Prediction of the Disordered Regions of Intrinsically Disordered Proteins Based on the Molecular Functions.

BACKGROUND: Intrinsically disordered proteins lack a well-defined three dimensional structure under physiological conditions while possessing the essential biological functions. They take part in various physiological processes such as signal transduction, transcription and posttranslational modifications and etc. The disordered regions are the main functional sites for intrinsically disordered proteins. Therefore, the research of the disordered regions has become a hot issue. OBJECTIVE: In this paper, our motivation is to analysis of the features of disordered regions with different molecular functions and predict of different disordered regions using valid features. METHODS: In this article, according to the different molecular function, we firstly divided intrinsically disordered proteins into six classes in DisProt database. Then, we extracted four features using bioinformatics methods, namely, Amino Acid Index (AAIndex), codon frequency (Codon), three kinds of protein secondary structure compositions (3PSS) and Chemical Shifts (CSs), and used these features to predict the disordered regions of the different functions by Support Vector Machine (SVM). RESULTS: The best overall accuracy was 99.29% using the chemical shift (CSs) as feature. In feature fusion, the overall accuracy can reach 88.70% by using CSs+AAIndex as features. The overall accuracy was up to 86.09% by using CSs+AAIndex+Codon+3PSS as features. CONCLUSION: We predicted and analyzed the disordered regions based on the molecular functions. The results showed that the prediction performance can be improved by adding chemical shifts and AAIndex as features, especially chemical shifts. Moreover, the chemical shift was the most effective feature in the prediction. We hoped that our results will be constructive for the study of intrinsically disordered proteins.

Identify Secretory Protein of Malaria Parasite with Modified Quadratic Discriminant Algorithm and Amino Acid Composition.

Malaria parasite secretes various proteins in infected red blood cell for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine or drug against malaria. In this study, the modified method of quadratic discriminant analysis is presented for predicting the secretory proteins. Firstly, 20 amino acids are divided into five types according to the physical and chemical characteristics of amino acids. Then, we used five types of amino acids compositions as inputs of the modified quadratic discriminant algorithm. Finally, the best prediction performance is obtained by using 20 amino acid compositions, the sensitivity of 96 %, the specificity of 92 % with 0.88 of Mathew's correlation coefficient in fivefold cross-validation test. The results are also compared with those of existing prediction methods. The compared results shown our method are prominent in the prediction of secretory proteins.