alphafold3-tools 0.3.1

Toolkit for alphafold3 input and output files

alphafold3_tools

Toolkit for alphafold3 input generation and output analysis

Installation

Requirements:

• Python 3.12 or later

# install from GitHub
python3 -m pip install alphafold3-tools

Usage

More detailed usage information can be found by running the commands with the -h option. The version information will be displayed with the -v option.

msatojson

msatojson is a command to convert an a3m-formatted multiple sequence alignment (MSA) file to JSON format. The input name can be specified with the -n option.

msatojson -i input.a3m -o input.json -n inputname

The input a3m MSA file can be generated by MMSeqs2 webserver (or ColabFold). colabfold_batch --msa-only option is useful to generate a3m MSA files only.

msatojson can accept a directory containing multiple a3m files. In this case, the output JSON files will be saved in the specified output directory.

msatojson -i /path/to/a3m_containing/directory -o /path/to/output/directory

From version 0.2.0, templates can be also added to the output JSON file. Use the --include_templates option to include templates. The directory path /path/to/mmcif_files containing mmCIF files and the corresponding pdb_seqres.txt file must be specified with the --pdb_database_path and --seqres_database_path options, respectively.

• --max_template_date option can be used to set the maximum template date. The default value is 2099-09-30, which means no filtering based on template date. If you want to the same results as AlphaFold3, set this value to 2021-09-30.
• --max_subsequence_ratio option can be used to set the maximum subsequence ratio for template filtering. The default value is 0.95 (same as the default value of AlphaFold3). However, if you want to include all templates regardless of the subsequence ratio, set this option to 1.0.
• -d option can be used to enable debug mode, which will print debug information during the template search process.

# Example command to include templates in the output JSON file
msatojson -i input.a3m -o output.json \
    --include_templates \
    --pdb_database_path /path/to/mmcif_files \
    --seqres_database_path /path/to/pdb_seqres.txt \
    --max_template_date 2099-09-30 \
    --hmmbuild_binary_path /path/to/hmmbuild \
    --hmmsearch_binary_path /path/to/hmmsearch \
    --save_hmmsto \
    --max_subsequence_ratio 1.0 \
    -d

[!NOTE]

• This feature requires HMMER 3 or later to be installed and accessible in your PATH. For macOS users, you can install HMMER via Homebrew:

brew install hmmer

• --hmmbuild_binary_path and --hmmsearch_binary_path options can be used to specify the paths to the hmmbuild and hmmsearch binaries, respectively, if they are not in your PATH.
• --save_hmmsto option can be used to save HMMER's intermediate file.
• The pdb_seqres.txt file can be downloaded from wwPDB. The file size is about 356 MB (as of Dec. 2025).

fastatojson

fastatojson is a command to convert a FASTA file to JSON format compatible with AlphaFold3.

fastatojson -i input.fasta [-s 1 2 3 ...] [-d]

• -i: Input FASTA file. Mandatory.
• -s: Model seeds to be used. Optional. Default is 1. Multiple seeds can be specified.
• -d: Debug mode. Optional. If specified, the command will print debug information.

For example, if you have a FASTA file containing two sequences, input.fasta:

>P12345
KAKDLSKCLS
>Q67890
KADFILCSLK
>I23L45_I3PLS2
LAKDCL:KKALS

You will obtain three JSON files, p12345.json, q67890.json, and i23l45_i3pls2.json. The last one contains two sequences, LAKDCL and KKALS, which are separated by a colon (:). The output JSON files will look like this:

{
  "name": "i23l45_i3pls2",
  "dialect": "alphafold3",
  "version": 1,
  "sequences": [
    {
      "protein": {
        "id": ["A"],
        "sequence": "LAKDCL"
      },
      "protein": {
        "id": ["B"],
        "sequence": "KKALS"
      }
    }
  ],
  "modelSeeds": [1],
}

modjson

modjson is a command to modify an existing AlphaFold3 input json file. This tool is useful to add/modify the ligand entities and User-provided CCD string in an input json file.

modjson -i input.json -o output.json [-n jobname] [-p] \
       [-a smiles "CCOCCC" 1 -a ccdCodes PRD 2] \
       [-u userccd1.cif userccd2.cif]

