AlphaFold

1. An implementation of AlphaFold-Multimer. This represents a work in progress and AlphaFold-Multimer isn't expected to be as stable as our monomer AlphaFold system. Read the guide for how to upgrade and update code. 2. The technical note containing the models and inference procedure for an updated AlphaFold v2.3.0. 3. A CASP15 baseline set of predictions along with documentation of any manual interventions performed. Any publication that discloses findings arising from using this source code or the model parameters should cite the AlphaFold paper and, if applicable, the AlphaFold-Multimer paper. Please also refer to the Supplementary Information for a detailed description of the method. You can use a slightly simplified version of AlphaFold with community-supported versions (see below). If you have any questions, please contact the AlphaFold team at [email protected]. You will need a machine running Linux, AlphaFold does not support other operating systems. Full installation requires up to 3 TB of disk space to keep genetic databases (SSD storage is recommended) and a modern NVIDIA GPU (GPUs with more memory can predict larger protein structures). Install NVIDIA Container Toolkit for GPU support. Setup running Docker as a non-root user. Please use the script scripts/download all data.sh to download and set up full databases. This may take substantial time (download size is 556 GB), so we recommend running this script in the background: 1. Install the run docker.py dependencies. Note: You may optionally wish to create a Python Virtual Environment to prevent conflicts with your system's Python environment. 1. Make sure that the output directory exists (the default is /tmp/alphafold ) and that you have sufficient permissions to write into it. 1. Once the run is over, the output directory shall contain predicted structures of the target protein. Please check the documentation below for additional options and troubleshooting tips. BFD, MGnify, PDB70, PDB (structures in the mmCIF format), PDB seqres – only for AlphaFold-Multimer, UniRef30 (FKA UniClust30), UniProt – only for AlphaFold-Multimer, UniRef90. will download a reduced version of the databases to be used with the reduced dbs database preset. This shall be used with the corresponding AlphaFold parameter --db preset=reduced dbs later during the AlphaFold run (please see AlphaFold parameters section). We don't provide exactly the database versions used in CASP14 – see the note on reproducibility. Some of the databases are mirrored for speed, see mirrored databases. :ledger: Note: If the download directory and datasets don't have full read and write permissions, it can cause errors with the MSA tools, with opaque (external) error messages. Please ensure the required permissions are applied, e.g. with the sudo chmod 755 --recursive "$DOWNLOAD DIR" command. The download all data.sh script will also download the model parameter files. Once the script has finished, you should have the following directory structure: bfd/ is only downloaded if you download the full databases, and small bfd/ is only downloaded if you download the reduced databases. The AlphaFold parameters are available from and are downloaded as part of the scripts/download all data.sh script. This script will download parameters for: 5 models which were used during CASP14, and were extensively validated for structure prediction quality (see Jumper et al. 2021, Suppl. Methods 1.12 for details). 5 pTM models, which were fine-tuned to produce pTM (predicted TM-score) and (PAE) predicted aligned error values alongside their structure predictions (see Jumper et al. 2021, Suppl. Methods 1.9.7 for details). 5 AlphaFold-Multimer models that produce pTM and PAE values alongside their structure predictions. If you have a previous version you can either reinstall fully from scratch (remove everything and run the setup from scratch) or you can do an incremental update that will be significantly faster b