Dataset Information

An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

ABSTRACT: Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacteriophage virion protein prediction from bacteriophage protein sequences is put forward with hybrid feature spaces incorporating CTD (composition, transition and distribution), bi-profile Bayes, PseAAC (pseudo-amino acid composition) and PSSM (position-specific scoring matrix). When performing on the training dataset 10-fold cross-validation, the presented method achieves a satisfactory prediction result with a sensitivity of 0.870, a specificity of 0.830, an accuracy of 0.850 and Matthew's correlation coefficient (MCC) of 0.701, respectively. To evaluate the prediction performance objectively, an independent testing dataset is used to evaluate the proposed method. Encouragingly, our proposed method performs better than previous studies with a sensitivity of 0.853, a specificity of 0.815, an accuracy of 0.831 and MCC of 0.662 on the independent testing dataset. These results suggest that the proposed method can be a potential candidate for bacteriophage virion protein prediction, which may provide a useful tool to find novel antibacterial drugs and to understand the relationship between bacteriophage and host bacteria. For the convenience of the vast majority of experimental Int. J. Mol. Sci. 2015, 16,21735 scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established.

SUBMITTER: Zhang L

PROVIDER: S-EPMC4613277 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

Zhang Lina L Zhang Chengjin C Gao Rui R Yang Runtao R

International journal of molecular sciences 20150909 9

Bacteriophage virion proteins and non-virion proteins have distinct functions in biological processes, such as specificity determination for host bacteria, bacteriophage replication and transcription. Accurate identification of bacteriophage virion proteins from bacteriophage protein sequences is significant to understand the complex virulence mechanism in host bacteria and the influence of bacteriophages on the development of antibacterial drugs. In this study, an ensemble method for bacterioph ...[more]

PMID: 26370987

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Project description:The P22 capsid is a T=7 icosahedrally symmetric protein shell with a portal protein dodecamer at one 5-fold vertex. Extending outwards from that vertex is a short tail, and putatively extending inwards is a 15-nm-long α-helical barrel formed by the C-terminal domains of portal protein subunits. In addition to the densely packed genome, the capsid contains three "ejection proteins" (E-proteins [gp7, gp16, and gp20]) destined to exit from the tightly sealed capsid during the process of DNA delivery into target cells. We estimated their copy numbers by quantitative SDS-PAGE as approximately 12 molecules per virion of gp16 and gp7 and 30 copies of gp20. To localize them, we used bubblegram imaging, an adaptation of cryo-electron microscopy in which gaseous bubbles induced in proteins by prolonged irradiation are used to map the proteins' locations. We applied this technique to wild-type P22, a triple mutant lacking all three E-proteins, and three mutants each lacking one E-protein. We conclude that all three E-proteins are loosely clustered around the portal axis, in the region displaced radially inwards from the portal crown. The bubblegram data imply that approximately half of the α-helical barrel seen in the portal crystal structure is disordered in the mature virion, and parts of the disordered region present binding sites for E-proteins. Thus positioned, the E-proteins are strategically placed to pass down the shortened barrel and through the portal ring and the tail, as they exit from the capsid during an infection. While it has long been appreciated that capsids serve as delivery vehicles for viral genomes, there is now growing awareness that viruses also deliver proteins into their host cells. P22 has three such proteins (ejection proteins [E-proteins]), whose initial locations in the virion have remained unknown despite their copious amounts (total of 2.5 MDa). This study succeeded in localizing them by the novel technique of bubblegram imaging. The P22 E-proteins are seen to be distributed around the orifice of the portal barrel. Interestingly, this barrel, 15 nm long in a crystal structure, is only about half as long in situ: the remaining, disordered, portion appears to present binding sites for E-proteins. These observations document a spectacular example of a regulatory order-disorder transition in a supramolecular system and demonstrate the potential of bubblegram imaging to map the components of other viruses as well as cellular complexes.

Project description:BackgroundIn recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed when analyzing this data is distinguishing between long non-coding RNAs (lncRNAs) and protein coding transcripts (PCTs). Thus, we present a Support Vector Machine (SVM) based method to distinguish lncRNAs from PCTs, using features based on frequencies of nucleotide patterns and ORF lengths, in transcripts.MethodsThe proposed method is based on SVM and uses the first ORF relative length and frequencies of nucleotide patterns selected by PCA as features. FASTA files were used as input to calculate all possible features. These features were divided in two sets: (i) 336 frequencies of nucleotide patterns; and (ii) 4 features derived from ORFs. PCA were applied to the first set to identify 6 groups of frequencies that could most contribute to the distinction. Twenty-four experiments using the 6 groups from the first set and the features from the second set where built to create the best model to distinguish lncRNAs from PCTs.ResultsThis method was trained and tested with human (Homo sapiens), mouse (Mus musculus) and zebrafish (Danio rerio) data, achieving 98.21%, 98.03% and 96.09%, accuracy, respectively. Our method was compared to other tools available in the literature (CPAT, CPC, iSeeRNA, lncRNApred, lncRScan-SVM and FEELnc), and showed an improvement in accuracy by ≈3.00%. In addition, to validate our model, the mouse data was classified with the human model, and vice-versa, achieving ≈97.80% accuracy in both cases, showing that the model is not overfit. The SVM models were validated with data from rat (Rattus norvegicus), pig (Sus scrofa) and fruit fly (Drosophila melanogaster), and obtained more than 84.00% accuracy in all these organisms. Our results also showed that 81.2% of human pseudogenes and 91.7% of mouse pseudogenes were classified as non-coding. Moreover, our method was capable of re-annotating two uncharacterized sequences of Swiss-Prot database with high probability of being lncRNAs. Finally, in order to use the method to annotate transcripts derived from RNA-seq, previously identified lncRNAs of human, gorilla (Gorilla gorilla) and rhesus macaque (Macaca mulatta) were analyzed, having successfully classified 98.62%, 80.8% and 91.9%, respectively.ConclusionsThe SVM method proposed in this work presents high performance to distinguish lncRNAs from PCTs, as shown in the results. To build the model, besides using features known in the literature regarding ORFs, we used PCA to identify features among nucleotide pattern frequencies that contribute the most in distinguishing lncRNAs from PCTs, in reference data sets. Interestingly, models created with two evolutionary distant species could distinguish lncRNAs of even more distant species.

Dataset Information

An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

Publications

An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.

Similar Datasets

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