Gellar proteins fail to be detected and many detected proteins can

Gellar proteins fail to be detected and many detected proteins can not be assigned to flagellum with certainty. In this study, we developed a computational method TFPP to identify flagellar proteins in T. brucei based on sequence-derived features. We collected a set of flagellar and non-flagellar proteins that have been annotated with high confidence, and selected a number of discriminating properties from various sequence and structural features using a feature Madecassoside manufacturer selection procedure. On the basis of these features, we developed a support vector machine (SVM)-based classifier to predict flagellar proteins in T. brucei. Our results indicate that our method performs well in identifying flagellar proteins and would help to uncover the flagellar proteome in T. brucei. We compared the expression profiles of the T. brucei proteome at three important life cycle stages, and found that the expression of ,45 of the expressed flagellar proteins changes greatly during life cycle, indicating life cycle stage-specific regulation of flagellar functions in T. brucei which is consistent with previous studies [3].and disordered regions [23]; (d) signal peptide [24] and transmembrane topology [25,26]; (e) post-translational modifications such as phosphorylation [27], acetylation [28] and palmitoylation [29]. Amino acid Hesperidin chemical information composition reflects the fraction of amino acids in a protein sequence, while di-peptide composition also encapsulates information about the local order of amino acids in a protein sequence. AAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids, currently containing 24272870 544 amino acid indices derived from published literature. 544 properties were obtained for each protein by calculating the average value of each amino acid index across the whole protein sequence. The details of the initial features and the computer programs used to calculate them are listed in Table S2. Note that some of these features are represented by multiple feature elements. For example, the amino acid composition of a protein sequence is represented by 20 feature elements. In total, 21 features are considered in our initial feature list, which are represented using 1000 feature elements (Table S2).Feature selection and classificationSupport vector machine (SVM) is a very useful machine learning method, which has been widely used to solve biological problems such as protein-protein interaction prediction [30], protein subcellular localization prediction [9], post-translational modification recognition [31], biomarker identification in cancer research [32], etc. In this study, SVM with the popular non-linear Gaussian Radial Basis Function kernel (RBF) was used to build the classifier for distinguishing flagellar proteins from non-flagellar proteins. The SVM software we used is LIBSVM (http://www. csie.ntu.edu.tw/,cjlin/libsvm/) which is currently one of the most widely used SVM software. A grid search-based method was used to automatically optimize the two parameters C and c in the training procedure of each SVM classifier, nd the search spaces ???for C and c are 215 ,2{5 and 2{5 ,2{15 with steps being 2{1 and 2, respectively. Codes for parameter selection are publicly available from LIBSVM package. It is widely appreciated that feature selection in classification is very important not only for reducing running time but also for improving performance and mining useful feature elements which are really relev.Gellar proteins fail to be detected and many detected proteins can not be assigned to flagellum with certainty. In this study, we developed a computational method TFPP to identify flagellar proteins in T. brucei based on sequence-derived features. We collected a set of flagellar and non-flagellar proteins that have been annotated with high confidence, and selected a number of discriminating properties from various sequence and structural features using a feature selection procedure. On the basis of these features, we developed a support vector machine (SVM)-based classifier to predict flagellar proteins in T. brucei. Our results indicate that our method performs well in identifying flagellar proteins and would help to uncover the flagellar proteome in T. brucei. We compared the expression profiles of the T. brucei proteome at three important life cycle stages, and found that the expression of ,45 of the expressed flagellar proteins changes greatly during life cycle, indicating life cycle stage-specific regulation of flagellar functions in T. brucei which is consistent with previous studies [3].and disordered regions [23]; (d) signal peptide [24] and transmembrane topology [25,26]; (e) post-translational modifications such as phosphorylation [27], acetylation [28] and palmitoylation [29]. Amino acid composition reflects the fraction of amino acids in a protein sequence, while di-peptide composition also encapsulates information about the local order of amino acids in a protein sequence. AAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids, currently containing 24272870 544 amino acid indices derived from published literature. 544 properties were obtained for each protein by calculating the average value of each amino acid index across the whole protein sequence. The details of the initial features and the computer programs used to calculate them are listed in Table S2. Note that some of these features are represented by multiple feature elements. For example, the amino acid composition of a protein sequence is represented by 20 feature elements. In total, 21 features are considered in our initial feature list, which are represented using 1000 feature elements (Table S2).Feature selection and classificationSupport vector machine (SVM) is a very useful machine learning method, which has been widely used to solve biological problems such as protein-protein interaction prediction [30], protein subcellular localization prediction [9], post-translational modification recognition [31], biomarker identification in cancer research [32], etc. In this study, SVM with the popular non-linear Gaussian Radial Basis Function kernel (RBF) was used to build the classifier for distinguishing flagellar proteins from non-flagellar proteins. The SVM software we used is LIBSVM (http://www. csie.ntu.edu.tw/,cjlin/libsvm/) which is currently one of the most widely used SVM software. A grid search-based method was used to automatically optimize the two parameters C and c in the training procedure of each SVM classifier, nd the search spaces ???for C and c are 215 ,2{5 and 2{5 ,2{15 with steps being 2{1 and 2, respectively. Codes for parameter selection are publicly available from LIBSVM package. It is widely appreciated that feature selection in classification is very important not only for reducing running time but also for improving performance and mining useful feature elements which are really relev.