# Scientific Software Scientific applications available on the cluster. # AlphaFold AlphaFold is an AI program developed by DeepMind that predicts protein structures from amino acid sequences. ## Databases The necessary databases are pre-mounted on GPU nodes at `/alphafold_storage/alphafold_db` — no download needed. ## Required Parameters

Parameter	Description
`-d `	Path to the supporting data directory
`-o `	Path to store results
`-f `	Path to FASTA file(s). Multiple sequences in one file = multimer. Multiple files comma-separated = fold sequentially
`-t `	Maximum template release date (YYYY-MM-DD)

## Optional Parameters

Parameter	Default	Description
`-g`	true	Enable NVIDIA GPU runtime
`-r`	true	Run final relaxation step
`-e`	true	Run relax on GPU
`-n`	all cores	OpenMM threads
`-a`	0	CUDA\_VISIBLE\_DEVICES — comma-separated GPU list
`-m`	monomer	Model preset: `monomer`, `monomer_casp14`, `monomer_ptm`, `multimer`
`-c`	full\_dbs	MSA database preset: `reduced_dbs` or `full_dbs`
`-p`	false	Use precomputed MSAs from disk
`-l`	5	Predictions per model (multimer only)
`-b`	false	Benchmark mode — excludes compilation time

## Example Job Script ```bash #!/bin/bash #SBATCH --job-name=AlphaFold-Multimer #SBATCH --partition=gpu2 #SBATCH --nodes=1 #SBATCH --ntasks=1 #SBATCH --cpus-per-task=4 #SBATCH --mem=32G #SBATCH --gres=gpu:1 #SBATCH --output=alphafold_%j.out #SBATCH --error=alphafold_%j.err module load alphafold/alphafold_non_docker_2.3.1 bash $ALPHAFOLD_SCRIPT_PATH/run_alphafold.sh \ -d $ALPHAFOLD_DB_PATH \ -o ~/output_dir \ -f $ALPHAFOLD_SCRIPT_PATH/examples/query.fasta \ -t $(date +%Y-%m-%d) ``` ## Memory Guidelines - **Monomer** — at least 32GB RAM - **Multimer** — at least 64GB RAM; large/complex structures may need 128GB+ ## Additional Resources - Sample FASTA: `/home/alphafold_folder/alphafold_multimer_non_docker/example/query.fasta` - [alphafold\_non\_docker GitHub repository](https://github.com/kalininalab/alphafold_non_docker) # AlphaFold3 AlphaFold3 runs via Singularity/Apptainer container on GPU nodes with the `af3` GRES constraint. ## Loading the Module ```bash module load alphafold3 ``` This automatically loads Apptainer and sets the following environment variables:

Variable	Description
`$AF3_CONTAINER`	Path to the Singularity container
`$AF3_MODELS`	Path to model parameters
`$AF3_DB`	Path to the database
`$AF3_SRC`	Path to AlphaFold3 source directory

## Bind Mounts Singularity requires bind mounts to expose host directories inside the container: ```bash --bind /path/on/host:/path/inside/container ``` Your input folder can be bound to any path inside the container — `/root/af_input` is not required, it's just an example. ## Running AlphaFold3 ```bash singularity exec --nv \ --bind $AF3_SRC:/root/custom_folder \ --bind /tmp:/root/af_output \ --bind $AF3_MODELS:/root/models \ --bind $AF3_DB:/root/public_databases \ --bind /home/user/alphafold_inputs:/root/custom_folder \ $AF3_CONTAINER \ python /root/custom_folder/run_alphafold.py \ --json_path=/root/custom_folder/fold_input.json \ --model_dir=/root/models \ --db_dir=/root/public_databases \ --output_dir=/root/af_output ``` Replace `/home/user/alphafold_inputs` with the actual path to your input folder. For input file format, see the [AlphaFold3 Input File Guide](https://github.com/google-deepmind/alphafold3/blob/main/docs/input.md). ## Listing All Available Flags ```bash singularity exec --nv \ --bind $AF3_SRC:/root/custom_folder \ --bind /tmp:/root/af_output \ --bind $AF3_MODELS:/root/models \ --bind $AF3_DB:/root/public_databases \ $AF3_CONTAINER \ python /root/custom_folder/run_alphafold.py --helpfull ``` ## Example Job Script ```bash #!/bin/bash #SBATCH --job-name=alphafold3 #SBATCH --partition=gpu-general #SBATCH --gres=gpu:1,af3 #SBATCH --cpus-per-task=8 #SBATCH --mem=64G #SBATCH --time=1-00:00:00 #SBATCH --output=alphafold3_%j.out #SBATCH --error=alphafold3_%j.err module load alphafold3 singularity exec --nv \ --bind $AF3_SRC:/root/custom_folder \ --bind /tmp:/root/af_output \ --bind $AF3_MODELS:/root/models \ --bind $AF3_DB:/root/public_databases \ --bind /home/user/alphafold_inputs:/root/custom_folder \ $AF3_CONTAINER \ python /root/custom_folder/run_alphafold.py \ --json_path=/root/custom_folder/fold_input.json \ --model_dir=/root/models \ --db_dir=/root/public_databases \ --output_dir=/root/af_output ``` ## Unloading ```bash module unload alphafold3 ``` This also unloads the Apptainer module. ## Troubleshooting - Module not found: `module avail alphafold3` to check the exact name - No nodes available: `sinfo -o "%N %G"` to check GPU node availability - Container fails: verify `$AF3_CONTAINER`, `$AF3_MODELS`, `$AF3_DB` are set correctly after module load - Input files not found: confirm the correct host directory is bound and paths are referenced from inside the container # RELION RELION is a cryo-EM structure determination package. On the TAU HPC cluster it runs on the dedicated `gpu-relion` partition with X11 forwarding. ## Requirements - Access to the `gpu-relion-users_v2` account — contact HPC support if you don't have it - SSH connection with X11 forwarding enabled: `ssh -X username@slurmlogin.tau.ac.il` ## Starting a RELION Session Start an interactive job on the GPU RELION partition with X11: ```bash srun --ntasks=1 -p gpu-relion-pool -A gpu-relion-users_v2 --qos=owner --x11 --pty bash ``` Load the RELION module: ```bash module load relion/relion-4.0.1 ``` Launch RELION: ```bash relion ``` ## Notes - RELION requires X11 — make sure your SSH connection was made with `-X` or `-Y` - For batch processing pipelines, RELION can submit its own Slurm jobs from within the GUI - For access or issues contact