phold 1.2.2
Phage Annotations using Protein Structures
Requires Python: <4,>=3.8
Keywords: keyword, are, cool

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# phold - Phage Annotation using Protein Structures

<p align="center">
  <img src="img/phold_logo.png" alt="phold Logo" height=250>
</p>

`phold` is a sensitive annotation tool for bacteriophage genomes and metagenomes using protein structural homology. 

**To learn more about `phold`, please read our manuscript:**  
https://academic.oup.com/nar/article/54/1/gkaf1448/8415830

> Bouras G., Grigson S.R., Mirdita M., Heinzinger M., Papudeshi B.,  
> Mallawaarachchi V., Green R., Kim S.R., Mihalia V., Psaltis A.J.,  
> Wormald P-J., Vreugde S., Steinegger M., Edwards R.A.  
>  
> *Protein Structure Informed Bacteriophage Genome Annotation with Phold*  
> **Nucleic Acids Research**, Volume 54, Issue 1, 13 January 2026  
> https://doi.org/10.1093/nar/gkaf1448

`phold` uses the [ProstT5](https://github.com/mheinzinger/ProstT5) protein language model to rapidly translate protein amino acid sequences to the 3Di token alphabet used by [Foldseek](https://github.com/steineggerlab/foldseek). Foldseek is then used to search these against a database of over 1.36 million phage protein structures mostly predicted using [Colabfold](https://github.com/sokrypton/ColabFold). 

<p align="center">
  <img src="img/phold_workflow.png" alt="phold workflow" height=300>
</p>

Alternatively, you can specify protein structures that you have pre-computed for your phage(s) instead of using ProstT5 using the parameters  `--structures` and `--structure_dir` with `phold compare`.

`phold` strongly outperforms sequence-based homology phage annotation tools like [Pharokka](https://github.com/gbouras13/pharokka), particularly for less characterised phages such as those from metagenomic datasets.

If you have already annotated your phage(s) with Pharokka, `phold` takes the Genbank output of Pharokka as an input option, so you can easily update the annotation with more functional predictions!

# Tutorial

Check out the `phold` tutorial at [https://phold.readthedocs.io/en/latest/tutorial/](https://phold.readthedocs.io/en/latest/tutorial/).

# Google Colab Notebooks

If you don't want to install `phold` locally, you can run it without any code using one of the following Google Colab notebooks:

* To run `pharokka` + `phold` + `phynteny` use [this link](https://colab.research.google.com/github/gbouras13/phold/blob/main/run_pharokka_and_phold_and_phynteny.ipynb)
    * [phynteny](https://github.com/susiegriggo/Phynteny_transformer) uses phage synteny (the conserved gene order across phages) to assign hypothetical phage proteins to a PHROG category - it might help you add extra PHROG category annotations to hypothetical genes remaining after you run `phold`. 
  
* Pharokka, Phold and Phynteny are complimentary tools and when used together, they substantially increase the annotation rate of your phage genome
* The below plot shows the annotation rate of different tools across 4 benchmarked datasets ((a) INPHARED 1419, (b) Cook, (c) Crass and (d) Tara - see the [Phold preprint]((https://www.biorxiv.org/content/10.1101/2025.08.05.668817v1)) for more information)
* The final Phynteny plots combine the benefits of annotation with Pharokka (with HMM, the second violin) followed by Phold (with structures, the fourth violin) followed by Phynteny

<p align="center">
  <img src="img/Pharokka_Phold_Phynteny.png" alt="pharokka plus phold plus phynteny" height=1200>
</p>

# Phold plot Wasm App

* We recommending running the web app to generate `phold plot` genomic maps using WebAssembly (Wasm) in your broswer  - no data ever leaves your machine!
* Please go to [https://gbouras13.github.io/phold-plot-wasm-app/](https://gbouras13.github.io/phold-plot-wasm-app/) to use it
* You will need to first run Phold and upload the GenBank file via the button
* This was built during the WebAssembly workshop at ABACBS2025 - for more, you can find the website [here](https://wasmodic.github.io)

