ESMFold is another class of prediction structure based on a Protein Language Model (PLM). It doesn't require any multiple sequence alignment and use solely the sequence of the protein of interest. It was developed by Meta (a.k.a Facebook).
It use the v1.0.3 available from the Github repository https://github.com/facebookresearch/esm
To know more about ESMFold, I highly recommend to read:
The installation follow the same process of AlphaFold.
It's available on nodes node061, node062, node063 and node081
The installation requires several Python packages to install. A conda environment esmfold was created for this purpose.
Use the same queues as alphafold: alphafold or alphafold2
See here: http://www-lbt.ibpc.fr/wiki/doku.php?id=cluster-lbt:extra-tools:alphafold_tool#queues
Since the main limitations of ESMFold is the GPU memory, you should always use half of the node for the predictions.
ESMFold support only a fasta file.
For monomer predictions, you can give a multifasta and sequences will be treated as batch (one after another).
For multimeres predictions, you need to supply a fasta file filled as a single sequence, with chains separated by a “:” character.
ESMFold use a lot of the GPU Memory for the predictions (like Omegafold):
The GPU installed in nodes 6X have ~10Gb memory and the ones in the node81 have ~48Gb.
However, ESMFold has an option to decrease the memory used (and thus increase the prediction time) called –chunk-size .
A script is available called esmfold_inference.py:
(esmfold) [santuz@node081 ~]$ esmfold_inference.py -h usage: esmfold_inference.py [-h] -i FASTA -o PDB [--num-recycles NUM_RECYCLES] [--max-tokens-per-batch MAX_TOKENS_PER_BATCH] [--chunk-size CHUNK_SIZE] [--cpu-only] [--cpu-offload] optional arguments: -h, --help show this help message and exit -i FASTA, --fasta FASTA Path to input FASTA file -o PDB, --pdb PDB Path to output PDB directory --num-recycles NUM_RECYCLES Number of recycles to run. Defaults to number used in training (4). --max-tokens-per-batch MAX_TOKENS_PER_BATCH Maximum number of tokens per gpu forward-pass. This will group shorter sequences together for batched prediction. Lowering this can help with out of memory issues, if these occur on short sequences. --chunk-size CHUNK_SIZE Chunks axial attention computation to reduce memory usage from O(L^2) to O(L). Equivalent to running a for loop over chunks of of each dimension. Lower values will result in lower memory usage at the cost of speed. Recommended values: 128, 64, 32. Default: None. --cpu-only CPU only --cpu-offload Enable CPU offloading
You can find below an example of a submission script to perform Omegafold computations.
Script version 21/11/2022
#!/bin/bash #PBS -S /bin/bash #PBS -N ESMFold #PBS -o $PBS_JOBID.out #PBS -e $PBS_JOBID.err #Half node always #PBS -l nodes=1:ppn=8 #PBS -l walltime=24:00:00 #PBS -A simlab_project #PBS -q alphafold_hn #script version 21.11.2022 ### FOR EVERYTHING BELOW, I ADVISE YOU TO MODIFY THE USER-part ONLY ### WORKDIR="/" NUM_NODES=$(cat $PBS_NODEFILE|uniq|wc -l) if [ ! -n "$PBS_O_HOME" ] || [ ! -n "$PBS_JOBID" ]; then echo "At least one variable is needed but not defined. Please touch your manager about." exit 1 else if [ $NUM_NODES -le 1 ]; then WORKDIR+="scratch/" export WORKDIR+=$(echo $PBS_O_HOME |sed 's#.*/\(home\|workdir\)/\(.*_team\)*.*#\2#g')"/$PBS_JOBID/" mkdir $WORKDIR rsync -ap $PBS_O_WORKDIR/ $WORKDIR/ # if you need to check your job output during execution (example: each hour) you can uncomment the following line # /shared/scripts/ADMIN__auto-rsync.example 3600 & else export WORKDIR=$PBS_O_WORKDIR fi fi echo "your current dir is: $PBS_O_WORKDIR" echo "your workdir is: $WORKDIR" echo "number of nodes: $NUM_NODES" echo "number of cores: "$(cat $PBS_NODEFILE|wc -l) echo "your execution environment: "$(cat $PBS_NODEFILE|uniq|while read line; do printf "%s" "$line "; done) cd $WORKDIR # If you're using only one node, it's counterproductive to use IB network for your MPI process communications if [ $NUM_NODES -eq 1 ]; then export PSM_DEVICES=self,shm export OMPI_MCA_mtl=^psm export OMPI_MCA_btl=shm,self else # Since we are using a single IB card per node which can initiate only up to a maximum of 16 PSM contexts # we have to share PSM contexts between processes # CIN is here the number of cores in node CIN=$(cat /proc/cpuinfo | grep -i processor | wc -l) if [ $(($CIN/16)) -ge 2 ]; then PPN=$(grep $HOSTNAME $PBS_NODEFILE|wc -l) if [ $CIN -eq 40 ]; then export PSM_SHAREDCONTEXTS_MAX=$(($PPN/4)) elif [ $CIN -eq 32 ]; then export PSM_SHAREDCONTEXTS_MAX=$(($PPN/2)) else echo "This computing node is not supported by this script" fi echo "PSM_SHAREDCONTEXTS_MAX defined to $PSM_SHAREDCONTEXTS_MAX" else echo "no PSM_SHAREDCONTEXTS_MAX to define" fi fi function get_gpu-ids() { if [ $PBS_NUM_PPN -eq $(cat /proc/cpuinfo | grep -cE "^processor.*:") ]; then echo "0,1" && return fi if [ -e /dev/cpuset/torque/$PBS_JOBID/cpus ]; then FILE="/dev/cpuset/torque/$PBS_JOBID/cpus" elif [ -e /dev/cpuset/torque/$PBS_JOBID/cpuset.cpus ]; then FILE="/dev/cpuset/torque/$PBS_JOBID/cpuset.cpus" else FILE="" fi if [ -e $FILE ]; then if [ $(cat $FILE | sed -r 's/^([0-9]).*$/\1/') -eq 0 ]; then echo "0" && return else echo "1" && return fi else echo "0,1" && return fi } gpus=$(get_gpu-ids) ## USER Part module load gcc/8.3.0 module load miniconda-py3/latest conda activate esmfold #Run cd $WORKDIR/ d1=`date +%s` echo $(date) esmfold_inference.py -i query.fasta -o outputdir/ d2=$(date +%s) echo $(date) diff=$((($d2 - $d1)/60)) echo "Time spent (min) : ${diff}" ## DO NOT MODIFY THE PART OF SCRIPT: you will be accountable for any damage you cause # At the term of your job, you need to get back all produced data synchronizing workdir folder with you starting job folder and delete the temporary one (workdir) if [ $NUM_NODES -le 1 ]; then cd $PBS_O_WORKDIR rsync -ap $WORKDIR/ $PBS_O_WORKDIR/ rm -rf $WORKDIR fi ## END-DO
In case of trouble, you can contact me at : hubert.santuz[at]ibpc.fr