Installations at TACC

Running AlphaFold

Note: AlphaFold is still being tested for performance and I/O efficiency. These instructions are subject to change.

Single Sequence Prediction

To perform 3-D protein structure prediction with AlphaFold, first upload a fasta-formatted protein primary sequence to your $WORK or $SCRATCH (recommended) space. Sample fasta sequences are provided in the machine-specific "Examples" paths listed in the table above. A valid fasta sequence might look like:

>sample sequence consisting of 350 residues
MTANHLESPNCDWKNNRMAIVHMVNVTPLRMMEEPRAAVEAAFEGIMEPAVVGDMVEYWN
KMISTCCNYYQMGSSRSHLEEKAQMVDRFWFCPCIYYASGKWRNMFLNILHVWGHHHYPR
NDLKPCSYLSCKLPDLRIFFNHMQTCCHFVTLLFLTEWPTYMIYNSVDLCPMTIPRRNTC
RTMTEVSSWCEPAIPEWWQATVKGGWMSTHTKFCWYPVLDPHHEYAESKMDTYGQCKKGG
MVRCYKHKQQVWGNNHNESKAPCDDQPTYLCPPGEVYKGDHISKREAENMTNAWLGEDTH
NFMEIMHCTAKMASTHFGSTTIYWAWGGHVRPAATWRVYPMIQEGSHCQC

Next, prepare a batch job submission script for running AlphaFold. Two different templates for different levels of precision are provided within the "Examples" paths listed in the table above:

• reduced_dbs.slurm: higher speed
• full_dbs.slurm: higher precision (default)

See the AlphaFold documentation for more information on the speed / quality tradeoff of each preset. The example templates each need to be customized before they can be used. Copy the desired template to your $WORK or $SCRATCH space along with the input fasta file. After necessary customizations, a batch script for running the full databases on Frontera may contain:

#!/bin/bash
# -----------------------------------------------------------------
#SBATCH -J my_af2_job                 # Job name
#SBATCH -o my_af2_job.%j.out          # Name of stdout output file
#SBATCH -e my_af2_job.%j.err          # Name of stderr output file
#SBATCH -p rtx                        # Queue (partition) name
#SBATCH -N 1                          # Total # of nodes
#SBATCH -n 1                          # Total # of mpi tasks
#SBATCH -t 12:00:00                   # Run time (hh:mm:ss)
#SBATCH -A myproject                  # Project/Allocation name
# -----------------------------------------------------------------

# Load modules (example path on Frontera)
module unload xalt
module use /scratch2/projects/bio/alphafold/modulefiles
module load alphafold/2.2.0-ctr

# Run AlphaFold
run_alphafold.sh --flagfile=$AF2_HOME/test/flags/full_dbs.ff \
                 --fasta_paths=$SCRATCH/sample.fasta \
                 --output_dir=$SCRATCH/output \
                 --model_preset=monomer \
                 --use_gpu_relax=True

In the batch script, make sure to use a batch queue (#SBATCH -p), node / wallclock limits, and allocation name (#SBATCH -A) appropriate to the machine you are running on. Also, make sure the path shown in the module use line matches the machine-specific "Module" path listed in the table above.

The flagfile is a configuration file passed to AlphaFold containing parameters including the level of precision, the location of the databases for multiple sequence alignment, and more. Flag files for all three presets can be found in the "Examples" directory, and typically they should not be edited. The other three parameters passed to AlphaFold should be customized to your input path / filename, desired output path, and the selection of models. The parameters are summarized in the following table:

Multiple Sequence Predictions

The multiple sequence alignment step of the AlphaFold workflow is exceedingly I/O intensive.
Limit your concurrent AlphaFold processes per node to a maximum of four.

To perform 3-D protein structure prediction with AlphaFold for many protein sequences, we recommend using TACC's launcher or launcher-gpu utility. First review the instructions for submitting single sequence predictions above, then make the following adjustments:

Fasta formatted sequences should be uniquely identifiable either by giving each a unique name or by putting each sequence in its own uniquely-named directory. The simplest way to achieve this is to have one sub directory (e.g. $SCRATCH/inputs/) with all uniquely named fasta sequences in it:

login1$ ls $SCRATCH/inputs/
seq1.fasta
seq2.fasta
seq3.fasta
...

Next, prepare a launcher jobfile that contains each command that needs to be run. There should be one line in the jobfile for each input fasta sequence. Each line should refer to a unique input sequence and a unique output path:

Sample Alphafold Launcher Job File

singularity exec --nv $AF2_HOME/images/alphafold_2.2.0.sif /app/run_alphafold.sh --flagfile=$AF2_HOME/test/flags/full_dbs.ff --fasta_paths=$SCRATCH/input/seq1.fasta --output_dir=$SCRATCH/output1 --model_preset=monomer --use_gpu_relax=True
singularity exec --nv $AF2_HOME/images/alphafold_2.2.0.sif /app/run_alphafold.sh --flagfile=$AF2_HOME/test/flags/full_dbs.ff --fasta_paths=$SCRATCH/input/seq2.fasta --output_dir=$SCRATCH/output2 --model_preset=monomer --use_gpu_relax=True
singularity exec --nv $AF2_HOME/images/alphafold_2.2.0.sif /app/run_alphafold.sh --flagfile=$AF2_HOME/test/flags/full_dbs.ff --fasta_paths=$SCRATCH/input/seq3.fasta --output_dir=$SCRATCH/output3 --model_preset=monomer --use_gpu_relax=True
...

NOTE: Due to the way launcher_gpu distributes tasks to individual GPUs, the full singularity command must be used in the jobfile as shown above.