ColabFold on your local PC (or macOS). See also ColabFold repository.
LocalColabFold is an installer script designed to make ColabFold functionality available on users' local machines. It supports wide range of operating systems, such as Windows 10 or later (using Windows Subsystem for Linux 2), macOS, and Linux.
If you only intend to predict a small number of naturally occurring proteins, I recommend using ColabFold notebook or downloading structures from the AlphaFold Protein Structure Database or UniProt. LocalColabFold is suitable for more advanced applications, such as batch processing of structure predictions for natural complexes, non-natural proteins, or predictions with manually specified MSAs/templates.
- Structure inference and relaxation will be accelerated if your PC has Nvidia GPU and CUDA drivers.
- No Time out (90 minutes and 12 hours)
- No GPU limitations
- NOT necessary to prepare the large database required for native AlphaFold2.
- Since current GPU-supported jax > 0.4.26 requires CUDA 12.1 or later and cudnn 9, please upgrade or install your CUDA driver and cudnn. CUDA 12.4 is recommended.
- ColabFold now upgrade to 1.5.5 (compatible with AlphaFold 2.3.2). Now LocalColabFold requires CUDA 12.1 or later. Please update your CUDA driver if you have not done so.
- Now (Local)ColabFold can predict protein structures without connecting the Internet. Use
setup_databases.sh
script to download and build the databases (See also ColabFold Downloads). An instruction to runcolabfold_search
to obtain the MSA and templates locally is written in this comment.
- 30Jan2024, ColabFold 1.5.5 (Compatible with AlphaFold 2.3.2). Now LocalColabFold requires CUDA 12.1 or later. Please update your CUDA driver.
- 30Apr2023, Updated to use python 3.10 for compatibility with Google Colaboratory.
- 09Mar2023, version 1.5.1 released. The base directory has been changed to
localcolabfold
fromcolabfold_batch
to distinguish it from the execution command. - 09Mar2023, version 1.5.0 released. See Release v1.5.0
- 05Feb2023, version 1.5.0-pre released.
- 16Jun2022, version 1.4.0 released. See Release v1.4.0
- 07May2022, Updated
update_linux.sh
. See also How to update. Please use a new option--use-gpu-relax
if GPU relaxation is required (recommended). - 12Apr2022, version 1.3.0 released. See Release v1.3.0
- 09Dec2021, version 1.2.0-beta released. easy-to-use updater scripts added. See How to update.
- 04Dec2021, LocalColabFold is now compatible with the latest pip installable ColabFold. In this repository, I will provide a script to install ColabFold with some external parameter files to perform relaxation with AMBER. The weight parameters of AlphaFold and AlphaFold-Multimer will be downloaded automatically at your first run.
-
Make sure
curl
,git
, andwget
commands are already installed on your PC. If not present, you need install them at first. For Ubuntu, typesudo apt -y install curl git wget
. -
Make sure your Cuda compiler driver is 11.8 or later (the latest version 12.4 is preferable). If you don't have a GPU or don't plan to use a GPU, you can skip this step :
$ nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2022 NVIDIA Corporation Built on Wed_Sep_21_10:33:58_PDT_2022 Cuda compilation tools, release 11.8, V11.8.89 Build cuda_11.8.r11.8/compiler.31833905_0
DO NOT usenvidia-smi
to check the version.
See NVIDIA CUDA Installation Guide for Linux if you haven't installed it. -
Make sure your GNU compiler version is 9.0 or later because
GLIBCXX_3.4.26
is required for openmm:$ gcc --version gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 Copyright (C) 2019 Free Software Foundation, Inc. This is free software; see the source for copying conditions. There is NO warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
If the version is 8.5.0 or older (e.g. CentOS 7, Rocky/Almalinux 8, etc.), install a new one and addPATH
to it. -
Download
install_colabbatch_linux.sh
from this repository:$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_linux.sh
and run it in the directory where you want to install:$ bash install_colabbatch_linux.sh
About 5 minutes later,localcolabfold
directory will be created. Do not move this directory after the installation.Keep the network unblocked. And check the log output to see if there are any errors.
If you find errors in the output log, the easiest way is to check the network and delete the localcolabfold directory, then re-run the installation script.