• -i: Input json file. Mandatory.
• -o: Output json file. Mandatory.
• -n: Job name. Optional. Sets the job name in the input JSON file.
• -p: Purge all ligand entities from the input JSON file at first.
• -a: Add ligand to the input JSON file. Provide 'ligand type', 'ligand name', and 'number of the ligand molecule'. The 'ligand type' must be either 'smiles' or 'ccdCodes'. Multiple ligands can be added.
  • Example: -a smiles "CCOCCC" 1 -a ccdCodes PRD 2 -a ...
• -u: Add user provided ccdCodes to the input JSON file. Multiple files can be provided.
  • Example: -u userccd1.cif userccd2.cif

[!NOTE] A *_data.json file in the AlphaFold3's output directory can be also used as an input JSON file of modjson.

paeplot

paeplot is a command to plot the predicted aligned error (PAE). The color map can be specified with the -c option. The default color map is bwr (ColabFold-like), but Greens_r is also available for AlphaFold Structure Database (AFDB)-like coloring.

paeplot -i /path/to/alphafold3_output/directory [-c {Greens_r,bwr}] [--dpi 300] [-n foo] [-f {png,svg}] [-a] [-t "PAE Plot"] [--chain-cmap {pymol,unhcr,<matplotlib_colormap_name>}]

arguments:

• -i: Input directory containing the AlphaFold3 output files. Mandatory.
• -c: Color map for the PAE plot. Optional. Default is bwr. Choose either Greens_r or bwr.
• --dpi: DPI of the output image. Optional. Default is 100, but 300 is recommended for publication-quality images.
• -n: Name prefix for the output image file. Optional.
• -f: Output image file format. Optional. Choose either png or svg. Default is png.
• -a: If specified, the plot will include all models in the output directory.
• -t: Title of the plot. Optional.
• --chain-cmap: Color map for chain coloring on top and right. Optional. Choose either pymol, unhcr, or any valid matplotlib colormap name. (e.g. tab20) Default is pymol.

superpose_ciffiles

superpose_ciffiles is a command to superpose the output mmCIF files. The command creates a multi-model mmCIF file containing all the predicted model.cif subdirectories. The output file name can be specified with the -o option. By default, the output file will be saved as foo_superposed.cif in the input directory. -c option can be used to specify the chain ID to be superposed.

superpose_ciffiles -i /path/to/alphafold3_output/directory [-o /path/to/output/directory/foo_superposed.cif] [-c A]

In PyMOL, the following command will be useful to visualize the plDDT values.

color 0x0053D6, b < 100
color 0x65CBF3, b < 90
color 0xFFDB13, b < 70
color 0xFF7D45, b < 50
util.cnc

[!NOTE] To visualize only an object of seed-1_sample-0 with plddt values, type the following command in PyMOL.

color 0x0053D6, seed-1_sample-0 and b < 100
color 0x65CBF3, seed-1_sample-0 and b < 90
color 0xFFDB13, seed-1_sample-0 and b < 70
color 0xFF7D45, seed-1_sample-0 and b < 50

sdftoccd

sdftoccd is a command to convert sdf file to ccd format. Please refer to the AlphaFold3's input document for the detail of User-provided CCD format.

sdftoccd -i input.sdf -o userccd.cif -n STR

jsontomsa

jsontomsa is a command to extract MSA from the AlphaFold3 input JSON file. The output file name can be specified with the -o option.

jsontomsa -i /path/to/alphafold3_data.json -o /path/to/out.a3m

pdbtocif

pdbtocif is a command to convert a PDB file to mmCIF format. The output file name can be specified with the -o option.

pdbtocif -i input.pdb -o output.cif [--pdb_id XXXX]

This tool is useful for converting legacy PDB-formatted files into mmCIF format, which is required for template search in msatojson as well as for input to AlphaFold 3. The --pdb_id option allows users to specify the PDB ID assigned to the output mmCIF file. This is particularly useful when using predicted structures (e.g., from the AlphaFold Structure Database) as templates, because such structures often have nonstandard identifiers (e.g., AF-P12345-F1-model_v1) that are not suitable for template search. By default, the PDB ID in the output mmCIF file is set to xxxx. The PDB ID must be a four-character string consisting of lowercase letters and/or digits, as the template search in msatojson is case-sensitive and the template database uses lowercase PDB IDs.

Other tools are being developed and will be added.

ipsae

ipsae calculates ipSAE and related interaction scores (ipTM, pDockQ, pDockQ2, LIS) for protein–protein (and protein–nucleic acid) complexes predicted by AlphaFold3, ColabFold, or Boltz. It is a reimplementation of ipsae.py (MIT License) by Roland L. Dunbrack Jr., extended with JSON output and batch processing support.

