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Bioluminescent proteins are highly sensitive optical reporters for imaging in live animals; they have been extensively used in analytical applications in intracellular monitoring, genetic regulation and detection, and immune and binding assays. In this work, we systematically analyzed the sequence and structure information of 199 bioluminescent and nonbioluminescent proteins, respectively. Based on the results, we presented a novel method called auto covariance of averaged chemical shift (acACS) for extracting structure features from a sequence. A classifier of support vector machine (SVM) fusing increment of diversity (ID) was used to distinguish bioluminescent proteins from nonbioluminescent proteins by combining dipeptide composition, reduced amino acid composition, evolutionary information, and acACS. The overall prediction accuracy evaluated by jackknife validation reached 82.16%. This result was better than that obtained by other existing methods. Improvement of the overall prediction accuracy reached up to 5.33% higher than those of the SVM and auto covariance of sequential evolution information by 10-fold cross-validation. The acACS algorithm also outperformed other feature extraction methods, indicating that our approach is better than other existing methods in the literature.

RNA-protein interactions play important roles in various biological processes. The precise detection of RNA-protein interaction sites is very important for understanding essential biological processes and annotating the function of the proteins. In this study, based on various features from amino acid sequence and structure, including evolutionary information, solvent accessible surface area and torsion angles (φ, ψ) in the backbone structure of the polypeptide chain, a computational method for predicting RNA-binding sites in proteins is proposed. When the method is applied to predict RNA-binding sites in three datasets: RBP86 containing 86 protein chains, RBP107 containing 107 proteins chains and RBP109 containing 109 proteins chains, better sensitivities and specificities are obtained compared to previously published methods in five-fold cross-validation tests. In order to make further examination for the efficiency of our method, the RBP107 dataset is used as training set, RBP86 and RBP109 datasets are used as the independent test sets. In addition, as examples of our prediction, RNA-binding sites in a few proteins are presented. The annotated results are consistent with the PDB annotation. These results show that our method is useful for annotating RNA binding sites of novel proteins.

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.

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. Based on the concept that the subcellular location of an apoptosis protein is mainly determined by its amino acid sequence, a new algorithm for prediction of the subcellular location of an apoptosis protein is proposed. By using of a distinctive set of information parameters derived from the primary sequence of 317 apoptosis proteins, the increment of diversity (ID), the sole prediction parameter, is calculated. The higher predictive success rates than the previous other algorithms is obtained by the jackknife tests using the expanded dataset. Our prediction results show that the local compositions of twin amino acids and hydropathy distribution are very useful to predict subcellular location of protein.

Based on the conservation analysis of the 683 latest experimentally verified sigma(70)-promoter sequences of Escherichia coli K-12, it is found that the conservative hexamers segments in different sites play a key role of promoter regions, a novel position-correlation scoring matrix (PCSM) algorithm for predicting sigma(70) promoter is presented. The predictive capacity of the algorithm is tested by 10-cross validation test. The results show that the overall prediction accuracies (sensitivity) and specificity are 91% and 81%, respectively. By selecting the 683 experimentally verified sigma(70) promoters as training set and searching for the complete sequence in E. coli K-12 with 4639221bp. Results show that the 100% of the 683 experimentally verified sigma(70) promoters have been identified and some possible promoters are predicted.