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| Content Provider | PubMed Central |
|---|---|
| Author | Zhang, Lina Zhang, Chengjin Gao, Rui Yang, Runtao |
| Editor | Christov, Christo Z. |
| Copyright Year | 2015 |
| 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 scientists, a user-friendly and publicly-accessible web-server for the proposed ensemble method is established. |
| Related Links | http://dx.doi.org/10.3390/ijms160921734 |
| Ending Page | 21758 |
| Page Count | 25 |
| Starting Page | 21734 |
| File Format | |
| ISSN | 14220067 |
| e-ISSN | 14220067 |
| Journal | International Journal of Molecular Sciences |
| Issue Number | 9 |
| Volume Number | 16 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2015-09-01 |
| Access Restriction | Open |
| Rights Holder | MDPI |
| Subject Keyword | Physical and Theoretical Chemistry Inorganic Chemistry Organic Chemistry Spectroscopy Molecular Biology Catalysis Computer Science Applications Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Spectroscopy Organic Chemistry Medicine Molecular Biology Physical and Theoretical Chemistry Catalysis Inorganic Chemistry Computer Science Applications |
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