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噬菌体引领：利用集成机器学习方法快速评估噬菌体治疗适用性。

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

机构信息

Center of Excellence for Omics-Driven Computational Biodiscovery (COMBio), AIMST University, Bedong 08100, Kedah, Malaysia.

GLOBE Institute, University of Copenhagen, 1165 Copenhagen, Denmark.

出版信息

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

DOI:10.3390/v14020342
PMID:35215934
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8879740/
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.

摘要

噬菌体基因组的特征分析在噬菌体治疗的成功率中起着至关重要的作用。在筛选噬菌体候选物时，需要检查三个检查点，即是否存在温和噬菌体标记物、抗生素耐药性（AMR）基因和毒力基因。然而，目前还没有用于此目的的单一工具。因此，我们开发了一种能够检查选择合适的治疗性噬菌体候选物所需的所有三个条件的工具。该工具由一组基于机器学习的预测器组成，用于确定温和噬菌体标记物（整合酶、Cro/CI 阻遏物、免疫阻遏物、DNA 分区蛋白 A 和反阻遏物）的存在情况，同时集成了 ABRicate 工具来确定抗生素耐药性基因和毒力基因的存在情况。利用温和噬菌体标记物的生物学特征，我们能够以高 MCC 分数（>0.70）预测温和噬菌体标记物的存在情况，其对应于噬菌体的生活方式，准确率为 96.5%。此外，对 183 个裂解噬菌体基因组的筛选结果表明，有 6 个噬菌体被发现含有 AMR 或毒力基因，这表明并非所有裂解噬菌体都适合用于治疗。PhageLeads 预测器套件以及集成的 ABRicate 工具可以在线访问，用于从单个基因组或宏基因组序列中筛选合适的治疗性噬菌体候选物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/7390d1c15efc/viruses-14-00342-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/29f4ddbfdc75/viruses-14-00342-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/b122585a130a/viruses-14-00342-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/954bb520725c/viruses-14-00342-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/e418255db24d/viruses-14-00342-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/7390d1c15efc/viruses-14-00342-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/29f4ddbfdc75/viruses-14-00342-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/6f6ca5567a23/viruses-14-00342-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/a5040fc125e0/viruses-14-00342-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/b122585a130a/viruses-14-00342-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/67c3/8879740/e418255db24d/viruses-14-00342-g006.jpg
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