Prediction of Membrane Protein Types in a Hybrid Space

Prediction of the types of membrane proteins is of great importance both for genome-wide annotation and for experimental researchers to understand proteins’ functions. We describe a new strategy for the prediction of the types of membrane proteins using the Nearest Neighbor Algorithm. We introduced a bipartite feature space consisting of two kinds of disjoint vectors, proteins’ domain profile and proteins’ physiochemical characters. Jackknife cross validation test shows that a combination of both features greatly improves the prediction accuracy. Furthermore, the contribution of the physiochemical features to the classification of membrane proteins has also been explored using the feature selection method called “mRMR” (Minimum Redundancy, Maximum Relevance) (IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27 (8), 1226−1238). A more compact set of features that are mostly contributive to membrane protein classification are obtained. The analyses highlighted both hydrophobicity and polarity as the most important features. The predictor with 56 most contributive features achieves an acceptable prediction accuracy of 87.02%. Online prediction service is available freely on our Web site http://pcal.biosino.org/TransmembraneProteinClassification.html

Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication-2

<p><b>Copyright information:</b></p><p>Taken from "Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication"</p><p>BMC Genomics 2006;7():74-74.</p><p>Published online 5 Apr 2006</p><p>PMCID:PMC1458327.</p><p>Copyright © 2006 Qian et al; licensee BioMed Central Ltd.</p>n in other strains (red arrow), which is suspected as an annotation error. So ORF finder program was rerun on this region and new_tarBs in Mu50 and N315 were identified. As shown in figure, new_tar

Prediction of Membrane Protein Types in a Hybrid Space

Prediction of the types of membrane proteins is of great importance both for genome-wide annotation and for experimental researchers to understand proteins’ functions. We describe a new strategy for the prediction of the types of membrane proteins using the Nearest Neighbor Algorithm. We introduced a bipartite feature space consisting of two kinds of disjoint vectors, proteins’ domain profile and proteins’ physiochemical characters. Jackknife cross validation test shows that a combination of both features greatly improves the prediction accuracy. Furthermore, the contribution of the physiochemical features to the classification of membrane proteins has also been explored using the feature selection method called “mRMR” (Minimum Redundancy, Maximum Relevance) (IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27 (8), 1226−1238). A more compact set of features that are mostly contributive to membrane protein classification are obtained. The analyses highlighted both hydrophobicity and polarity as the most important features. The predictor with 56 most contributive features achieves an acceptable prediction accuracy of 87.02%. Online prediction service is available freely on our Web site http://pcal.biosino.org/TransmembraneProteinClassification.html

Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication-0

<p><b>Copyright information:</b></p><p>Taken from "Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication"</p><p>BMC Genomics 2006;7():74-74.</p><p>Published online 5 Apr 2006</p><p>PMCID:PMC1458327.</p><p>Copyright © 2006 Qian et al; licensee BioMed Central Ltd.</p>AST hits (statistically significant) were used as input. MEGA3 program was used to perform this analysis. NJ trees were constructed using PC (Poisson Correlation) distance and a bootstrap value of 500. After deleting distant branches (homologs but not orthologs), final tree generated. Figure 1a depicts Tar/TagA orthologs. Figure 1b depicts Tar/TagD orthologs. Figure 1c depicts double TarI orthologs in each strain. Figure 1d depicts double TarJ orthologs in each strain. Figure 1e depicts orthologs of Tar/TagB, Tar/TagF, TarL and once again, two orthologs of TarL in each strain are found. However, there is no ortholog of TarK

Prediction of Membrane Protein Types in a Hybrid Space

Prediction of the types of membrane proteins is of great importance both for genome-wide annotation and for experimental researchers to understand proteins’ functions. We describe a new strategy for the prediction of the types of membrane proteins using the Nearest Neighbor Algorithm. We introduced a bipartite feature space consisting of two kinds of disjoint vectors, proteins’ domain profile and proteins’ physiochemical characters. Jackknife cross validation test shows that a combination of both features greatly improves the prediction accuracy. Furthermore, the contribution of the physiochemical features to the classification of membrane proteins has also been explored using the feature selection method called “mRMR” (Minimum Redundancy, Maximum Relevance) (IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27 (8), 1226−1238). A more compact set of features that are mostly contributive to membrane protein classification are obtained. The analyses highlighted both hydrophobicity and polarity as the most important features. The predictor with 56 most contributive features achieves an acceptable prediction accuracy of 87.02%. Online prediction service is available freely on our Web site http://pcal.biosino.org/TransmembraneProteinClassification.html

