Changes between Version 3 and Version 4 of SOPs/AlphaFoldMultimer

Timestamp:

04/23/24 07:12:32 (9 months ago)

Author:

twhitfie

Comment:

--

SOPs/AlphaFoldMultimer

@@ -6 +6 @@
 === Running AlphaFold-Multimer using ChimeraX ===
 
-As with structure prediction for monomeric proteins, [https://www.cgl.ucsf.edu/chimerax/ ChimeraX] is a good [https://www.youtube.com/watch?v=6lXeCPuTePs starting point due to its intuitive 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.
+As with structure prediction for monomeric proteins, [https://www.cgl.ucsf.edu/chimerax/ ChimeraX] is a good [https://www.youtube.com/watch?v=6lXeCPuTePs starting point due to its intuitive 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 [https://www.nature.com/articles/s41592-022-01488-1 ColabFold] implementation of AlphaFold, which uses [https://www.nature.com/articles/nbt.3988 MMseqs2] to efficiently compute an initial multiple sequence alignment (MSA).
 
 === 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:
+It may happen that the freely available computational resources accessed via ChimeraX are a constraint on completing your AlphaFold-Multimer predictions.  In that case, there are multiple ways that you can make the predictions locally.  One way is to make use of the ColabFold implementation of AlphaFold-Multimer, which makes use of MMseqs2 from the initial MSA step.
  
+{{{
+sbatch 
+}}}
+
+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 that is submitted to the SLURM scheduler might look like:
+
+{{{
+#!/bin/bash
+
+#SBATCH --job-name=AFbatch
+#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-A6000-20 # partition (queue) to use
+#SBATCH --output AFbatch1.5.5.out # write output to file.
+#SBATCH --gres=gpu:1 # Required for GPU access
+
+export PATH="/nfs/apps/test/colab155test/localcolabfold/colabfold-conda/bin:$PATH"
+
+workpath=/lab/BaRC_projects/labs/Gehring_Lab/Carly_Martin_ProteinComplex_Feb_2024
+cd ${workpath}
+
+colabfold_batch --msa-mode mmseqs2_uniref_env --model-type alphafold2_multimer_v3 --rank multimer fasta/RALF23_FERONIA_complex.fa RALF23_FERONIA_CF_complex
+
+#!/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
+}}}
+
+The following instructions allow you to run AlphaFold-Multimer locally without using ColabFold:
+
 {{{
 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