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Published on: October 3, 2018
PhageTailFinder: A tool for phage tail module detection and annotation
Fengxia Zhou1, Han Yang1, Yu Si1
1HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
Abstract:
Decades of overconsumption of antimicrobials in the treatment and prevention of bacterial infections have resulted in the increasing emergence of drug-resistant bacteria, which poses a significant challenge to public health, driving the urgent need to find alternatives to conventional antibiotics. Bacteriophages are viruses infecting specific bacterial hosts, often destroying the infected bacterial hosts. Phages attach to and enter their potential hosts using their tail proteins, with the composition of the tail determining the range of potentially infected bacteria. To aid the exploitation of bacteriophages for therapeutic purposes, we developed the PhageTailFinder algorithm to predict tail-related proteins and identify the putative tail module in previously uncharacterized phages. The PhageTailFinder relies on a two-state hidden Markov model (HMM) to predict the probability of a given protein being tail-related. The process takes into account the natural modularity of phage tail-related proteins, rather than simply considering amino acid properties or secondary structures for each protein in isolation. The PhageTailFinder exhibited robust predictive power for phage tail proteins in novel phages due to this sequence-independent operation. The performance of the prediction model was evaluated in 13 extensively studied phages and a sample of 992 complete phages from the NCBI database. The algorithm achieved a high true-positive prediction rate (>80%) in over half (571) of the studied phages, and the ROC value was 0.877 using general models and 0.968 using corresponding morphologic models. It is notable that the median ROC value of 992 complete phages is more than 0.75 even for novel phages, indicating the high accuracy and specificity of the PhageTailFinder. When applied to a dataset containing 189,680 viral genomes derived from 11,810 bulk metagenomic human stool samples, the ROC value was 0.895. In addition, tail protein clusters could be identified for further studies by density-based spatial clustering of applications with the noise algorithm (DBSCAN). The developed PhageTailFinder tool can be accessed either as a web server (http://www.microbiome-bigdata.com/PHISDetector/index/tools/PhageTailFinder) or as a stand-alone program on a standard desktop computer (https://github.com/HIT-ImmunologyLab/PhageTailFinder).
Insights
Drug-resistant bacteria necessitate novel treatments. PhageTailFinder is a new algorithm that accurately predicts bacteriophage tail proteins, aiding the development of phage-based therapies against bacterial infections.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- The rise of antimicrobial resistance poses a significant global health threat, necessitating the development of alternative therapeutic strategies.
- Bacteriophages, viruses that infect bacteria, offer a promising alternative due to their specificity and ability to lyse bacterial cells.
Purpose of the Study:
- To develop and validate an algorithm, PhageTailFinder, for predicting bacteriophage tail-related proteins.
- To facilitate the identification of functional tail modules in uncharacterized phages for therapeutic applications.
Main Methods:
- Developed PhageTailFinder, a hidden Markov model (HMM)-based algorithm, to predict tail-related proteins by considering protein modularity.
- Evaluated the algorithm's performance on 13 well-studied phages and 992 complete phages from the NCBI database.
- Applied the algorithm to a large dataset of viral genomes from human stool samples and utilized DBSCAN for tail protein cluster identification.
Main Results:
- PhageTailFinder demonstrated robust predictive power for phage tail proteins, achieving high true-positive rates (>80%) in over half of the studied phages.
- The algorithm achieved high ROC values (0.877 general, 0.968 morphologic) and a median ROC value >0.75 for novel phages.
- A high ROC value of 0.895 was obtained when applied to viral genomes from human metagenomic samples.
Conclusions:
- PhageTailFinder is an accurate and specific tool for predicting bacteriophage tail proteins, crucial for identifying novel phage candidates for therapeutic use.
- The algorithm's sequence-independent approach and consideration of protein modularity enhance its utility for characterizing uncharacterized phages.
- PhageTailFinder, available as a web server and standalone program, can accelerate the discovery and development of phage-based antimicrobial strategies.