# Recent Updates

## v1.2.0 Update (8 January 2026)

* Improved ProstT5 3Di prediction throughput for  `phold run`, `phold predict` and `phold proteins-predict` due to smarter batching implmentations
* Addition of `phold autotune` subcommand to detect an appropriate `--batch_size` for your hardware
* You can also use `--autotune` with `phold run`, `phold predict` and `phold proteins-predict` to automatically detect and use the optimal `--batch_size` (only recommended for large datasets with thousands of proteins)

# Table of Contents

- [phold - Phage Annotation using Protein Structures](#phold---phage-annotation-using-protein-structures)
- [Tutorial](#tutorial)
- [Google Colab Notebooks](#google-colab-notebooks)
- [Phold plot Wasm App](#phold-plot-wasm-app)
- [Recent Updates](#recent-updates)
  - [v1.2.0 Update (8 January 2026)](#v120-update-8-january-2026)
- [Table of Contents](#table-of-contents)
- [Documentation](#documentation)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Output](#output)
- [Usage](#usage)
- [Plotting](#plotting)
- [Citation](#citation)

# Documentation

Check out the full documentation at [https://phold.readthedocs.io](https://phold.readthedocs.io).

# Installation

For more details (particularly if you are using a non-NVIDIA GPU), check out the [installation documentation](https://phold.readthedocs.io/en/latest/install/).

The best way to install `phold` is using conda via [miniforge](https://github.com/conda-forge/miniforge), as this will install [Foldseek](https://github.com/steineggerlab/foldseek) (the only non-Python dependency) along with the Python dependencies.

To install `phold` using [conda](https://github.com/conda-forge/miniforge):

```bash
conda create -n pholdENV -c conda-forge -c bioconda phold 
```

To utilise `phold` with GPU, a GPU compatible version of `pytorch` must be installed. By default conda will install a CPU-only version. 

If you have an NVIDIA GPU, please try:

```bash
conda create -n pholdENV -c conda-forge -c bioconda phold pytorch=*=cuda*
```

If you have a Mac running an Apple Silicon chip (M1/M2/M3/M4), `phold` should be able to use the GPU. Please try:

```bash
conda create -n pholdENV python==3.13  
conda activate pholdENV
conda install pytorch::pytorch torchvision torchaudio -c pytorch 
conda install -c conda-forge -c bioconda phold 
```

If you are have a different non-NVIDIA GPU, or have trouble with `pytorch`, see [this link](https://pytorch.org) for more instructions. If you have an older version of CUDA installed, then you might find [this link useful](https://pytorch.org/get-started/previous-versions/).

Once `phold` is installed, to download and install the database run:

```bash
phold install -t 8
```

If you have an NVIDIA GPU and can take advantage of Foldseek's GPU acceleration, instead run

```bash
phold install -t 8 --foldseek_gpu
```

* Note: You will need at least 8GB of free space (the `phold` databases including ProstT5 are just over 8GB uncompressed).

# Quick Start

* `phold` takes a GenBank format file output from [pharokka](https://github.com/gbouras13/pharokka) or from [NCBI Genbank](https://www.ncbi.nlm.nih.gov/genbank/) as its input by default. 
* If you are running `phold` on a local work station with GPU available, using `phold run` is recommended. It runs both `phold predict` and `phold compare`

``` bash
phold run -i tests/test_data/NC_043029.gbk  -o test_output_phold -t 8
```

* If you have an NVIDIA GPU available, add `--foldseek_gpu`
* If you do not have any GPU available, add `--cpu`.
* `phold run` will run in a reasonable time for small datasets with CPU only (e.g. <5 minutes for a 50kbp phage). With GPU it should complete in under 1 minute.
* `phold predict` will complete much faster if a GPU is available, and is necessary for large metagenomic datasets to run in a reasonable time. 

* In a cluster environment where GPUs are scarce, for large datasets it may be most efficient to run `phold` in 2 steps for optimal resource usage.

