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.
setup_databases.sh script to download and build the databases (See also ColabFold Downloads). An instruction to run colabfold_search to obtain the MSA and templates locally is written in this comment.localcolabfold from colabfold_batch to distinguish it from the execution command.update_linux.sh. See also How to update. Please use a new option --use-gpu-relax if GPU relaxation is required (recommended).Make sure curl, git, and wget commands are already installed on your PC. If not present, you need install them at first. For Ubuntu, type sudo 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_0DO NOT use
nvidia-smi to check the version.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 add
PATH to it.
Download install_colabbatch_linux.sh from this repository:
$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_linux.shand run it in the directory where you want to install:
$ bash install_colabbatch_linux.shAbout 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 zshIt 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 the
outputdir. 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 is auto (use alphafold2_ptm for monomers and alphafold2_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 enableReplace 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 SiliconPlease use the correct installer for your Mac.
$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
wget, gnu-sed, HH-suite and kalign using Homebrew:$ brew install wget gnu-sed
\$ brew install brewsci/bio/hh-suite brewsci/bio/kalign
install_colabbatch_intelmac.sh from this repository:$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_intelmac.shand run it in the directory where you want to install:
$ bash install_colabbatch_intelmac.shAbout 5 minutes later,
colabfold_batch directory will be created. Do not move this directory after the installation.Note: This installer is experimental because most of the dependent packages are not fully tested on Apple Silicon Mac.
$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
$ brew install wget cmake gnu-sed
$ brew install brewsci/bio/hh-suite
$ brew install brewsci/bio/kalign
miniforge command using Homebrew:$ brew install --cask miniforge
install_colabbatch_M1mac.sh from this repository:$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_M1mac.shand run it in the directory where you want to install:
$ bash install_colabbatch_M1mac.shAbout 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.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 toIt 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
CAYKTTQANKHIIVACEGNPYVPVHFDASVFor prediction of multimers, insert : between the protein sequences.
>1BJP_homohexamer
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR:
PIAQIHILEGRSDEQKETLIREVSEAISRSLDAPLTSVRVIITEMAKGHFGIGGELASKVRR>3KUD_RasRaf_complex
MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQ
YMRTGEGFLCVFAINNTKSFEDIHQYREQIKRVKDSDDVPMVLVGNKCDLAARTVESRQAQDLARSYGIP
YIETSAKTRQGVEDAFYTLVREIRQH:
PSKTSNTIRVFLPNKQRTVVNVRNGMSLHDCLMKALKVRGLQPECCAVFRLLHEHKGKKARLDWNTDAAS
LIGEELQVDFLMultiple > 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:MPYTVRFTTTARRDLHKLPPRILAAVVEFAFGDLSREPLRVGKPLRRELAGTFSARRGTYRLLYRIDDEHTTVVILRVDHRADIYRRYou 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 is 3. (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 https://github.com/sokrypton/ColabFold/issues/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.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 execute the following:
# set your OS. Select one of the following variables {linux,intelmac,M1mac}
$ OS=linux # if Linux
# navigate to the directory where you installed localcolabfold, e.g.
$ cd /home/moriwaki/Desktop/localcolabfold/
# 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 .curl and wget commands.query_sequence: and its use of ColabFold: AlphaFold2 using MMseqs2.CUDA_VISIBLE_DEVICES environment variable. See https://github.com/YoshitakaMo/localcolabfold/issues/200.CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered.
nvcc --version command, not nvidia-smi.