localizations. Therefore, predicting protein subcellular localization is an important but challenging problem.

Observation:

Most of the existing methods for predicting protein subcellular localization assume that a protein locates at a single site. Although a few methods have been proposed to deal with proteins with multiple sites, correlations between subcellular localization are not efficiently taken into account. In this paper, we propose an integrated method for predicting protein subcellular localizations with both single site and multiple sites.

Methods:

Firstly, we extend the Multi-Label Radial Basis Function (ML-RBF) method to the regularized version, and augment the first layer of ML-RBF to take local correlations between subcellular localization into account. Secondly, we embed the modified ML-RBF into a multi-label Error-Correcting Output Codes (ECOC) method in order to further consider the subcellular localization dependency. We name our method ML-rRBF-ECOC. Finally, the performance of ML-rRBF-ECOC is evaluated on three benchmark datasets.

Results:

The results demonstrate that ML-rRBF-ECOC has highly competitive performance to the related multi-label learning method and some state-of-the-art methods for predicting protein subcellular localizations with multiple sites. Considering dependency between subcellular localizations can contribute to the improvement of prediction performance.

Conclusion:

This also indicates that correlations between different subcellular localizations really exist. Our method at least plays a complementary role to existing methods for predicting protein subcellular localizations with multiple sites.

Position-specific analysis and prediction of protein pupylation sites based on multiple features

Pupylation is one of the most important posttranslational modifications of proteins; accurate identification of pupylation sites will facilitate the understanding of the molecular mechanism of pupylation. Besides the conventional experimental approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, we developed a novel predictor to predict the pupylation sites. First, the maximum relevance minimum redundancy (mRMR) and incremental feature selection methods were made on five kinds of features to select the optimal feature set. Then the prediction model was built based on the optimal feature set with the assistant of the support vector machine algorithm. As a result, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was 0.764, and the Mathews correlation coefficient was 0.522, indicating a good prediction. Feature analysis showed that all features types contributed to the prediction of protein pupylation sites. Further site-specific features analysis revealed that the features of sites surrounding the central lysine contributed more to the determination of pupylation sites than the other sites.

An ensemble method for predicting subnuclear localizations from primary protein structures

Background

Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods.

Methodology/Principal Findings

A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis.

Conclusions

It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method. It is freely available at http://bioinformatics.awowshop.com/snlpred_page.php.

An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity

With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.

Prediction of lysine ubiquitylation with ensemble classifier and feature selection

Ubiquitylation is an important process of post-translational modification. Correct identification of protein lysine ubiquitylation sites is of fundamental importance to understand the molecular mechanism of lysine ubiquitylation in biological systems. This paper develops a novel computational method to effectively identify the lysine ubiquitylation sites based on the ensemble approach. In the proposed method, 468 ubiquitylation sites from 323 proteins retrieved from the Swiss-Prot database were encoded into feature vectors by using four kinds of protein sequences information. An effective feature selection method was then applied to extract informative feature subsets. After different feature subsets were obtained by setting different starting points in the search procedure, they were used to train multiple random forests classifiers and then aggregated into a consensus classifier by majority voting. Evaluated by jackknife tests and independent tests respectively, the accuracy of the proposed predictor reached 76.82% for the training dataset and 79.16% for the test dataset, indicating that this predictor is a useful tool to predict lysine ubiquitylation sites. Furthermore, site-specific feature analysis was performed and it was shown that ubiquitylation is intimately correlated with the features of its surrounding sites in addition to features derived from the lysine site itself. The feature selection method is available upon request.

EBP50 exerts tumor suppressor activity by promoting cell apoptosis and retarding extracellular signal-regulated kinase activity

The expression of Ezrin-radixin-moesin-binding phosphoprotein-50 (EBP50) and the intragenic mutation of the ebp50 gene have been reported to correlate with human breast cancer development, but the exact impacts on breast cancer development and its molecular mechanism are not fully understood. In this study, we investigate the potential function of EBP50 through over-expression in the breast cancer cell line, MDA-MB-231, which has low EBP50 protein expression levels. The effects of EBP50 over-expression on cellular proliferation, anchorage-independent growth and apoptosis were examined. In addition, the activity of extracellular signal-regulated kinase (ERK) was also determined. Our results show that a decrease of cellular proliferation and attenuation of colony-forming ability were evident in MDA-MB-231 cells stably transfected with an EBP50 expressing plasmid (EBP-231) when compared with control cells. There was also a statistically significant increase in spontaneous apoptosis in EBP-231 cells accompanied by an attenuation in ERK activity. Altogether, our results suggest that restoring EBP50 expression could suppress breast cancer cell proliferation by promoting cell apoptosis and inhibiting ERK activity, and that EBP50 may be a target for development of diagnostics and therapeutics in breast cancer.