1. Predict the 3Di sequences with ProstT5 using `phold predict`. This is massively accelerated if a GPU available.

```bash
phold predict -i tests/test_data/NC_043029.gbk -o test_predictions 
```

2. Compare the the 3Di sequences to the `phold` structure database with Foldseek using `phold compare`. This does not utilise a GPU. 

```bash
phold compare -i tests/test_data/NC_043029.gbk --predictions_dir test_predictions -o test_output_phold -t 8 
```

# Output

* The primary outputs are:
  * `phold_3di.fasta` containing the 3Di sequences for each CDS
  * `phold_per_cds_predictions.tsv` containing detailed annotation information on every CDS
  * `phold_all_cds_functions.tsv` containing counts per contig of CDS in each PHROGs category, VFDB, CARD, ACRDB and Defensefinder databases (similar to the `pharokka_cds_functions.tsv` from Pharokka)
  * `phold.gbk`, which contains a GenBank format file including these annotations, and keeps any other genomic features (tRNA, CRISPR repeats, tmRNAs) included from the `pharokka` Genbank input file if provided

# Usage

```bash
Usage: phold [OPTIONS] COMMAND [ARGS]...

Options:
  -h, --help     Show this message and exit.
  -V, --version  Show the version and exit.

Commands:
  autotune          Determines optimal batch size for 3Di prediction with
  citation          Print the citation(s) for this tool
  compare           Runs Foldseek vs phold db
  createdb          Creates foldseek DB from AA FASTA and 3Di FASTA input...
  install           Installs ProstT5 model and phold database
  plot              Creates Phold Circular Genome Plots
  predict           Uses ProstT5 to predict 3Di tokens - GPU recommended
  proteins-compare  Runs Foldseek vs phold db on proteins input
  proteins-predict  Runs ProstT5 on a multiFASTA input - GPU recommended
  remote            Uses Foldseek API to run ProstT5 then Foldseek locally
  run               phold predict then comapare all in one - GPU recommended
```

```bash
Usage: phold run [OPTIONS]

  phold predict then comapare all in one - GPU recommended

Options:
  -h, --help                     Show this message and exit.
  -V, --version                  Show the version and exit.
  -i, --input PATH               Path to input file in Genbank format or
                                 nucleotide FASTA format  [required]
  -o, --output PATH              Output directory   [default: output_phold]
  -t, --threads INTEGER          Number of threads  [default: 1]
  -p, --prefix TEXT              Prefix for output files  [default: phold]
  -d, --database TEXT            Specific path to installed phold database
  -f, --force                    Force overwrites the output directory
  --autotune                     Run autotuning to detect and automatically
                                 use best batch size for your hardware.
                                 Recommended only if you have a large dataset
                                 (e.g. thousands of proteins), or else
                                 autotuning will add rather than save runtime.
  --batch_size INTEGER           batch size for ProstT5.  [default: 1]
  --cpu                          Use cpus only.
  --omit_probs                   Do not output per residue 3Di probabilities
                                 from ProstT5. Mean per protein 3Di
                                 probabilities will always be output.
  --save_per_residue_embeddings  Save the ProstT5 embeddings per resuide in a
                                 h5 file
  --save_per_protein_embeddings  Save the ProstT5 embeddings as means per
                                 protein in a h5 file
  --mask_threshold FLOAT         Masks 3Di residues below this value of
                                 ProstT5 confidence for Foldseek searches
                                 [default: 25]
  --finetune                     Use gbouras13/ProstT5Phold encoder + CNN
                                 model both finetuned on phage proteins
  --vanilla                      Use vanilla CNN model (trained on CASP14)
                                 with ProstT5Phold encoder instead of the one
                                 trained on phage proteins
  --hyps                         Use this to only annotate hypothetical
                                 proteins from a Pharokka GenBank input
  -e, --evalue FLOAT             Evalue threshold for Foldseek  [default:
                                 1e-3]
  -s, --sensitivity FLOAT        Sensitivity parameter for foldseek  [default:
                                 9.5]
  --keep_tmp_files               Keep temporary intermediate files,
                                 particularly the large foldseek_results.tsv
                                 of all Foldseek hits
  --card_vfdb_evalue FLOAT       Stricter E-value threshold for Foldseek CARD
                                 and VFDB hits  [default: 1e-10]
  --separate                     Output separate GenBank files for each contig
  --max_seqs INTEGER             Maximum results per query sequence allowed to
                                 pass the prefilter. You may want to reduce
                                 this to save disk space for enormous datasets
                                 [default: 1000]
  --ultra_sensitive              Runs phold with maximum sensitivity by
                                 skipping Foldseek prefilter. Not recommended
                                 for large datasets.
  --extra_foldseek_params TEXT   Extra foldseek search params
  --custom_db TEXT               Path to custom database
  --foldseek_gpu                 Use this to enable compatibility with
                                 Foldseek-GPU search acceleration
  --restart                      Use this to restart phold from 'Processing
                                 Foldseek output' after foldseek_results.tsv
                                 is generated
  ```