-
Add environment variable PATH:
# For bash or zsh
It is recommended to add this export command to
# e.g. export PATH="/home/moriwaki/Desktop/localcolabfold/colabfold-conda/bin:$PATH"
export PATH="/path/to/your/localcolabfold/colabfold-conda/bin:$PATH"~/.bashrc
and restart bash (~/.bashrc
will be executed every time bash is started) -
To run the prediction, type
colabfold_batch input outputdir/
The result files will be created in theoutputdir
. This command will execute the prediction without templates and relaxation (energy minimization). If you want to use templates and relaxation, add--templates
and--amber
flags, respectively. For example,colabfold_batch --templates --amber input outputdir/
colabfold_batch
will automatically detect whether the prediction is for monomeric or complex prediction. In most cases, users don't have to add--model-type alphafold2_multimer_v3
to turn on multimer prediction.alphafold2_multimer_v1, alphafold2_multimer_v2
are also available. Default isauto
(usealphafold2_ptm
for monomers andalphafold2_multimer_v3
for complexes.)
For more details, see Flags and colabfold_batch --help
.
Caution: If your installation fails due to symbolic link (symlink
) creation issues, this is due to the Windows file system being case-insensitive (while the Linux file system is case-sensitive). To resolve this, run the following command on Windows Powershell:
fsutil file SetCaseSensitiveInfo path\to\localcolabfold\installation enable
Replace path\to\colabfold\installation
with the path to the directory where you are installing LocalColabFold. Also, make sure that you are running the command on Windows Powershell (not WSL). For more details, see Adjust Case Sensitivty (Microsoft).
Before running the prediction:
export TF_FORCE_UNIFIED_MEMORY="1"
export XLA_PYTHON_CLIENT_MEM_FRACTION="4.0"
export XLA_PYTHON_CLIENT_ALLOCATOR="platform"
export TF_FORCE_GPU_ALLOW_GROWTH="true"
It is recommended to add these export commands to ~/.bashrc
and restart bash (~/.bashrc
will be executed every time bash is started)
Caution: Due to the lack of Nvidia GPU/CUDA driver, the structure prediction on macOS are 5-10 times slower than on Linux+GPU. For the test sequence (58 a.a.), it may take 30 minutes. However, it may be useful to play with it before preparing Linux+GPU environment.
You can check whether your Mac is Intel or Apple Silicon by typing uname -m
on Terminal.
$ uname -m
x86_64 # Intel
arm64 # Apple Silicon
Please use the correct installer for your Mac.
- Install Homebrew if not present:
$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
- Install
wget
,gnu-sed
, HH-suite and kalign using Homebrew:$ brew install wget gnu-sed
$ brew install brewsci/bio/hh-suite brewsci/bio/kalign - Download
install_colabbatch_intelmac.sh
from this repository:$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_intelmac.sh
and run it in the directory where you want to install:$ bash install_colabbatch_intelmac.sh
About 5 minutes later,colabfold_batch
directory will be created. Do not move this directory after the installation. - The rest procedure is the same as "For Linux".
Note: This installer is experimental because most of the dependent packages are not fully tested on Apple Silicon Mac.
- Install Homebrew if not present:
$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
- Install several commands using Homebrew (Now kalign 3.3.2 is available!):
$ brew install wget cmake gnu-sed
$ brew install brewsci/bio/hh-suite
$ brew install brewsci/bio/kalign - Install
miniforge
command using Homebrew:$ brew install --cask miniforge
- Download
install_colabbatch_M1mac.sh
from this repository:$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_M1mac.sh
and run it in the directory where you want to install:$ bash install_colabbatch_M1mac.sh
About 5 minutes later,colabfold_batch
directory will be created. Do not move this directory after the installation. You can ignore the installation errors that appear along the way. - The rest procedure is the same as "For Linux".
ColabFold can accept multiple file formats or directory.
positional arguments:
input Can be one of the following: Directory with fasta/a3m
files, a csv/tsv file, a fasta file or an a3m file
results Directory to write the results to
It is recommended that the header line starting with >
be short since the description will be the prefix of the output file. It is acceptable to insert line breaks in the amino acid sequence.
>sp|P61823
MALKSLVLLSLLVLVLLLVRVQPSLGKETAAAKFERQHMDSSTSAASSSNYCNQMMKSRN
LTKDRCKPVNTFVHESLADVQAVCSQKNVACKNGQTNCYQSYSTMSITDCRETGSSKYPN
CAYKTTQANKHIIVACEGNPYVPVHFDASV
For prediction of multimers, insert :
between the protein sequences.
>1BJP_homohexamer
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR
>3KUD_RasRaf_complex
MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQ
YMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIP
YIETSAKTRQGVEDAFYTLVREIRQH:
PSKTSNTIRVFLPNKQRTVVNVRNGMSLHDCLMKALKVRGLQPECCAVFRLLHEHKGKKARLDWNTDAAS
LIGEELQVDFL
Multiple >
header lines with sequences in a FASTA format file yield multiple predictions at once in the specified output directory.
