Predicting protein structure from sequence using AlphaFold
Background
The success of DeepMind's AlphaFold protein folding algorithm in the CASP14 structural prediction assessment has been widely celebrated and has profoundly invigorated the structural biology community. Today, if you have a protein sequence for which you'd like to learn a high quality predicted structure, an excellent place to start is the AlphaFold Protein Structure Database. An alternative database to search is the ESM Metagenomic Atlas, where you may find predicted structures for orphan proteins with few sequence homologs.
Running AlphaFold using ChimeraX
If you cannot find a predicted structure for your protein within the databases listed above, perhaps because amino acid substitutions relative to the reference sequence are present, ChimeraX is an easy place to start due to its graphical user interface and convenient visualization tools. You will need to install ChimeraX on a desktop or laptop computer, but the AlphaFold predictions will be made using computing resources in the cloud via the ColabFold implementation of AlphaFold, which uses MMseqs2 to efficiently compute an initial multiple sequence alignment.
Running AlphaFold using computing resources at Whitehead
It may happen that the freely available computational resources accessed via ChimeraX are a constraint on completing your AlphaFold predictions. In that case, you can make the predictions locally using a command like the following:
sbatch --export=ALL,FASTA_NAME=example.fa,USERNAME='user',FASTA_PATH=/path/to/fasta/file,AF2_WORK_DIR=/path/to/working/directory ./RunAlphaFold_2.3.2_slurm.sh
In the command above, substitute your own user id, fasta file and the paths to both the fasta file and the working directory. In this example, the job (named RunAlphaFold_2.3.2_slurm.sh above) that is submitted to the SLURM scheduler might look like:
#!/bin/bash
#SBATCH --job-name=AF2 # friendly name for job.
#SBATCH --nodes=1 # ensure cores are on one node
#SBATCH --ntasks=1 # run a single task
#SBATCH --cpus-per-task=8 # number of cores/threads requested.
#SBATCH --mem=64gb # memory requested.
#SBATCH --partition=nvidia-t4-20 # partition (queue) to use
#SBATCH --output output-%j.out # %j inserts jobid to STDOUT
#SBATCH --gres=gpu:1 # Required for GPU access
export TF_FORCE_UNIFIED_MEMORY=1
export XLA_PYTHON_CLIENT_MEM_FRACTION=4
export OUTPUT_NAME='model_1'
export ALPHAFOLD_DATA_PATH='/alphafold/data.2023b' # Specify ALPHAFOLD_DATA_PATH
cd $AF2_WORK_DIR
singularity run -B $AF2_WORK_DIR:/af2 -B $ALPHAFOLD_DATA_PATH:/data -B .:/etc --pwd /app/alphafold --nv /alphafold/alphafold_2.3.2.sif --data_dir=/data/ --output_dir=/af2/$FASTA_PATH --fasta_paths=/af2/$FASTA_PATH/$FASTA_NAME --max_template_date=2050-01-01 --db_preset=full_dbs --bfd_database_path=/data/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt --uniref30_database_path=/data/uniref30/UniRef30_2023_02 --uniref90_database_path=/data/uniref90/uniref90.fasta --mgnify_database_path=/data/mgnify/mgy_clusters_2022_05.fa --template_mmcif_dir=/data/pdb_mmcif/mmcif_files --obsolete_pdbs_path=/data/pdb_mmcif/obsolete.dat --use_gpu_relax=True --model_preset=monomer --pdb70_database_path=/data/pdb70/pdb70
# Email the STDOUT output file to specified address.
/usr/bin/mail -s "$SLURM_JOB_NAME $SLURM_JOB_ID" $USERNAME@wi.mit.edu < $AF2_WORK_DIR/output-${SLURM_JOB_ID}.out
