PhageTailFinder: A tool for phage tail module detection and annotation.

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.