PhageTailFinder: A tool for phage tail module detection and annotation - Zhou, Fengxia; Yang, Han; Si, Yu; Gan, Rui; Yu, Ling; Chen, Chuangeng; Ren, Chunyan; Wu, Jiqiu; Zhang, Fan (Frontiers in Genetics, 2023)
Decades of overconsumption of antimicrobials in the treatment and prevention of bacterial infections have brought out the increasing emergence of drug-resistant bacteria which poses a significant chal-lenge to public health, driving the urgent need to find alternatives to conventional antibiotics. Bacte-riophages 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 proposes we developed the PhageTailFinder algorithm to predict tail-related proteins and identify the putative tail module in previously uncharacterized phages. 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 structure for each protein in isolation. PhageTailFinder exhibited robust predictive power for phage tail proteins in novel phages due to this sequence independent operation. The performance of prediction model was evaluated on 13 exten-sively 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 PhageTailFinder. When applied to a dataset containing 189,680 viral genomes derived from 11,810 bulk metagenomic samples of human stool samples, the ROC value was 0.895. In addition, tail protein clusters could be identified for further studies by den-sity-based spatial clustering of applications with noise algorithm (DBSCAN). The developed Phage-TailFinder 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).