Configure:
FASTA Sequence Paste or load a sequence, with or without header. One may insert path to a file or load from disk.
Submit and Analysis (Buttons)
# QwikFold plugin v 0.7 The QwikFold plugin provides a graphical user interface for AlphaFold, making it easier to perform protein folding predictions from within VMD. QwikFold allow predictions both Monomer and Multimer, and provides quick access to model quality metrics. ## Software Requirements: To start jobs from the QwikFold plugin, AlphaFold and its companion software must be installed locally. One should follow the Step-by-Step intructions bellow to properly install a local copy. ## Download Requirements Alphafold uses large sequence databases for genetic search. The download scripts are included in the github distribution. Total: ~ 2.2 TB (download: 438 GB) ## Using the Plugin. In addition to the FASTA sequence to model, one should provide a Job ID, paths to alphafold installation, downloaded databases, and path to outputs. ### Configure: Job ID: Unique identifier for the job. Path : Path to AlphaFold cloned from GitHub. Databases: Path to sequence databases downloaded for the genetic search Output folder: Path to output results ### FASTA Sequence Paste or load a sequence, with or without header. One may insert path to a file or load from disk. ## Submit and Analysis (Buttons) "Read config" - not implemented. "Write config" - Write a script to run alphafold "FOLD !" - Run alphafold "Load Models" - Load all generated models "Align Models" - Perform structural alignment of loaded models ## Instructions to install AlphaFold locally. Follow this Step-by-Step guide to installing alphafold using the conda environment ### Step 1 - Create a new, clean conda environment. conda create -n alphafold python=3.8 -y --no-default-packages ### Step 2 - activate the environment conda activate alphafold ### Step 3 - Clone alphafold from it's github repository git clone https://github.com/deepmind/alphafold ### Step 4 - Go to AlphaFold folder and export alphafold_path cd alphafold export ALPHAFOLD_PATH=$PWD ### Step 5 - Download chemical properties to the "common" folder wget -q -P alphafold/common/ https://git.scicore.unibas.ch/schwede/openstructure/-/raw/7102c63615b64735c4941278d92b554ec94415f8/modules/mol/alg/src/stereo_chemical_props.txt ### Step 6 - Install required software for AlphaFold 2 (follow this order) # Step 6.1 - AlphaFold requirements using conda conda install -y -c nvidia cudnn==8.0.4 conda install -y -c conda-forge openmm=7.5.1 pdbfixer=1.7 pip conda install -y -c bioconda hmmer=3.3.2 hhsuite==3.3.0 kalign2=2.04 # Step 6.2 - AlphaFold requirements using PIP. pip3 install --upgrade pip pip3 install -r ./requirements.txt # Step 6.3 - QwikFold requirements using conda. conda install -y matplotlib ### Step 7 - Instal JAX. Adjust compatibility to CUDA drivers on local machine. pip3 install --upgrade "jax[cuda111]" jaxlib==0.1.72+cuda111 -f https://storage.googleapis.com/jax-releases/jax_releases.html ### Step 8 - Apply patches # Step 8.1 Patches to OpenMM python_path=$(which python) cd $(dirname $(dirname ${python_path}))/lib/python3.8/site-packages patch -p0 < ${ALPHAFOLD_PATH}/docker/openmm.patch cd ${ALPHAFOLD_PATH} # Step 8.2 - Apply QwikFold Patches to AlphaFold. # Step 9.2.2 - Fix path issue for stereo_chemical_props.txt # patch -u ${ALPHAFOLD_PATH}/alphafold/common/residue_constants.py -i /usr/local/lib/vmd/plugins/noarch/tcl/qwikfold0.6/patches/residue_constants.py.patch # Step 8.2.3 (optional) - Set CUDA as openMM platform. patch -u ${ALPHAFOLD_PATH}/alphafold/relax/amber_minimize.py -i /usr/local/lib/vmd/plugins/noarch/tcl/qwikfold0.6/patches/amber_minimize.py.patch ### Step 9 - Install alphafold to your conda environment. cd ${ALPHAFOLD_PATH} python setup.py install # You're all set ! ################################ # Download AlphaFold databases # ################################ # Set path to dowload databases ALPHAFOLD_DATA=/home/user/alphafold_dbs/ # Go to Alphafold folder cd ${ALPHAFOLD_PATH} # Run script to download the databases bash scripts/download_all_data.sh ${ALPHAFOLD_DATA} reduced_dbs # or Download Alphafold full datasets ( ~2.2Tb ) bash scripts/download_all_data.sh ${ALPHAFOLD_DATA}