# Plotting 

`phold plot` will allow you to create Circos plots with [pyCirclize](https://github.com/moshi4/pyCirclize) for all your phage(s). For example:

```bash
phold plot -i tests/test_data/NC_043029_phold_output.gbk  -o NC_043029_phold_plots -t '${Stenotrophomonas}$ Phage SMA6'  
```

<p align="center">
  <img src="img/NC_043029.png" alt="NC_043029" height=600>
</p>

# Citation

Please cite our preprint:

* Bouras G, Grigson SR, Mirdita M, Heinzinger M, Papudeshi B, Mallawaarachchi V, Green R, Kim SR, Mihalia V, Psaltis AJ, Wormald P-J, Vreugde S, Steinegger M, Edwards RA: "Protein Structure Informed Bacteriophage Genome Annotation with Phold", Nucleic Acids Research, Volume 54, Issue 1, 13 January 2026, gkaf1448, [https://doi.org/10.1093/nar/gkaf1448](https://doi.org/10.1093/nar/gkaf1448)

Please be sure to cite the following core dependencies and PHROGs database - citing all bioinformatics tools that you use helps us, so helps you get better bioinformatics tools:

* Pharokka - (https://github.com/gbouras13/pharokka) [Bouras G, Nepal R, Houtak G, Psaltis AJ, Wormald P-J, Vreugde S. Pharokka: a fast scalable bacteriophage annotation tool. Bioinformatics, Volume 39, Issue 1, January 2023, btac776](https://doi.org/10.1093/bioinformatics/btac776)
* Foldseek - (https://github.com/steineggerlab/foldseek) [van Kempen M, Kim S, Tumescheit C, Mirdita M, Lee J, Gilchrist C, Söding J, and Steinegger M. Fast and accurate protein structure search with Foldseek. Nature Biotechnology (2023), [doi:10.1038/s41587-023-01773-0 ](https://www.nature.com/articles/s41587-023-01773-0)
* ProstT5 - (https://github.com/mheinzinger/ProstT5) [Michael Heinzinger, Konstantin Weissenow, Joaquin Gomez Sanchez, Adrian Henkel, Martin Steinegger, Burkhard Rost. ProstT5: Bilingual language model for protein sequence and structure. NAR Genomics and Bioinformatics (2024) [doi:10.1101/2023.07.23.550085](https://doi.org/10.1093/nargab/lqae150) 
* Colabfold - (https://github.com/sokrypton/ColabFold) [Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. ColabFold: Making protein folding accessible to all. Nature Methods (2022) [doi: 10.1038/s41592-022-01488-1 ](https://www.nature.com/articles/s41592-022-01488-1)
* PHROGs - (https://phrogs.lmge.uca.fr) [Terzian P., Olo Ndela E., Galiez C., Lossouarn J., Pérez Bucio R.E., Mom R., Toussaint A., Petit M.A., Enault F., "PHROG : families of prokaryotic virus proteins clustered using remote homology", NAR Genomics and Bioinformatics, (2021) [https://doi.org/10.1093/nargab/lqab067](https://doi.org/10.1093/nargab/lqab067)

