ArticleBMC genomics2016
Prediction of peptidoglycan hydrolases- a new class of antibacterial proteins.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 30 citations in OpenAlex.
- Transcriptional Analysis and Identification of a Peptidoglycan Hydrolase (PGH) and a Ribosomal Protein with Antimicrobial Activity Produced byInternational journal of molecular sciences · 2024Article
- Engineering of chimeric enzymes with expanded tolerance to ionic strength.Microbiology spectrum · 2024Article
- An exploration of mechanisms underlyingmSphere · 2024Article
- Metagenomic exploration of Andaman region of the Indian Ocean.Scientific reports · 2024Article
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- Making a chink in their armor: Current and next-generation antimicrobial strategies against the bacterial cell envelope.Advances in microbial physiology · 2023Article
- Analysis and Evaluation of the Flagellin Activity ofMolecules (Basel, Switzerland) · 2022Article
- Discovery of a novel antibacterial protein CB6-C to target methicillin-resistant Staphylococcus aureus.Microbial cell factories · 2022Article
- One fold, many functions-M23 family of peptidoglycan hydrolases.Frontiers in microbiology · 2022Review
- The Lysozyme Inhibitor Thionine Acetate Is Also an Inhibitor of the Soluble Lytic Transglycosylase Slt35 fromMolecules (Basel, Switzerland) · 2021Article
- Phagocytosis-like cell engulfment by a planctomycete bacterium.Nature communications · 2019Article
- Peptidoglycan Muropeptides: Release, Perception, and Functions as Signaling Molecules.Frontiers in microbiology · 2019Review
- In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches.BioMed research international · 2016Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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/ .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.