Rapid & standardized annotation of bacterial genomes, MAGs & plasmids using protein structural information

baktfold is a sensitive annotation tool for bacterial genomes, MAGs & plasmids genomes using protein structural homology.

baktfold is very similar to phold but goes beyond phages to bacterial annotation. baktfold takes all "hypothetical proteins" from Bakta's output and uses the ProstT5 protein language model to rapidly translate protein amino acid sequences to the 3Di token alphabet used by Foldseek. Foldseek is then used to search these against a series of databases (SwissProt, AlphaFold Database non-singleton clusters, PDB and CATH).

Additionally, instead of using ProstT5, you can specify protein structures that you have pre-computed for your hypothetical proteins.

You can also specify custom databases to search against using --custom-db

Baktfold is currently under active development. We would welcome any and all feedback (especially bugs) via Issues

If you don't want to install baktfold locally, you can run it without any code using the Google Colab notebook

• baktfold
• Google Colab Notebook
• Table of Contents
  • Install
    • Conda (recommended)
    • Pip
    • Source
    • Database Installation
  • Example
  • Usage
  • Output
    • Conceptual terms
  • Citations

• The best way to install baktfold is using conda, as this will install Foldseek (the only non-Python dependency) along with the Python dependencies
• We would highly recommend installing conda via miniforge.
• To install baktfold:

conda create -n baktfoldENV -c conda-forge -c bioconda baktfold 

• To utilise phold with GPU, a GPU compatible version of pytorch must be installed. By default conda will usually install a CPU-only version.
• If you have an NVIDIA GPU, please try:

conda create -n baktfoldENV -c conda-forge -c bioconda baktfold pytorch=*=cuda*

• If you have a Mac with M-series Apple Silicon, you may need to install a particular version of Pytorch to utilise GPU-acceleration
• The same is true if you use other non-NVIDIA e.g. AMD GPUs
• See this link for some more detail and further links

• You can also install baktfold using pip.

pip install baktfold

• You will need to have Foldseek (ideally v10.941cd33) installed and available in the $PATH.

• You can install the latest version of baktfold with potentially untested and unreleased changes into a conda environment using conda as follows:

conda create -n baktfoldENV foldseek
conda activate baktfoldENV
git clone https://github.com/gbouras13/baktfold.git
cd baktfold
pip install .
baktfold --help

• To download and install baktfold's databases (use as many threads with -t as you can to speed up downloading)

baktfold install -d baktfold_db -t 8

• If you have an NVIDIA GPU, you will need to format the database to allow it to use Foldseek-GPU with --foldseek-gpu
  • Note: you can do this after downloading the database with the above command (it won't redownload the database, only do the relevant Foldseek database padding)

baktfold install -d baktfold_db --foldseek-gpu

• You will first need to run bakta and use the resulting .json file as input for baktfold
  • We will add other input formats eventually, put we would always recommend running Bakta first, as it is awesome and comprehensive
• To use baktfold run using a dummy test example

# with nvidia gpu 
baktfold run -i tests/test_data/assembly_bakta_output/assembly.json  -o baktfold_output -f -t 8 -d baktfold_db   --foldseek-gpu
# without nvidia gpu available
baktfold run -i tests/test_data/assembly_bakta_output/assembly.json  -o baktfold_output -f -t 8 -d baktfold_db   

• To use baktfold proteins using a dummy test example protein .faa file
• Note that this can be any .faa (It does not have to be the output of Bakta)

# with nvidia gpu 
baktfold proteins -i tests/test_data/assembly.hypotheticals.faa  -o baktfold_proteins_output -f -t 8 -d baktfold_db   --foldseek-gpu
# without nvidia gpu available
baktfold proteins -i tests/test_data/assembly.hypotheticals.faa  -o baktfold_proteins_output -f -t 8 -d baktfold_db   

• The two most useful commands are baktfold run and baktfold proteins

• baktfold run accepts a Bakta json file as its input, and by default, it will annotate all hypothetical CDS and return a variety of Bakta-like compliant output formats. All other annotations will be inherited from the Bakta output

• baktfold proteins accepts a protein FASTA .faa format file as input. It will annotate all protein sequences and return a variety of bakta_proteins-like output formats

• baktfold predict and baktfold compare split baktfold run into the ProstT5 and Foldseek modules, while baktfold proteins-predict and baktfold proteins-compare do the same for baktfold proteins (useful if you have non-NVIDIA GPUs)

• It is recommend you run baktfold with a GPU if you can.

• If you do not have a GPU, baktfold will still run, but the ProstT5 step will be fairly slow.

• If you have a NVIDIA GPU, you can also use the --foldseek-gpu parameter to accelerate Foldseek further

Usage: baktfold [OPTIONS] COMMAND [ARGS]...