In a csv format, id
and sequence
should be separated by ,
.
id,sequence
5AWL_1,YYDPETGTWY
3G5O_A_3G5O_B,MRILPISTIKGKLNEFVDAVSSTQDQITITKNGAPAAVLVGADEWESLQETLYWLAQPGIRESIAEADADIASGRTYGEDEIRAEFGVPRRPH:MPYTVRFTTTARRDLHKLPPRILAAVVEFAFGDLSREPLRVGKPLRRELAGTFSARRGTYRLLYRIDDEHTTVVILRVDHRADIYRR
You can input your a3m format MSA file. For multimer predictions, the a3m file should be compatible with colabfold format.
These flags are useful for the predictions.
--amber
: Use amber for structure refinement (relaxation / energy minimization). To control number of top ranked structures are relaxed set--num-relax
.--templates
: Use templates from pdb.--use-gpu-relax
: Run amber on NVidia GPU instead of CPU. This feature is only available on a machine with Nvidia GPUs.--num-recycle <int>
: Number of prediction recycles. Increasing recycles can improve the quality but slows down the prediction. Default is3
. (e.g.--num-recycle 10
)--custom-template-path <directory>
: Restrict template files used for--template
to only those contained in the specified directory. This flag enables us to use non-public pdb files for the prediction. See also sokrypton/ColabFold#177 .--random-seed <int>
Changing the seed for the random number generator can result in different structure predictions. (e.g.--random-seed 42
)--num-seeds <int>
Number of seeds to try. Will iterate from range(random_seed, random_seed+num_seeds). (e.g.--num-seed 5
)--max-msa
: Defines:max-seq:max-extra-seq
number of sequences to use (e.g.--max-msa 512:1024
).--max-seq
and--max-extra-seq
arguments are also available if you want to specify separately. This is a reimplementation of the paper of Sampling alternative conformational states of transporters and receptors with AlphaFold2 demonstrated by del Alamo et al.--use-dropout
: activate dropouts during inference to sample from uncertainity of the models.--overwrite-existing-results
: Overwrite the result files.- For more information,
colabfold_batch --help
.
Since ColabFold is still a work in progress, your localcolabfold should be also updated frequently to use the latest features. An easy-to-use update script is provided for this purpose.
To update your localcolabfold, simply type in the colabfold_batch
directory:
# set your OS. Select one of the following variables {linux,intelmac,M1mac}
$ OS=linux # if Linux
# get the latest updater
$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/update_${OS}.sh -O update_${OS}.sh
$ chmod +x update_${OS}.sh
# execute it.
$ ./update_${OS}.sh .
- What else do I need to do before installation? Do I need sudo privileges?
- No, except for installation of
curl
andwget
commands.
- No, except for installation of
- Do I need to prepare the large database such as PDB70, BFD, Uniclust30, MGnify?
- No. it is not necessary. Generation of MSA is performed by the MMseqs2 web server, just as implemented in ColabFold.
- Are the pLDDT score and PAE figures available?
- Yes, they will be generated just like the ColabFold.
- Is it possible to predict homooligomers and complexes?
- Yes, the format of input sequence is the same as ColabFold. See
query_sequence:
and its use of ColabFold: AlphaFold2 using MMseqs2.
- Yes, the format of input sequence is the same as ColabFold. See
- Is it possible to create MSA by jackhmmer?
- No, it is not currently supported.
- I want to use multiple GPUs to perform the prediction.
- AlphaFold and ColabFold does not support multiple GPUs. Only One GPU can model your protein.
- I have multiple GPUs. Can I specify to run LocalColabfold on each GPU?
- Use
CUDA_VISIBLE_DEVICES
environment variable. See YoshitakaMo#200.
- Use
- I got an error message
CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered
.- You may not have updated to CUDA 11.8 or later. Please check the version of Cuda compiler with
nvcc --version
command, notnvidia-smi
.
- You may not have updated to CUDA 11.8 or later. Please check the version of Cuda compiler with
- Is this available on Windows 10?
- You can run LocalColabFold on your Windows 10 with WSL2.
- (New!)I want to use a custom MSA file in the format of a3m.
- ColabFold can accept various input files now. See the help messsage. You can set your own A3M file, a fasta file that contains multiple sequences (in FASTA format), or a directory that contains multiple fasta files.
- The original colabfold was first created by Sergey Ovchinnikov (@sokrypton), Milot Mirdita (@milot_mirdita) and Martin Steinegger (@thesteinegger).
- 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 - If you’re using AlphaFold, please also cite:
Jumper et al. "Highly accurate protein structure prediction with AlphaFold."
Nature (2021) doi: 10.1038/s41586-021-03819-2 - If you’re using AlphaFold-multimer, please also cite:
Evans et al. "Protein complex prediction with AlphaFold-Multimer."
BioRxiv (2022) doi: 10.1101/2021.10.04.463034v2