ColabFold on your local PC (or macOS). See also ColabFold repository.

• 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.

1. 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.
2. Make sure your Cuda compiler driver is 11.1 or later:

   $ nvcc --version
   nvcc: NVIDIA (R) Cuda compiler driver
   Copyright (c) 2005-2020 NVIDIA Corporation
   Built on Mon_Oct_12_20:09:46_PDT_2020
   Cuda compilation tools, release 11.1, V11.1.105
   Build cuda_11.1.TC455_06.29190527_0
   

   DO NOT use nvidia-smi to check the version.
   See NVIDIA CUDA Installation Guide for Linux if you haven't installed it.
3. Make sure your GNU compiler version is 4.9 or later because GLIBCXX_3.4.20 is required:

   $ 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 4.8.5 or older (e.g. CentOS 7), install a new one and add PATH to it.
4. 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, colabfold_batch directory will be created. Do not move this directory after the installation.
5. Add environment variable PATH:

   # For bash or zsh
   # e.g. export PATH="/home/moriwaki/Desktop/colabfold_batch/bin:$PATH"
   export PATH="<COLABFOLDBATCH_DIR>/bin:$PATH"

6. To run the prediction, type

   colabfold_batch --amber --templates --num-recycle 3 inputfile outputdir/ 

   The result files will be created in the outputdir. For more details, see colabfold_batch --help.

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.

1. Install Homebrew if not present:

   $ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

2. Install wget, gnu-sed, HH-suite and kalign using Homebrew:

   $ brew install wget gnu-sed
   $ brew install brewsci/bio/hh-suite brewsci/bio/kalign

3. 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.
4. 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.

1. Install Homebrew if not present:

   $ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

2. Install several commands using Homebrew (currently kalign can't be installed, but no effects):

   $ brew install wget cmake gnu-sed
   $ brew install brewsci/bio/hh-suite

3. Install miniforge command using Homebrew:

   $ brew install --cask miniforge

4. 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.
5. The rest procedure is the same as "For Linux".

A Warning message appeared when you run the prediction:

You are using an experimental build of OpenMM v7.5.1.
This is NOT SUITABLE for production!
It has not been properly tested on this platform and we cannot guarantee it provides accurate results.

This message is due to Apple Silicon, but I think we can ignore it.

Because ColabFold is still a work in progress, the localcolabfold should be also updated frequently to use the latest features. I will provide an easy-to-use update script.

To update your localcolabfold, simply type in the colabfold_batch directory:

$ ./update_linux.sh . # if Linux
$ ./update_intelmac.sh . # if Intel Mac
$ ./update_M1mac.sh . # if M1 Mac

Or, if you have already installed localcolabfold before, please download the updater from this repository and execute it.

# set your OS. Select one of the following variables {linux,intelmac,M1mac}
$ OS=linux # if Linux
$ wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/update_${OS}.sh
$ chmod +x update_${OS}.sh
$ ./update_${OS}.sh /path/to/your/colabfold_batch

• 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.

• What else do I need to do before installation? Do I need sudo privileges?
  • No, except for installation of curl and wget commands.
• 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.
• 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 got an error message CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered.
  • You may not have updated to CUDA 11.1 or later. Please check the version of Cuda compiler with nvcc --version command, not nvidia-smi.
• 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.

• ColabFold Tutorial presented at the Boston Protein Design and Modeling Club. [video] [slides].

• 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.
  bioRxiv (2021) doi: 10.1101/2021.08.15.456425
• 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 (2021) doi: 10.1101/2021.10.04.463034v1
• If you are using RoseTTAFold, please also cite:
  Minkyung et al. "Accurate prediction of protein structures and interactions using a three-track neural network."
  Science (2021) doi: 10.1126/science.abj8754