Comprehensive Guide to Installing and Configuring AlphaFold 3 Using Conda Python 3.11 Environment

This guide details the necessary steps to install and configure AlphaFold 3 using a conda Python 3.11 environment. It includes the installation of Miniconda, environment creation, dependency installation, repository cloning, model configuration, and the preparation of an execution script. It has been designed to rely solely on conda, without the need for installing any additional packages on the operating system, making it possible to perform this installation on any operating system that supports conda. It is assumed that the appropriate graphics drivers for your Linux distribution are correctly installed. For detailed instructions, please refer to the mini-tutorial.

0. Install Miniconda

Download and install Miniconda (for x86_64 architecture):

# Download the Miniconda installer
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh

# Run the installer
bash miniconda.sh

# Source the .bashrc to update your environment
source ~/.bashrc

Note: During installation, you can accept the default settings or customize them according to your preferences.

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1. Create a Conda Environment with Python 3.11

Create and activate a new conda environment named Alphafold3 with Python 3.11:

# Create the environment
conda create -n Alphafold3 python=3.11

# Activate the environment
conda activate Alphafold3

Note: During installation and execution of AlphaFold 3, the AlphaFold3 Conda environment needs to be activated.

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2. Install Development Tools and Dependencies

Install the necessary development tools and dependencies within the conda environment, not on the system OS. Note that HMMER is installed using conda and does not require independent installation or compilation, although there may be tools that cannot be installed in this way.

# Install cmake, gcc, and gxx
conda install -c conda-forge cmake gcc gxx

# Install Boost for Python 3.11 and numpy
conda install -c conda-forge boost boost-cpp numpy  # Boost for Python 3.11

# Install compression libraries
conda install -c conda-forge bzip2 zstd

# Install git and zlib
conda install -c conda-forge git zlib

# Install HMMER
conda config --add channels bioconda
conda install -c bioconda hmmer

# Install and Upgrade pip within the Alphafold3 environment
conda install pip
pip install --upgrade pip  # Update pip (specific to the AF3 environment)

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3. Install Required Python Packages with pip

Install the necessary Python packages using pip:

pip install pandas==2.2.3 matplotlib==3.10.0 absl-py==2.1.0 chex==0.1.87 \
    dm-haiku==0.0.13 dm-tree==0.1.8 filelock==3.16.1 \
    "jax[cuda12]==0.4.34" jax-cuda12-pjrt==0.4.34 jax-triton==0.2.0 \
    jaxlib==0.4.34 jaxtyping==0.2.34 jmp==0.0.4 ml-dtypes==0.5.0 \
    numpy==2.1.3 nvidia-cublas-cu12==12.6.3.3 \
    nvidia-cuda-cupti-cu12==12.6.80 nvidia-cuda-nvcc-cu12==12.6.77 \
    nvidia-cuda-runtime-cu12==12.6.77 nvidia-cudnn-cu12==9.5.1.17 \
    nvidia-cufft-cu12==11.3.0.4 nvidia-cusolver-cu12==11.7.1.2 \
    nvidia-cusparse-cu12==12.5.4.2 nvidia-nccl-cu12==2.23.4 \
    nvidia-nvjitlink-cu12==12.6.77 opt-einsum==3.4.0 pillow==11.0.0 \
    rdkit==2024.3.5 scipy==1.14.1 tabulate==0.9.0 toolz==1.0.0 \
    tqdm==4.67.0 triton==3.1.0 typeguard==2.13.3 \
    typing-extensions==4.12.2 zstandard==0.23.0

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4. Install AlphaFold 3

4.1 Clone the AlphaFold 3 Repository

Define your desired installation location and clone the official repository:

# Set the desired application directory
export APPDIR="/home/user/Programs"  # Replace "/home/user/Programs" with your desired path

# Create the directory and navigate to it
mkdir -p $APPDIR
cd $APPDIR

# Clone the AlphaFold 3 repository
git clone https://github.com/google-deepmind/alphafold3.git

# Define the AlphaFold 3 directory variable
export ALPHAFOLD3DIR="$APPDIR/alphafold3"
cd ${ALPHAFOLD3DIR}

Important: Use setenv instead of export in C shell (csh/tcsh)

4.2 Download the Databases

The fetch_databases.sh script downloads the necessary databases for AlphaFold 3. By default, it downloads them to your home directory. To change the download location to ${ALPHAFOLD3DIR}, modify the script:

# Modify the download path in the script
sed -i 's|$HOME|$ALPHAFOLD3DIR|g' fetch_databases.sh

# Make the script executable
chmod +x fetch_databases.sh

# Run the script to download the databases
./fetch_databases.sh

Important: Ensure you have sufficient disk space, as the databases are quite large (~627GB).
If you use C shell (csh/tcsh), run the fetch_databases.csh script included in the repository.

4.3 Obtain Model Parameters and Place Them in models

You need to request access to the AlphaFold 3 model parameters:

1. Complete the official request form provided by the AlphaFold team.
2. Access will be granted at Google DeepMind’s sole discretion. They aim to respond to requests within 2–3 business days. You may only use AlphaFold 3 model parameters if received directly from Google. Use is subject to these terms of use.
3. Once approved, you will receive a download link for af3.bin.zst.

Proceed to decompress and move the model files:

# Download the model parameters (replace <your_download_url>)
wget <your_download_url>

# Move the compressed model to the models directory
mv af3.bin.zst ${ALPHAFOLD3DIR}/models/

# Navigate to the models directory
cd ${ALPHAFOLD3DIR}/models/

# Decompress the model
unzstd af3.bin.zst

4.4 Install AlphaFold 3 from the Repository

There might be issues with zlib not linking correctly. Use the following environment variables to resolve this:

cd ${ALPHAFOLD3DIR}

# Export paths for zlib
export CXXFLAGS="-I$(dirname $(find ${CONDA_PREFIX} -name zlib.h | head -n 1))"
export LDFLAGS="-L$(dirname $(find ${CONDA_PREFIX} -name libz.so | head -n 1)) -lz"

# Install AlphaFold 3 without additional dependencies
python -m pip install --no-deps .

4.5 Build Additional Components

Compile the necessary components from the conda environment's bin directory:

cd ${CONDA_PREFIX}/bin

# Execute the build script
./build_data  # Execute

4.6 Test the Installation

Verify that the installation was successful by displaying the help message:

cd ${ALPHAFOLD3DIR}

# Display the help message
python run_alphafold.py --help

4.7 Make run_alphafold.py Executable from Anywhere

To run run_alphafold.py from any location, add the shebang line with the path to Python from your conda environment and make it executable:

# Add the shebang line to the script
sed -i '1s|^|#!'"$(which python)"'\n|' run_alphafold.py

# Make the script executable
chmod +x run_alphafold.py

Create a symbolic link to the script in your conda environment's bin directory:

ln -s ${PWD}/run_alphafold.py ${CONDA_PREFIX}/bin/run_alphafold.py

Now, when your environment is active, you can run run_alphafold.py from any location.

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5. Create an Execution Script: AF3_run.sh

Once you have installed AlphaFold 3, go to your working directory and test the AlphaFold 3 run using, for example, the following input JSON file named fold_input.json:

{
  "name": "2PV7",
  "sequences": [
    {
      "protein": {
        "id": ["A", "B"],
        "sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
      }
    }
  ],
  "modelSeeds": [1],
  "dialect": "alphafold3",
  "version": 1
}

Create a script called AF3_run.sh with the following content to facilitate running AlphaFold 3:

#!/bin/bash

APPDIR="/home/user/Programs"  # Replace with your actual path if different
ALPHAFOLD3DIR="$APPDIR/alphafold3"
#HMMER3_BINDIR="/usr/bin" # Path to HMMER binaries (**installed via OS package manager or specify your path**)
HMMER3_BINDIR="${CONDA_PREFIX}/bin/" # Path to Conda binarys (**installed via conda**)
DB_DIR="${ALPHAFOLD3DIR}/public_databases"
MODEL_DIR="${ALPHAFOLD3DIR}/models"
WORK_DIR=$(pwd)
OUTPUT_DIR="${WORK_DIR}/output/${BASE_NAME}"
LOG_FILE="${OUTPUT_DIR}/af3_run.log"
JSON_FILE=$(ls -1 *.json 2>/dev/null | head -n 1)

run_alphafold.py \
    --jackhmmer_binary_path="${HMMER3_BINDIR}/jackhmmer" \
    --nhmmer_binary_path="${HMMER3_BINDIR}/nhmmer" \
    --hmmalign_binary_path="${HMMER3_BINDIR}/hmmalign" \
    --hmmsearch_binary_path="${HMMER3_BINDIR}/hmmsearch" \
    --hmmbuild_binary_path="${HMMER3_BINDIR}/hmmbuild" \
    --db_dir="${DB_DIR}" \
    --model_dir="${MODEL_DIR}" \
    --json_path="${WORK_DIR}/${JSON_FILE}" \
    --output_dir="${OUTPUT_DIR}" \
    --buckets="256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120" \
    2>&1 | tee -a "${LOG_FILE}"

Important: Use set in C shell (csh/tcsh) to initialize APPDIR, ALPHAFOLD3DIR,HMMER3_BINDIR, DB_DIR, MODEL_DIR, WORK_DIR, OUTPUT_DIR and LOG_FILE JSON_FILE. e.g. set APPDIR="/home/user/Programs"

Make the script executable:

chmod +x AF3_run.sh

Now, with your conda environment active and in the appropriate working directory, you can run AlphaFold 3 simply by executing:

./AF3_run.sh

Additional Notes

• Use the AF3_run.sh script anywhere on the system.

      cp AF3_run.sh ${CONDA_PREFIX}/bin/
      chmod +x ${CONDA_PREFIX}/bin/AF3_run.sh
  

• Hardware Requirements: To run AlphaFold 3 on systems with limited resources, a minimum of an Amper NVIDIA GPU with 8 o 12 GB of VRAM. However, for optimal performance, it's recommended to use professional GPUs like the NVIDIA A100, H100 or high-end consumer GPUs such as the RTX 3090, 4090, or the latest 5090, as these offer superior memory and processing capabilities that significantly enhance the efficiency of running AlphaFold 3.

CUDA and NVIDIA Drivers

Verify that you have the correct versions of CUDA and NVIDIA drivers that match the installed nvidia packages.
It is recommended to install the latest graphics card driver, particularly the one provided by each distribution, especially when installing on Linux.

For instance, in Debian-like distributions:

sudo apt-get install nvidia-driver-565
sudo reboot

In the case of a RedHat/CentOS-based distribution, one needs to add the Nvidia repository:

sudo yum install epel-release
sudo yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
sudo yum install nvidia-driver-565
sudo reboot

Other Considerations

• Disk Space: The databases (627 GB), the models (1.1 GB), and the Conda environment (6.7 GB) require significant disk space. Make sure you have at least 700 GB of free space available. For optimal performance, a dedicated 1 TB NVMe drive is recommended.
• Updates and Support: Regularly check the official AlphaFold 3 repository for updates and potential changes to dependencies.

If you encounter any issues during the installation or execution process, feel free to ask for additional assistance.

Acknowledgment:

This work was depeloped by Hiiragizawa and inspired by the Alphafold3-Fedora-install repository by ullahsamee, originally developed for Fedora. We have adapted it to enhance its general applicability.