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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Sharma, Ashok K. Kumar, Sanjiv K., Harish Dhakan, Darshan B. Sharma, Vineet K. |
| Abstract | Background The efficacy of antibiotics against bacterial infections is decreasing due to the development of resistance in bacteria, and thus, there is a need to search for potential alternatives to antibiotics. In this scenario, peptidoglycan hydrolases can be used as alternate antibacterial agents due to their unique property of cleaving peptidoglycan cell wall present in both gram-positive and gram-negative bacteria. Along with a role in maintaining overall peptidoglycan turnover in a cell and in daughter cell separation, peptidoglycan hydrolases also play crucial role in bacterial pathophysiology requiring development of a computational tool for the identification and classification of novel peptidoglycan hydrolases from genomic and metagenomic data. Results In this study, the known peptidoglycan hydrolases were divided into multiple classes based on their site of action and were used for the development of a computational tool ‘HyPe’ for identification and classification of novel peptidoglycan hydrolases from genomic and metagenomic data. Various classification models were developed using amino acid and dipeptide composition features by training and optimization of Random Forest and Support Vector Machines. Random Forest multiclass model was selected for the development of HyPe tool as it showed up to 71.12 % sensitivity, 99.98 % specificity, 99.55 % accuracy and 0.80 MCC in four different classes of peptidoglycan hydrolases. The tool was validated on 24 independent genomic datasets and showed up to 100 % sensitivity and 0.94 MCC. The ability of HyPe to identify novel peptidoglycan hydrolases was also demonstrated on 24 metagenomic datasets. Conclusions The present tool helps in the identification and classification of novel peptidoglycan hydrolases from complete genomic or metagenomic ORFs. To our knowledge, this is the only tool available for the prediction of peptidoglycan hydrolases from genomic and metagenomic data. Availability: http://metagenomics.iiserb.ac.in/hype/ and http://metabiosys.iiserb.ac.in/hype/ . |
| Related Links | https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-016-2753-8.pdf |
| Ending Page | 12 |
| Page Count | 12 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 14712164 |
| DOI | 10.1186/s12864-016-2753-8 |
| Journal | BMC Genomics |
| Issue Number | 1 |
| Volume Number | 17 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2016-05-27 |
| Access Restriction | Open |
| Subject Keyword | Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Peptidoglycan hydrolase N-acetylglucosaminidase N-acetylmuramidases Lytic transglycosylases Endopeptidase N-acetylmuramoyl-L-alanine Carboxypeptidase Cell wall hydrolases Support Vector Machine Random Forest |
| Content Type | Text |
| Resource Type | Article |
| Subject | Biotechnology Genetics |
| Journal Impact Factor | 3.5/2023 |
| 5-Year Journal Impact Factor | 4.1/2023 |
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