Dataset Information

DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases.

ABSTRACT: Biofilm production plays a clinically significant role in the pathogenicity of many bacteria, limiting our ability to apply antimicrobial agents and contributing in particular to the pathogenesis of chronic infections. Bacteriophage depolymerases, leveraged by these viruses to circumvent biofilm mediated resistance, represent a potentially powerful weapon in the fight against antibiotic resistant bacteria. Such enzymes are able to degrade the extracellular matrix that is integral to the formation of all biofilms and as such would allow complementary therapies or disinfection procedures to be successfully applied. In this manuscript, we describe the development and application of a machine learning based approach towards the identification of phage depolymerases. We demonstrate that on the basis of a relatively limited number of experimentally proven enzymes and using an amino acid derived feature vector that the development of a powerful model with an accuracy on the order of 90% is possible, showing the value of such approaches in protein functional annotation and the discovery of novel therapeutic agents.

SUBMITTER: Magill DJ

PROVIDER: S-EPMC10199479 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases.

Magill Damian J DJ Skvortsov Timofey A TA

BMC bioinformatics 20230519 1

Biofilm production plays a clinically significant role in the pathogenicity of many bacteria, limiting our ability to apply antimicrobial agents and contributing in particular to the pathogenesis of chronic infections. Bacteriophage depolymerases, leveraged by these viruses to circumvent biofilm mediated resistance, represent a potentially powerful weapon in the fight against antibiotic resistant bacteria. Such enzymes are able to degrade the extracellular matrix that is integral to the formatio ...[more]

PMID: 37208612

Similar Datasets

Project description:BACKGROUND:Micropeptides are small proteins with length < = 100 amino acids. Short open reading frames that could produces micropeptides were traditionally ignored due to technical difficulties, as few small peptides had been experimentally confirmed. In the past decade, a growing number of micropeptides have been shown to play significant roles in vital biological activities. Despite the increased amount of data, we still lack bioinformatics tools for specifically identifying micropeptides from DNA sequences. Indeed, most existing tools for classifying coding and noncoding ORFs were built on datasets in which "normal-sized" proteins were considered to be positives and short ORFs were generally considered to be noncoding. Since the functional and biophysical constraints on small peptides are likely to be different from those on "normal" proteins, methods for predicting short translated ORFs must be trained independently from those for longer proteins. RESULTS:In this study, we have developed MiPepid, a machine-learning tool specifically for the identification of micropeptides. We trained MiPepid using carefully cleaned data from existing databases and used logistic regression with 4-mer features. With only the sequence information of an ORF, MiPepid is able to predict whether it encodes a micropeptide with 96% accuracy on a blind dataset of high-confidence micropeptides, and to correctly classify newly discovered micropeptides not included in either the training or the blind test data. Compared with state-of-the-art coding potential prediction methods, MiPepid performs exceptionally well, as other methods incorrectly classify most bona fide micropeptides as noncoding. MiPepid is alignment-free and runs sufficiently fast for genome-scale analyses. It is easy to use and is available at https://github.com/MindAI/MiPepid. CONCLUSIONS:MiPepid was developed to specifically predict micropeptides, a category of proteins with increasing significance, from DNA sequences. It shows evident advantages over existing coding potential prediction methods on micropeptide identification. It is ready to use and runs fast.

Project description:BackgroundThe emergence of multidrug- or extensively drug-resistant Acinetobacter baumannii has made it difficult to treat and control infections caused by this bacterium. It is urgently necessary to search for alternatives to conventional antibiotics for control of severe A. baumannii infections. In recent years, bacteriophages and their derivatives, such as depolymerases, showed great potential as antibacterial or antivirulence agents against bacterial infections. Nonetheless, unlike broad-spectrum bactericidal antibiotics, phage-encoded depolymerase targets only a limited number of bacterial strains. Therefore, identification of novel depolymerases and evaluation of their ability to control A. baumannii infections is important.MethodsA bacteriophage was isolated from hospital sewage using an extensively drug-resistant A. baumannii strain as the host bacterium, and the phage's plaque morphology and genomic composition were studied. A polysaccharide depolymerase (Dpo48) was expressed and identified, and the effects of pH and temperature on its activity were determined. Besides, a serum killing assay was conducted, and amino acid sequences homologous to those of putative polysaccharide depolymerases were compared.ResultsPhage IME200 yielded clear plaques surrounded by enlarged halos, with polysaccharide depolymerase activity against the host bacterium. A tail fiber protein with a Pectate_lyase_3 domain was identified as Dpo48 and characterized . Dpo48 was found to degrade the capsule polysaccharide of the bacterial surface, as revealed by Alcian blue staining. Dpo48 manifested stable activity over a broad range of pH (5.0-9.0) and temperatures (20-70 °C). Results from in vitro serum killing assays indicated that 50% serum was sufficient to cause a five log reduction of overnight enzyme-treated bacteria, with serum complement playing an important role in these killing assays. Moreover, Dpo48 had a spectrum of activity exactly the same as its parental phage IME200, which was active against 10 out of 41 A. baumannii strains. Amino acid sequence alignment showed that the putative tail fiber proteins had a relatively short, highly conserved domain in their N-terminal sequences, but their amino acid sequences containing pectate lyase domains, found in the C-terminal regions, were highly diverse.ConclusionsPhage-encoded capsule depolymerases may become promising antivirulence agents for preventing and controlling A. baumannii infections.

Dataset Information

DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases.

Publications

DePolymerase Predictor (DePP): a machine learning tool for the targeted identification of phage depolymerases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets