For the purpose of this manual, the expected file is already provided here: ./example_data/example_1_sequences.fasta. If you want to run a smaller test, you can use ./example_data/example_1_sequences_shorter.fasta instead.
📝 The example file was generated by downloading all 294 proteins that belong to human translation pathway from: Reactome. eIF4G2 sequence was downloaded from (Uniprot:P78344).
Now run:
source activate AlphaPulldown
create_individual_features.py \
--fasta_paths=baits.fasta,example_1_sequences.fasta \
--data_dir=<path to alphafold databases> \
--save_msa_files=False \
--output_dir=<dir to save the output objects> \
--use_precomputed_msas=False \
--max_template_date=<any date you want, format like: 2050-01-01> \
--skip_existing=False \
--seq_index=<any number you want or skip the flag to run all one after another>create_individual_features.py will compute necessary features each protein in ./example_data/example_1_sequences.fasta and store them in the output_dir. Please be aware that everything after > will be
taken as the description of the protein and please be aware that any special symbol, such as | : ; #, after > will be replaced with _.
The name of the pickles will be the same as the descriptions of the sequences in fasta files (e.g. ">protein_A" in the fasta file will yield "protein_A.pkl")
On a compute cluster, you may want to run all jobs in parallel as a job array. For example, on SLURM queuing system at EMBL we could use the following create_individual_features_SLURM.sh script:
#!/bin/bash
#A typical run takes couple of hours but may be much longer
#SBATCH --job-name=array
#SBATCH --time=10:00:00
#log files:
#SBATCH -e logs/create_individual_features_%A_%a_err.txt
#SBATCH -o logs/create_individual_features_%A_%a_out.txt
#qos sets priority
#SBATCH --qos=low
#Limit the run to a single node
#SBATCH -N 1
#Adjust this depending on the node
#SBATCH --ntasks=8
#SBATCH --mem=64000
module load HMMER/3.3.2-gompic-2020b
module load HH-suite/3.3.0-gompic-2020b
module load Anaconda3
source activate AlphaPulldown
create_individual_features.py \
--fasta_paths=baits.fasta,example_1_sequences_shorter.fasta \
--data_dir=/scratch/AlphaFold_DBs/2.2.2/ \
--save_msa_files=False \
--output_dir=/scratch/user/output/features \
--use_precomputed_msas=False \
--max_template_date=2050-01-01 \
--skip_existing=True \
--seq_index=$SLURM_ARRAY_TASK_IDand then run using:
mkdir logs
#Count the number of jobs corresponding to the number of sequences:
baits=`grep ">" baits.fasta | wc -l`
candidates=`grep ">" example_1_sequences_shorter.fasta | wc -l`
count=$(( $baits + $candidates ))
#Run the job array, 100 jobs at a time:
sbatch --array=1-$count%100 create_individual_features_SLURM.sh
By default is False to save storage stage but can be changed into True. If it is set to True, the programme will
create individual folder for each protein. The output directory will look like:
output_dir
|- protein_A.pkl
|- protein_A
|- uniref90_hits.sto
|- pdb_hits.sto
|- etc.
|- protein_B.pkl
|- protein_B
|- uniref90_hits.sto
|- pdb_hits.sto
|- etc.
If save_msa_files=False then the output_dir will look like:
output_dir
|- protein_A.pkl
|- protein_B.pkl
Default value is False. However, if you have already had msa files for your proteins, please set the parameter to be True and arrange your msa files in the format as below:
example_directory
|- protein_A
|- uniref90_hits.sto
|- pdb_hits.sto
|-***.a3m
|- etc
|- protein_B
|- ***.sto
|- etc
Then, in the command line, set the output_dir=/path/to/example_directory
Default is False but if you have run the 1st step already for some proteins and now add new proteins to the list, you can change skip_existing to True in the
command line to avoid rerunning the same procedure for the previously calculated proteins.
Default is None and the programme will run predictions one by one in the given files. However, you can set seq_index to
different number if you wish to run an array of jobs in parallel then the programme will only run the corresponding job specified by the seq_index. e.g. the programme only calculate features for the 1st protein in your fasta file if seq_index is set to be 1. See also the Slurm sbatch script above for example how to use it for parallel execution.
❗ seq_index starts from 1.
Inspired by pull-down assays, one can specify one or more proteins as "bait" and another list of proteins as "candidates". Then the programme will use AlphafoldMultimerV2 to predict interactions between baits (as in example_data/baits.txt) and candidates (as in example_data/candidates.txt).
Note If you want to save time and run fewer jobs, you can use example_data/candidates_shorter.txt instead of example_data/candidates.txt
In this example, we selected pulldown mode and made eIF4G2(Uniprot:P78344) as a bait while the other 294 proteins as candidates. Thus, in total, there will be 1 * 294 = 294 predictions.
The command line interface for using pulldown mode will then become:
run_multimer_jobs.py --mode=pulldown \
--num_cycle=3 \
--num_predictions_per_model=1 \
--output_path=<output directory> \
--data_dir=<path to alphafold databases> \
--protein_lists=baits.txt,candidates.txt \
--monomer_objects_dir=/path/to/monomer_objects_directory \
--job_index=<any number you want>
📝 To reproduce the results of Lassa virus Z protein vs L protein fragments written in our paper, simply use baits_Z_protein.txt and L_protein_fragments.txt as the --protein_listsinputs. This example shows also how to run the interaction screen for fragments of proteins, keeping the original full-length residue numbering in the output!
It should be the same directory as output_dir specified in Step 1. It can be one directory or contain multiple directories if you stored pre-calculated objects in different locations. In the case of
multiple monomer_objects_dir, remember to put a , between each e.g. --monomer_objects_dir=<dir_1>,<dir_2>
Default is None and the programme will run predictions one by one in the given files. However, you can set job_index to
different number if you wish to run an array of jobs in parallel then the programme will only run the corresponding job specified by the job_index
❗ job_index starts from 1
On a compute cluster, you may want to run all jobs in parallel as a job array. For example, on SLURM queuing system at EMBL we could use the following run_multimer_jobs_SLURM.sh sbatch script:
#!/bin/bash
#A typical run takes couple of hours but may be much longer
#SBATCH --job-name=array
#SBATCH --time=2-00:00:00
#log files:
#SBATCH -e logs/run_multimer_jobs_%A_%a_err.txt
#SBATCH -o logs/run_multimer_jobs_%A_%a_out.txt
#qos sets priority
#SBATCH --qos=low
#SBATCH -p gpu
#lower end GPUs might be sufficient for pairwise screens:
#SBATCH -C "gpu=2080Ti|gpu=3090"
#Reserve the entire GPU so no-one else slows you down
#SBATCH --gres=gpu:1
#Limit the run to a single node
#SBATCH -N 1
#Adjust this depending on the node
#SBATCH --ntasks=8
#SBATCH --mem=64000
module load Anaconda3
module load CUDA/11.3.1
module load cuDNN/8.2.1.32-CUDA-11.3.1
source activate AlphaPulldown
MAXRAM=$(echo `ulimit -m` '/ 1024.0'|bc)
GPUMEM=`nvidia-smi --query-gpu=memory.total --format=csv,noheader,nounits|tail -1`
export XLA_PYTHON_CLIENT_MEM_FRACTION=`echo "scale=3;$MAXRAM / $GPUMEM"|bc`
export TF_FORCE_UNIFIED_MEMORY='1'
run_multimer_jobs.py --mode=pulldown \
--num_cycle=3 \
--num_predictions_per_model=1 \
--output_path=/scratch/user/output/models \
--data_dir=/scratch/AlphaFold_DBs/2.2.2/ \
--protein_lists=baits.txt,candidates_shorter.txt \
--monomer_objects_dir=/scratch/user/output/features \
--job_index=$SLURM_ARRAY_TASK_IDand then run using:
mkdir -p logs
#Count the number of jobs corresponding to the number of sequences:
baits=`grep -c "" baits.txt` #count lines even if the last one has no end of line
candidates=`grep -c "" candidates_shorter.txt` #count lines even if the last one has no end of line
count=$(( $baits * $candidates ))
sbatch --array=1-$count example_data/run_multimer_jobs_SLURM.sh
Feature 1
When a batch of jobs is finished, AlphaPulldown can create a Jupyter notebook that presents a neat overview of the models, as seen in the example screenshot 
On the left side, there is a bookmark listing all the jobs and when clicking a bookmark, and executing the corresponding cells, the notebook will show: 1) PAE plots 2) predicted model coloured by pLDDT scores 3) predicted models coloured by chains.
In order to create the notebook, within the same conda environment, run:
source activate AlphaPulldown
cd <models_output_dir>
create_notebook.py --cutoff=5.0<output_dir>!
This command will yield an output.ipynb, which you can open it via Jupyterlab. Jupyterlab is already installed when installing AlphaPulldown with pip. Thus, to view the notebook:
source activate AlphaPulldown
cd <models_output_dir>
jupyter-lab output.ipynb📝 If you run AlphaPulldown on a remote computer cluster, you will need a graphical connection to open the notebook in a browser, mount the remote directory to your local computer as a network directory, or copy the entire <models_output_dir> to the local computer.
About the parameters
cutoff is to check the value of PAE between chains. In the case of multimers, the analysis programme will create the notebook only from models with inter-chain PAE values smaller than the cutoff.
Feature 2
We have also provided a singularity image called alpha-analysis.sifto generate a CSV table with structural properties and scores.
Firstly, download the singularity image from here. Chrome user may not be able to download it after clicking the link. If so, please right click and select "Save link as".
Then execute the singularity image (i.e. the sif file) by:
singularity exec \
--no-home \
--bind /path/to/your/output/dir:/mnt \
<path to your downloaded image>/alpha-analysis.sif \
run_get_good_pae.sh \
--output_dir=/mnt \
--cutoff=10
About the outputs
By default, you will have a csv file named predictions_with_good_interpae.csv created in the directory /path/to/your/output/dir as you have given in the command above. predictions_with_good_interpae.csv reports: 1. iptm, iptm+ptm scores provided by AlphaFold 2. mpDockQ score developed by Bryant et al., 2022 3. PI_score developed by Malhotra et al., 2021. The detailed explainations on these scores can be found in our paper and an example screenshot of the table is below. 
As the name suggest, all_vs_all means predict all possible pairwise comparisons within a single input file. The input can be either full-length proteins or regions of a protein, as illustrated in the example_all_vs_all_list.txt and the figure below:
The corresponding command is:
run_multimer_jobs.py \
--mode=all_vs_all \
--num_cycle=3 \
--num_predictions_per_model=1 \
--output_path=<path to output directory> \
--data_dir=<path to AlphaFold data directory> \
--protein_lists=example_all_vs_all_list.txt \
--monomer_objects_dir=/path/to/monomer_objects_directory \
--job_index=<any number you want>