Recent article from our lab:
Read more at: http://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-016-2753-8
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.
Keywords
Peptidoglycan hydrolase
N-acetylglucosaminidase
N-acetylmuramidases
Lytic transglycosylases
Endopeptidase
N-acetylmuramoyl-L-alanine
Carboxypeptidase
Cell wall hydrolases
Support Vector Machine
Random Forest
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