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 on bio.tools.
-
24 Apr 2024 - We added scripts to simplify running and training our tool. Instructions here.
-
23 Feb 2024 - We presented our work at the eAsia 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. Slides here.
-
01 Dec 2023 - Presenting this work, the lead author (Mark Edward M. Gonzales) won 2nd 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. Presentation here (29:35–39:51) and slides here.
-
07 Jul 2023 - Our paper was accepted for publication in PLOS ONE.
↑ Return to Table of Contents.
Operating System: Windows, Linux, or macOS
Clone the repository:
git clone https://github.com/bioinfodlsu/phage-host-prediction
cd phage-host-prediction
Create a virtual environment with all the necessary dependencies installed via Conda (we recommend using Miniconda):
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 provided here. - Replace
<model_joblib>with the path to the trained model (recognized format: joblib or compressed joblib, framework: scikit-learn). Download our trained model from this link. No need to uncompress, but doing so will speed up loading the model albeit at the cost of additional storage requirements. Refer to this guide 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 provided here.
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 entire dataset. If your machine has a GPU, it will automatically be used to accelerate the protein embedding generation step.
python3 train.py --input <training_dataset>
- Replace
<training_dataset>with the path to the training dataset. A sample can be downloaded here. - 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 filename phiembed_trained.joblib.gz.
↑ Return to Table of Contents.
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.
↑ Return to Table of Contents.
The experiments 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.
Click here to show/hide the list of directories, Jupyter notebooks, and Python scripts, as well as the folder structure.
| Directory | Description |
|---|---|
inphared |
Contains the list of phage-host pairs in TSV format. The GenBank and FASTA files with the genomic and protein sequences of the phages, the embeddings of the receptor-binding proteins, and the phage-host-features CSV files should also be saved in this folder |
preprocessing |
Contains text files related to the preprocessing of host information and the selection of annotated RBPs |
rbp_prediction |
Contains the JSON file of the trained XGBoost model proposed by Boeckaerts et al. (2022) for the computational prediction of receptor-binding proteins. Downloaded from this repository (under the MIT License) |
temp |
Contains intermediate output files during preprocessing and performance evaluation |
↑ Return to Table of Contents.
Each notebook provides detailed instructions related to the required and output files, including the download links and where to save them.
↑ Return to Table of Contents.
| Script | Description |
|---|---|
ClassificationUtil.py |
Contains the utility functions for the generation of the phage-host-features CSV files, construction of the phage-host interaction model, and performance evaluation |
ConstantsUtil.py |
Contains the constants used in the notebooks and scripts |
EDAUtil.py |
Contains the utility functions for exploratory data analysis |
RBPPredictionUtil.py |
Contains the utility functions for the computational prediction of receptor-binding proteins |
SequenceParsingUtil.py |
Contains the utility functions for preprocessing host information and selecting annotated receptor-binding proteins |
boeckaerts.py |
Contains the utility functions written by Boeckaerts et al. (2021) for running their phage-host interaction prediction tool (with which we benchmarked our model). Downloaded from this repository (under the MIT License) |
↑ Return to Table of Contents.
Once you have cloned this repository and finished downloading (or generating) all the additional required files following the instructions in the Jupyter notebooks, your folder structure should be similar to the one below:
phage-host-prediction(root)datasetsexperimentsinpharedmodels(Download)boeckaerts.joblibesm.joblib- ...
preprocessingrbp_predictiontemp1. Sequence Preprocessing.ipynb- ...
ClassificationUtil.py- ...
↑ Return to Table of Contents.
Operating System: Windows, Linux, or macOS
Create a virtual environment with all the necessary dependencies installed via Conda (we recommend using Miniconda):
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.
Click here to show/hide note on running the notebook for protein embedding generation.
The notebook 4. Protein Embedding Generation.ipynb has a dependency (bio_embeddings) that requires it to be run on Unix or a Unix-like operating system. If you are using Windows, consider using Windows Subsystem for Linux (WSL) or a virtual machine. We did not include bio_embeddings in environment_experiments.yaml to maintain cross-platform compatibility; you have to install it following the instructions here.
Moreover, generating protein embeddings should ideally be done on a machine with a GPU. The largest (and best-performing) protein language model that we used, ProtT5, consumes 5.9 GB of GPU memory. If your local machine does not have a GPU or if its GPU has insufficient memory, we recommend using a cloud GPU platform.
UPDATE (12 Jun 2023): In May 2023, Google Colab upgraded its Python runtime, resulting in compatibility issues with bio_embeddings. An alternative cloud GPU platform is Paperspace, which provides a PyTorch 1.12 runtime that is compatible with bio_embeddings.
Click here to show/hide the complete list of Python libraries and modules used in this project (excluding those that are part of the Python Standard Library).
| Library/Module | Description | License |
|---|---|---|
pyyaml |
Supports standard YAML tags and provides Python-specific tags that allow to represent an arbitrary Python object | MIT License |
jsonnet |
Domain-specific language for JSON | Apache License 2.0 |
protobuf |
Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data | BSD 3-Clause "New" or "Revised" License |
regex |
Provides additional functionality over the standard re module while maintaining backwards-compatibility |
Apache License 2.0 |
nltk |
Provides interfaces to corpora and lexical resources, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning | Apache License 2.0 |
biopython |
Provides tools for computational molecular biology | Biopython License Agreement, BSD 3-Clause License |
ete3 |
Provides functions for automated manipulation, analysis, and visualization of phylogenetic trees | GNU General Public License v3.0 |
pandas |
Provides functions for data analysis and manipulation | BSD 3-Clause "New" or "Revised" License |
numpy |
Provides a multidimensional array object, various derived objects, and an assortment of routines for fast operations on arrays | BSD 3-Clause "New" or "Revised" License |
scipy |
Provides efficient numerical routines, such as those for numerical integration, interpolation, optimization, linear algebra, and statistics | BSD 3-Clause "New" or "Revised" License |
scikit-learn |
Provides efficient tools for predictive data analysis | BSD 3-Clause "New" or "Revised" License |
xgboost |
Implements machine learning algorithms under the gradient boosting framework | Apache License 2.0 |
imbalanced-learn |
Provides tools when dealing with classification with imbalanced classes | MIT License |
joblib |
Provides tools for lightweight pipelining in Python | BSD 3-Clause "New" or "Revised" License |
cudatoolkit |
Parallel computing platform and programming model for general computing on GPUs | NVIDIA Software License |
bio_embeddings |
Provides an interface for the use of language model-based biological sequence representations for transfer-learning | MIT License |
torch |
Optimized tensor library for deep learning using GPUs and CPUs | BSD 3-Clause "New" or "Revised" License |
transformers |
Provides pretrained models to perform tasks on different modalities such as text, vision, and audio | Apache License 2.0 |
sentencepiece |
Unsupervised text tokenizer and detokenizer mainly for neural network-based text generation systems | Apache License 2.0 |
matplotlib |
Provides functions for creating static, animated, and interactive visualizations | Matplotlib License (BSD-Compatible) |
umap-learn |
Implements uniform manifold approximation and projection, a dimensionality reduction technique | BSD 3-Clause "New" or "Revised" License |
The descriptions are taken from their respective websites.
↑ Return to Table of Contents.
-
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 the Bioinformatics Laboratory, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Philippines.
This research was partly funded by the Department of Science and Technology – Philippine Council for Health Research and Development (DOST-PCHRD) under the e-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.