Basic usage — explicit file paths

Specify PAE and structure files directly, equivalent to the original ipsae.py interface:

ipsae -p model_scores_rank_001.json -s model_relaxed_rank_001.pdb [-pc 10 -dc 10]

Options:

• -p / --pae_file: PAE file (.json for AF2/AF3, .npz for Boltz)
• -s / --struct_file: Structure file (.pdb for AF2/Boltz, .cif for AF3/Boltz)
• -pc / --pae_cutoff: PAE threshold in Å (default: 10.0)
• -dc / --dist_cutoff: Cβ distance threshold in Å (default: 10.0)

Directory mode — automatic input detection

ipsae -i /path/to/output_directory

When a directory is passed with -i, ipsae auto-detects the prediction format:

Format	PAE file	Structure file
AlphaFold3	*_confidences.json	*_model.cif
ColabFold	*_scores_rank_001_alphafold2_multimer_v3_model_*_seed_*.json	*_relaxed_rank_001_*.pdb (falls back to *_unrelaxed_* if absent)

Batch processing for ColabFold outputs

When the directory contains multiple ColabFold predictions, ipsae automatically processes all of them in one run. A prediction with prefix foobar is considered complete when both foobar.done.txt and foobar_coverage.png exist in the same directory. Prefix validation runs in parallel across all available CPU cores.

# Process all completed predictions in a ColabFold output directory
ipsae -i /path/to/colabfold_output_dir

Output files

Three files are written next to each structure file:

File	Description
{stem}_{pae}_{dist}.txt	Summary score table
{stem}_{pae}_{dist}_byres.txt	Per-residue score table
{stem}_{pae}_{dist}.pml	PyMOL script for interface visualisation

JSON output with ipSAE_min / ipSAE_max

ipsae -i /path/to/output_directory --json

The --json flag replaces the .txt summary with a .json file. The JSON format extends the original ipSAE output by providing, for each chain pair, the asymmetric score for each direction as well as max and min values across the two asymmetric directions (ipSAE_max and ipSAE_min):

{
  "model_name": {
    "pae_cutoff": 10,
    "dist_cutoff": 10,
    "A-B": {
      "asym": [
        {"chain1": "A", "chain2": "B", "ipSAE": 0.382, "ipSAE_d0chn": 0.412, "ipSAE_d0dom": 0.401, "ipTM_af": 0.65, "pDockQ": 0.731, "pDockQ2": 0.612, "LIS": 0.524, "...": "..."},
        {"chain1": "B", "chain2": "A", "ipSAE": 0.315, "ipSAE_d0chn": 0.298, "ipSAE_d0dom": 0.307, "ipTM_af": 0.65, "pDockQ": 0.731, "pDockQ2": 0.589, "LIS": 0.511, "...": "..."}
      ],
      "max": {"chain1": "A", "chain2": "B", "ipSAE": 0.382, "...": "..."},
      "min": {"chain1": "A", "chain2": "B", "ipSAE": 0.315, "...": "..."}
    }
  }
}

All usage examples

# AF2/ColabFold — explicit file paths (original ipsae.py interface)
ipsae -p foo_scores_rank_001_alphafold2_multimer_v3_model_1_seed_000.json \
      -s foo_relaxed_rank_001_alphafold2_multimer_v3_model_1_seed_000.pdb

# AlphaFold3 — directory auto-detection
ipsae -i /path/to/af3_seed-1_sample-0

# ColabFold — batch processing of an entire output directory
ipsae -i /path/to/colabfold_output_dir

# Custom cutoffs
ipsae -i /path/to/af3_seed-1_sample-0 -pc 15 -dc 15

# JSON output (includes ipSAE_min and ipSAE_max per chain pair)
ipsae -i /path/to/af3_seed-1_sample-0 --json

# ColabFold batch with JSON output
ipsae -i /path/to/colabfold_output_dir --json

Acknowledgements

This tool uses the following libraries:

• RDKit
• matplotlib
• numpy
• gemmi
• loguru
• IPSAE

PDBeurope/ccdutils is used for the conversion of sdf to ccd. RCSB PDB's MAXIT v11.400 is used as a reference for the conversion of PDB to mmCIF.

How do I reference this work?

• Moriwaki Y et al. High-throughput prediction of protein–protein interactions uncovers hidden molecular networks in biosynthetic gene clusters, bioRxiv 2025.10.26.684697; doi: 10.1101/2025.10.26.684697