# Therapeutic phage host-range prediction using proximity-guided metagenomics and artificial intelligence

> **NIH NIH R44** · PHASE GENOMICS, INC. · 2022 · $995,873

## Abstract

ABSTRACT
There is growing interest in the therapeutic application of phage for treatments of antibiotic-resistant infections
and gut microbiome-related disorders. Phage therapies have the advantage of potentially extreme specificity for
their targets leading to very little in the way of off-target side effects when compared with traditional antibiotic
therapy. However, the identification of phage that target an organism of interest and determining host range
remains a technical challenge. Host assignment for a phage typically requires laboratory culture of the organism
of interest, a significant barrier when trying to target organisms which are difficult to culture, and introducing
significant biases into the existing phage-host knowledge base. And like antibiotics, it is possible that organisms
can acquire resistance to phage transduction, limiting the utility of a single phage to treat an infection over time.
For these reasons it would be highly beneficial to have the ability to identify phage with potentially therapeutic
targets efficiently from an uncultured population of microbes.
In this application, we propose to develop a machine-learning based platform for the identification and
assignment of phage and their hosts from metagenomic whole genome sequencing (WGS) data. Our approach
leverages the unique property of proximity ligation sequencing, or Hi-C, to efficiently gather direct physical
evidence of phage-host associations from mixed microbial communities. We propose to use this technology to
assemble a large-scale, high-quality phage-host interaction dataset from human fecal samples, use it to train a
machine learning model to predict phage-host relationships from existing WGS data, and provide a convenient
platform for users to input metagenomic reads to receive phage-host information. This approach would enable
the identification of phage and combinations of phage to simultaneously target organisms that are otherwise
untractable through standard clinical methods from both existing and future WGS data sets.

## Key facts

- **NIH application ID:** 10547653
- **Project number:** 1R44AI172703-01
- **Recipient organization:** PHASE GENOMICS, INC.
- **Principal Investigator:** Ivan Liachko
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $995,873
- **Award type:** 1
- **Project period:** 2022-06-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10547653

## Citation

> US National Institutes of Health, RePORTER application 10547653, Therapeutic phage host-range prediction using proximity-guided metagenomics and artificial intelligence (1R44AI172703-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10547653. Licensed CC0.

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