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#!/bin/bash
#SBATCH -p gpu
#SBATCH --mem=32g
#SBATCH --gres=gpu:rtx2080:1
#SBATCH -c 3
#SBATCH --output=example_7.out
source activate mlfold
folder_with_pdbs="../PDB_complexes/pdbs/"
output_dir="../PDB_complexes/example_7_outputs"
if [ ! -d $output_dir ]
then
mkdir -p $output_dir
fi
path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
path_for_assigned_chains=$output_dir"/PDB_complexes/assigned_pdbs.jsonl"
path_for_bias=$output_dir"/bias_pdbs.jsonl"
AA_list="G P A"
bias_list="40.1 0.3 -0.05" #for G P A respectively; global AA bias in the logit space
chains_to_design="A B"
python ../helper_scripts/parse_multiple_chains.py --input_path=$folder_with_pdbs --output_path=$path_for_parsed_chains
python ../helper_scripts/assign_fixed_chains.py --input_path=$path_for_parsed_chains --output_path=$path_for_assigned_chains --chain_list "$chains_to_design"
python ../helper_scripts/make_bias_AA.py --output_path=$path_for_bias --AA_list="$AA_list" --bias_list="$bias_list"
python ../protein_mpnn_run.py \
--jsonl_path $path_for_parsed_chains \
--chain_id_jsonl $path_for_assigned_chains \
--out_folder $output_dir \
--bias_AA_jsonl $path_for_bias \
--num_seq_per_target 2 \
--sampling_temp "0.1" \
--batch_size 1
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