Prediction of Membrane Protein Types in a Hybrid Space

Prediction of the types of membrane proteins is of great importance both for genome-wide annotation and for experimental researchers to understand proteins’ functions. We describe a new strategy for the prediction of the types of membrane proteins using the Nearest Neighbor Algorithm. We introduced a bipartite feature space consisting of two kinds of disjoint vectors, proteins’ domain profile and proteins’ physiochemical characters. Jackknife cross validation test shows that a combination of both features greatly improves the prediction accuracy. Furthermore, the contribution of the physiochemical features to the classification of membrane proteins has also been explored using the feature selection method called “mRMR” (Minimum Redundancy, Maximum Relevance) (IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27 (8), 1226−1238). A more compact set of features that are mostly contributive to membrane protein classification are obtained. The analyses highlighted both hydrophobicity and polarity as the most important features. The predictor with 56 most contributive features achieves an acceptable prediction accuracy of 87.02%. Online prediction service is available freely on our Web site http://pcal.biosino.org/TransmembraneProteinClassification.html

Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication-5

<p><b>Copyright information:</b></p><p>Taken from "Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication"</p><p>BMC Genomics 2006;7():74-74.</p><p>Published online 5 Apr 2006</p><p>PMCID:PMC1458327.</p><p>Copyright © 2006 Qian et al; licensee BioMed Central Ltd.</p>TarL as a one step (bottom blocks)

Prediction of Membrane Protein Types in a Hybrid Space

Prediction of the types of membrane proteins is of great importance both for genome-wide annotation and for experimental researchers to understand proteins’ functions. We describe a new strategy for the prediction of the types of membrane proteins using the Nearest Neighbor Algorithm. We introduced a bipartite feature space consisting of two kinds of disjoint vectors, proteins’ domain profile and proteins’ physiochemical characters. Jackknife cross validation test shows that a combination of both features greatly improves the prediction accuracy. Furthermore, the contribution of the physiochemical features to the classification of membrane proteins has also been explored using the feature selection method called “mRMR” (Minimum Redundancy, Maximum Relevance) (IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27 (8), 1226−1238). A more compact set of features that are mostly contributive to membrane protein classification are obtained. The analyses highlighted both hydrophobicity and polarity as the most important features. The predictor with 56 most contributive features achieves an acceptable prediction accuracy of 87.02%. Online prediction service is available freely on our Web site http://pcal.biosino.org/TransmembraneProteinClassification.html

Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication-4

<p><b>Copyright information:</b></p><p>Taken from "Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication"</p><p>BMC Genomics 2006;7():74-74.</p><p>Published online 5 Apr 2006</p><p>PMCID:PMC1458327.</p><p>Copyright © 2006 Qian et al; licensee BioMed Central Ltd.</p>s clearly shown. The high homology also indicates this duplication should not be a remote event. And part of region is less conserved, which indicate that two copies could have different functions. This phenomenon can be used to explain why there is no homologs of in these strains. Other strains give similar results and are not shown here

Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication-3

<p><b>Copyright information:</b></p><p>Taken from "Genomic characterization of ribitol teichoic acid synthesis in : genes, genomic organization and gene duplication"</p><p>BMC Genomics 2006;7():74-74.</p><p>Published online 5 Apr 2006</p><p>PMCID:PMC1458327.</p><p>Copyright © 2006 Qian et al; licensee BioMed Central Ltd.</p>ince they have similar genomic organization (See for complete figure). The top line of this figure shows the divergon organizations of W23 and 168 as a reference. The following two lines represented in arrows are graphic demonstration of genomic organization of the selected two strains. Arrows in different colours represent different genes as illustrated by the colour table at the bottom. The length of each arrow is defined accurately by the scale, demonstrating the exact amino acid length of each gene. All six strains share a similar genomic organization, which is quite different from W23 and 168 counterparts. GenBank gi numbers are indicated