Dataset Information

A Toxicity Screening Approach to Identify Bacteriophage-Encoded Anti-Microbial Proteins.

ABSTRACT: The rapid emergence of antibiotic resistance among many pathogenic bacteria has created a profound need to discover new alternatives to antibiotics. Bacteriophages, the viruses of microbes, express special proteins to overtake the metabolism of the bacterial host they infect, the best known of which are involved in bacterial lysis. However, the functions of majority of bacteriophage encoded gene products are not known, i.e., they represent the hypothetical proteins of unknown function (HPUFs). In the current study we present a phage genomics-based screening approach to identify phage HPUFs with antibacterial activity with a long-term goal to use them as leads to find unknown targets to develop novel antibacterial compounds. The screening assay is based on the inhibition of bacterial growth when a toxic gene is expression-cloned into a plasmid vector. It utilizes an optimized plating assay producing a significant difference in the number of transformants after ligation of the toxic and non-toxic genes into a cloning vector. The screening assay was first tested and optimized using several known toxic and non-toxic genes. Then, it was applied to screen 94 HPUFs of bacteriophage ?R1-RT, and identified four HPUFs that were toxic to Escherichia coli. This optimized assay is in principle useful in the search for bactericidal proteins of any phage, and also opens new possibilities to understanding the strategies bacteriophages use to overtake bacterial hosts.

SUBMITTER: Mohanraj U

PROVIDER: S-EPMC6893735 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Toxicity Screening Approach to Identify Bacteriophage-Encoded Anti-Microbial Proteins.

Mohanraj Ushanandini U Wan Xing X Spruit Cindy M CM Skurnik Mikael M Pajunen Maria I MI

Viruses 20191114 11

The rapid emergence of antibiotic resistance among many pathogenic bacteria has created a profound need to discover new alternatives to antibiotics. Bacteriophages, the viruses of microbes, express special proteins to overtake the metabolism of the bacterial host they infect, the best known of which are involved in bacterial lysis. However, the functions of majority of bacteriophage encoded gene products are not known, i.e., they represent the hypothetical proteins of unknown function (HPUFs). I ...[more]

PMID: 31739448

Similar Datasets

Project description:BackgroundOver the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.DescriptionWe obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.ConclusionToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).

Dataset Information

A Toxicity Screening Approach to Identify Bacteriophage-Encoded Anti-Microbial Proteins.

Publications

A Toxicity Screening Approach to Identify Bacteriophage-Encoded Anti-Microbial Proteins.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets