Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition.

Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China.
Journal of Theoretical Biology (Impact Factor: 2.35). 03/2012; 304:88-95. DOI: 10.1016/j.jtbi.2012.03.017
Source: PubMed

ABSTRACT Mycobacterium tuberculosis (MTB) is a pathogenic bacterial species in the genus Mycobacterium and the causative agent of most cases of tuberculosis (Berman et al., 2000). Knowledge of the localization of Mycobacterial protein may help unravel the normal function of this protein. Automated prediction of Mycobacterial protein subcellular localization is an important tool for genome annotation and drug discovery. In this work, a benchmark data set with 638 non-redundant mycobacterial proteins is constructed and an approach for predicting Mycobacterium subcellular localization is proposed by combining amino acid composition, dipeptide composition, reduced physicochemical property, evolutionary information, pseudo-average chemical shift. The overall prediction accuracy is 87.77% for Mycobacterial subcellular localizations and 85.03% for three membrane protein types in Integral membranes using the algorithm of increment of diversity combined with support vector machine. The performance of pseudo-average chemical shift is excellent. In order to check the performance of our method, the data set constructed by Rashid was also predicted and the accuracy of 98.12% was obtained. This indicates that our approach was better than other existing methods in literature.

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    ABSTRACT: The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.
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    ABSTRACT: Protein subcellular localization is defined as predicting the functioning location of a given protein in the cell. It is considered an important step towards protein function prediction and drug design. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve protein subcellular localization prediction performance. However, relying solely on GO, this problem remains unsolved. At the same time, the impact of other sources of features especially evolutionary-based features has not been explored adequately for this task. In this study, we aim to extract discriminative evolutionary features to tackle this problem. To do this, we propose two segmentation based feature extraction methods to explore potential local evolutionary-based information for Gram-positive and Gram-negative subcellular localizations. We will show that by applying a Support Vector Machine (SVM) classifier to our extracted features, we are able to enhance Gram-positive and Gram-negative subcellular localization prediction accuracies by up to 6.4% better than previous studies including the studies that used GO for feature extraction.
    Journal of Theoretical Biology 09/2014; · 2.35 Impact Factor
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    ABSTRACT: Locating proteins within cellular contexts is of paramount significance in elucidating their biological functions. Computational methods based on knowledge databases (such as gene ontology annotation (GOA) database) are known to be more efficient than sequence-based methods. However, the predominant scenarios of knowledge-based methods are that (1) knowledge databases typically have enormous size and are growing exponentially, (2) knowledge databases contain redundant information, and (3) the number of extracted features from knowledge databases is much larger than the number of data samples with ground-truth labels. These properties render the extracted features liable to redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address these problems, this paper proposes an efficient multi-label predictor, namely R3P-Loc, which uses two compact databases for feature extraction and applies random projection (RP) to reduce the feature dimensions of an ensemble ridge regression (RR) classifier. Two new compact databases are created from Swiss-Prot and GOA databases. These databases possess almost the same amount of information as their full-size counterparts but with much smaller size. Experimental results on two recent datasets (eukaryote and plant) suggest that R3P-Loc can reduce the dimensions by seven folds and significantly outperforms state-of-the-art predictors. This paper also demonstrates that the compact databases reduce the memory consumption by 39 times without causing degradation in prediction accuracy. For readers' convenience, the R3P-Loc server is available online at url:
    Journal of Theoretical Biology 07/2014; · 2.35 Impact Factor


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Jun 2, 2014