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. 2022 Feb 8;14(2):342.
doi: 10.3390/v14020342.

PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach

Kumarasan Yukgehnaish  1 , Heera Rajandas  1   2 , Sivachandran Parimannan  1   2 , Ravichandran Manickam  1   3   4 , Kasi Marimuthu  1   3 , Bent Petersen  1   2   5 , Martha R J Clokie  6 , Andrew Millard  6 , Thomas Sicheritz-Pontén  1   2   5
Affiliations

PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach

Kumarasan Yukgehnaish et al. Viruses. .

Abstract

The characterization of therapeutic phage genomes plays a crucial role in the success rate of phage therapies. There are three checkpoints that need to be examined for the selection of phage candidates, namely, the presence of temperate markers, antimicrobial resistance (AMR) genes, and virulence genes. However, currently, no single-step tools are available for this purpose. Hence, we have developed a tool capable of checking all three conditions required for the selection of suitable therapeutic phage candidates. This tool consists of an ensemble of machine-learning-based predictors for determining the presence of temperate markers (integrase, Cro/CI repressor, immunity repressor, DNA partitioning protein A, and antirepressor) along with the integration of the ABRicate tool to determine the presence of antibiotic resistance genes and virulence genes. Using the biological features of the temperate markers, we were able to predict the presence of the temperate markers with high MCC scores (>0.70), corresponding to the lifestyle of the phages with an accuracy of 96.5%. Additionally, the screening of 183 lytic phage genomes revealed that six phages were found to contain AMR or virulence genes, showing that not all lytic phages are suitable to be used for therapy. The suite of predictors, PhageLeads, along with the integrated ABRicate tool, can be accessed online for in silico selection of suitable therapeutic phage candidates from single genome or metagenomic contigs.

Keywords: AMR; genomics; lysogeny; machine learning; phage therapy.

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Conflict of interest statement

The authors declare that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Network graph of the integrase dataset with green dots indicating positive labels and red dots indicating negative labels.
Figure 2
Figure 2
Test MCC of integrase predictor using original dataset and filtered datasets using different numbers of features. Graph on the right shows the magnified section of the original graph with MCC ranging from 0.8 to 1.
Figure 3
Figure 3
Test MCC of Cro/CI predictor using original dataset and filtered datasets using different numbers of features. Graph on the right shows the magnified section of the original graph with MCC ranging from 0.5 to 1.
Figure 4
Figure 4
Test MCC of immunity repressor predictor using original dataset and filtered datasets using different numbers of features. Graph on the right shows the magnified section of the original graph with MCC ranging from 0.5 to 1.
Figure 5
Figure 5
Test MCC of ParA predictor using original dataset and filtered datasets using different numbers of features.
Figure 6
Figure 6
Test MCC of antirepressor predictor using original dataset and filtered datasets using different numbers of features. Graph on the right shows the magnified section of the original graph with MCC ranging from 0.5 to 1.
Figure 7
Figure 7
Mean MCC of random 10-fold cross-validation and clustered 10-fold cross-validation.
See this image and copyright information in PMC

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