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PHIEmbed is a phage-host interaction prediction tool that uses protein language models to represent the receptor-binding proteins of phages. It presents improvements over using handcrafted (manually feature-engineered) sequence properties and eliminates the need to manually extract and select features from phage sequences.

Paper:https://doi.org/10.1371/journal.pone.0289030

If you find our work useful, please consider citing:

@article{10.1371/journal.pone.0289030,    doi = {10.1371/journal.pone.0289030},    author = {Gonzales, Mark Edward M. AND Ureta, Jennifer C. AND Shrestha, Anish M. S.},    journal = {PLOS ONE},    publisher = {Public Library of Science},    title = {Protein embeddings improve phage-host interaction prediction},    year = {2023},    month = {07},    volume = {18},    url = {https://doi.org/10.1371/journal.pone.0289030},    pages = {1-22},    number = {7}}

You can also find PHIEmbed onbio.tools.

• 📰 News
• 🚀 Installation & Usage
• 📚 Description
• 🧪 Reproducing Our Results
• 💻 Authors

• 13 Jan 2025 - We released a new phage-host interaction prediction tool,PHIStruct (published inBioinformatics), which incorporates protein structure information and works especially well for phages with receptor-binding proteins that have low sequence similarity to those of known phages. Learn more about ithere.

• 24 Apr 2024 - We added scripts to simplify running and training our tool. Instructionshere.

• 23 Feb 2024 - We presented our work at theeAsia AMR Workshop 2024 held virtually and in person in Tokyo, Japan, and attended by antimicrobial resistance (AMR) researchers from Thailand, USA, Australia, Japan, and the Philippines. Slideshere.

• 01 Dec 2023 - Presenting this work, the lead author (Mark Edward M. Gonzales) won2nd Prize at the 2023 Magsaysay Future Engineers/Technologists Award. This award is conferred by the National Academy of Science and Technology, the highest recognition and scientific advisory body of the Philippines, to recognize outstanding research outputs on engineering and technology at the collegiate level. Presentationhere (29:35–39:51) and slideshere.

• 24 Jul 2023 - Ourpaper is now published inPLOS ONE.

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Operating System: Windows, Linux, or macOS

Clone the repository:

git clone https://github.com/bioinfodlsu/phage-host-predictioncd phage-host-prediction

Create a virtual environment with all the necessary dependencies installed via Conda (we recommend usingMiniconda):

conda env create -f environment.yaml

Activate this environment by running:

conda activate PHIEmbed

python3 phiembed.py --input <input_fasta> --model <model_joblib> --output <results_dir>

• Replace<input_fasta> with the path to the FASTA file containing the receptor-binding protein sequences. A sample FASTA file is providedhere.
• Replace<model_joblib> with the path to the trained model (recognized format: joblib or compressed joblib, framework: scikit-learn). Download our trained model from thislink. No need to uncompress, but doing so will speed up loading the model albeit at the cost of additional storage requirements. Refer to thisguide for the list of accepted compressed formats.
• Replace<results_dir> with the path to the directory to which the results of running PHIEmbed will be written. The results of running PHIEmbed on the sample FASTA file are providedhere.

The results for each protein are written to a CSV file (without a header row). Each row contains two comma-separated values: a host genus and the corresponding prediction score (class probability). The rows are sorted in order of decreasing prediction score. Hence, the first row pertains to the top-ranked prediction.

Under the hood, this script first converts each sequence into a protein embedding using ProtT5 (the top-performing protein language model based on our experiments) and then passes the embedding to a random forest classifier trained on our entiredataset. If your machine has a GPU, it will automatically be used to accelerate the protein embedding generation step.

Note: Running this script for the first time may take a few extra minutes since it involves downloading a model (ProtT5, around 2 GB) from Hugging Face.

python3 train.py --input <training_dataset>

• Replace<training_dataset> with the path to the training dataset. A sample can be downloadedhere.
• The number of threads to be used for training can be specified using--threads. By default, it is set to -1 (that is, all threads are to be used).

The training dataset should be formatted as a CSV file (without a header row) where each row corresponds to a training sample. The first column is for the protein IDs, the second column is for the host genera, and the next 1,024 columns are for the components of the ProtT5 embeddings.

This script will output a gzip-compressed, serialized version of the trained model with filenamephiembed_trained.joblib.gz.

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Motivation: With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model.

Method: In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage's receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information.

Results: We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.

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Theexperiments folder contains the files and scripts for reproducing our results. Note that additional (large) files have to be downloaded (or generated) following the instructions in the Jupyter notebooks.

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Operating System: Windows, Linux, or macOS

Create a virtual environment with all the necessary dependencies installed via Conda (we recommend usingMiniconda):

conda env create -f environment_experiments.yaml

Activate this environment by running:

conda activate PHIEmbed-experiments

Thanks to Dr. Paul K. Yu for sharing his environment configuration.

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• Mark Edward M. Gonzales
  gonzales.markedward@gmail.com

• Ms. Jennifer C. Ureta
  jennifer.ureta@gmail.com

• Dr. Anish M.S. Shrestha
  anish.shrestha@dlsu.edu.ph

This is a research project under theBioinformatics Laboratory,Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Philippines.

This research was partly funded by theDepartment of Science and Technology – Philippine Council for Health Research and Development (DOST-PCHRD) under thee-Asia JRP 2021 Alternative therapeutics to tackle AMR pathogens (ATTACK-AMR) program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.