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

Commands:
  citation          Print the citation(s) for this tool
  compare           Runs Foldseek vs baktfold db
  install           Installs ProstT5 model and baktfold database
  predict           Uses ProstT5 to predict 3Di tokens - GPU recommended
  proteins          baktfold protein-predict then comapare all in one - GPU...
  proteins-compare  Runs Foldseek vs baktfold db on proteins input
  proteins-predict  Runs ProstT5 on a multiFASTA input - GPU recommended
  run               baktfold predict then comapare all in one - GPU...

Usage: baktfold run [OPTIONS]

  baktfold 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 Bakta Genbank format or
                                 Bakta JSON format  [required]
  -o, --output PATH              Output directory   [default: output_baktfold]
  -t, --threads INTEGER          Number of threads  [default: 1]
  -p, --prefix TEXT              Prefix for output files  [default: baktfold]
  -d, --database TEXT            Specific path to installed baktfold database
  -f, --force                    Force overwrites the output directory
  --batch-size INTEGER           batch size for ProstT5. 1 is usually fastest.
                                 [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]
  -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
  --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 baktfold 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
  --custom-annotations PATH      Custom Foldseek DB annotations, 2 column tsv.
                                 Column 1 matches the Foldseek headers, column
                                 2 is the description.
  -a, --all-proteins             annotate all proteins (not just
                                 hypotheticals)

• The majority of outputs match bakta.
• Specifically, all the format compliant outputs match bakta's.
• The differences are:
  • <prefix>.inference.tsv is changed compared to bakta. In baktfold, this file gives a quick overview of the different baktfold databases the query protein has hit (if any)
  • For example:

ID	Length	Product	Swissprot	AFDBClusters	PDB	CATH
MEGJMNBEGN_27	162	HTH-type quorum-sensing regulator RhlR	swissprot_P54292	afdbclusters_A0A9E1VSB0	pdb_5l09	cath_3sztB01
MEGJMNBEGN_30	68	hypothetical protein				
MEGJMNBEGN_70	94	hypothetical protein		afdbclusters_A0A1I3V7E0		

* `<prefix>_<database>_tophit.tsv` files give the detailed Foldseek alignment information for each tophit found for each database.
* For example:

query	target	bitscore	fident	evalue	qStart	qEnd	qLen	qCov	tStart	tEnd	tLen	tCov
MEGJMN_070	AF-A0A1I3V7E0-F1-model_v6	292	0.41	2.619e-06	1	91	93	0.97	1	95	99	0.95

* The full Foldseek search outputs are not kept by default (only tophits) - you can keep the full Foldseek search tsvs using `--keep-tmp-files`. They will be called `foldseek_results_<database>.tsv`
* `baktfold_3di.fasta` which gives the 3Di tokens for each input CDS
* `baktfold_prostT5_3di_mean_probabilities.csv` and `baktfold_prostT5_3di_all_probabilities.json`, which give some score of the confidence ProstT5 has in its predictions. You can disable this output with `--omit-probs`
* Baktfold does not have plotting functionality like Bakta (yet)

• As Baktfold inherits annotations from Bakta, please see the explanation in bakta for all other concepts here
• Baktfold adds one conceptual term in addition to Bakta's:
  • PSTC: protein structure clusters. These comprise of structure-based annotations to any of Baktfold's databases

• A manuscript describing baktfold is in preparation.

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

• 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

• 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

• Please also consider citing these databases where relevant:

• AFDB/SwissProt - Mihaly Varadi, Damian Bertoni, Paulyna Magana, Urmila Paramval, Ivanna Pidruchna, Malarvizhi Radhakrishnan, Maxim Tsenkov, Sreenath Nair, Milot Mirdita, Jingi Yeo, Oleg Kovalevskiy, Kathryn Tunyasuvunakool, Agata Laydon, Augustin Žídek, Hamish Tomlinson, Dhavanthi Hariharan, Josh Abrahamson, Tim Green, John Jumper, Ewan Birney, Martin Steinegger, Demis Hassabis, Sameer Velankar, AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences, Nucleic Acids Research, Volume 52, Issue D1, 5 January 2024, Pages D368–D375, https://doi.org/10.1093/nar/gkad1011

• CATH - Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM. CATH--a hierarchic classification of protein domain structures. Structure. 1997 Aug 15;5(8):1093-108. doi: 10.1016/s0969-2126(97)00260-8. PMID: 9309224.

• PDB - H.M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T.N. Bhat, H. Weissig, I.N. Shindyalov, P.E. Bourne, The Protein Data Bank (2000) Nucleic Acids Research 28: 235-242 https://doi.org/10.1093/nar/28.1.235