Prediction of peptidoglycan hydrolases- a new class of antibacterial proteins

BMC Genomics

Research Article

Vineet K. Sharma¹  Harish K.¹  Darshan B. Dhakan¹  Ashok K. Sharma¹  Sanjiv Kumar² 

[1] Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, 462066, Bhopal, India;Metagenomics and Systems Biology Group, Department of Biological Sciences, Indian Institute of Science Education and Research Bhopal, 462066, Bhopal, India;Department of Medicine, University of Connecticut Health Center, 06030, Farmington, CT, USA;

关键词: Peptidoglycan hydrolase;    N-acetylglucosaminidase;    N-acetylmuramidases;    Lytic transglycosylases;    Endopeptidase;    N-acetylmuramoyl-L-alanine;    Carboxypeptidase;    Cell wall hydrolases;    Support Vector Machine;    Random Forest;   

DOI  :  10.1186/s12864-016-2753-8

 received in 2016-04-06, accepted in 2016-05-19,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundThe 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.ResultsIn 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.ConclusionsThe 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/.

【 授权许可】

CC BY   
© The Author(s). 2016