Please also consider citing these supplementary databases where relevant:

* [CARD](https://card.mcmaster.ca) - Alcock B.P. et al, CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database Nucleic Acids Research (2022) [https://doi.org/10.1093/nar/gkac920](https://doi.org/10.1093/nar/gkac920)
* [VFDB](http://www.mgc.ac.cn/VFs/main.htm) - Chen L., Yang J., Yao Z., Sun L., Shen Y., Jin Q., "VFDB: a reference database for bacterial virulence factors", Nucleic Acids Research (2005) [https://doi.org/10.1093/nar/gki008](https://doi.org/10.1093/nar/gki008)
* [Defensefinder](https://defensefinder.mdmlab.fr) - F. Tesson,  R. Planel, A. Egorov, H. Georjon,  H. Vaysset,  B. Brancotte,  B. Néron,  E. Mordret,  A Bernheim,  G. Atkinson,  J. Cury. A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases. bioRxiv (2024) [https://doi.org/10.1101/2024.01.25.577194](https://doi.org/10.1101/2024.01.25.577194)
* [acrDB](https://bcb.unl.edu/AcrDB/) - please cite the original acrDB database paper Le Huang, Bowen Yang, Haidong Yi, Amina Asif, Jiawei Wang, Trevor Lithgow, Han Zhang, Fayyaz ul Amir Afsar Minhas, Yanbin Yin, AcrDB: a database of anti-CRISPR operons in prokaryotes and viruses. Nucleic Acids Research (2021) [https://doi.org/10.1093/nar/gkaa857](https://doi.org/10.1093/nar/gkaa857) AND the paper that generated the structures for these protein used by `phold` [Harutyun Sahakyan, Kira S. Makarova, and Eugene V. Koonin. Search for Origins of Anti-CRISPR Proteins by Structure Comparison. The CRISPR Journal (2023)](https://doi.org/10.1089/crispr.2023.0011)
* [Netflax](http://netflax.webflags.se) - Karin Ernits, Chayan Kumar Saha, Tetiana Brodiazhenko, Bhanu Chouhan, Aditi Shenoy, Jessica A. Buttress, Julián J. Duque-Pedraza, Veda Bojar, Jose A. Nakamoto, Tatsuaki Kurata, Artyom A. Egorov, Lena Shyrokova, Marcus J. O. Johansson, Toomas Mets, Aytan Rustamova, Jelisaveta Džigurski, Tanel Tenson, Abel Garcia-Pino, Henrik Strahl, Arne Elofsson, Vasili Hauryliuk, and Gemma C. Atkinson, The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin–antitoxin and related phage defense systems. PNAS (2023) [https://doi.org/10.1073/pnas.2305393120](https://doi.org/10.1073/pnas.2305393120) 
* [Netflax](http://netflax.webflags.se) - Karin Ernits, Chayan Kumar Saha, Tetiana Brodiazhenko, Bhanu Chouhan, Aditi Shenoy, Jessica A. Buttress, Julián J. Duque-Pedraza, Veda Bojar, Jose A. Nakamoto, Tatsuaki Kurata, Artyom A. Egorov, Lena Shyrokova, Marcus J. O. Johansson, Toomas Mets, Aytan Rustamova, Jelisaveta Džigurski, Tanel Tenson, Abel Garcia-Pino, Henrik Strahl, Arne Elofsson, Vasili Hauryliuk, and Gemma C. Atkinson, The structural basis of hyperpromiscuity in a core combinatorial network of type II toxin–antitoxin and related phage defense systems. PNAS (2023) [https://doi.org/10.1073/pnas.2305393120](https://doi.org/10.1073/pnas.2305393120)