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#!/bin/bash
#SBATCH -p gpu
#SBATCH --mem=32g
#SBATCH --gres=gpu:rtx2080:1
#SBATCH -c 2
#SBATCH --output=example_2.out
source activate mlfold
#new_probabilities_using_PSSM = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered
#probs - predictions from MPNN
#pssm_bias_gathered - input PSSM bias (needs to be a probability distribution)
#pssm_multi - a number between 0.0 (no bias) and 1.0 (no MPNN) inputed via flag --pssm_multi; this is a global number equally applied to all the residues
#pssm_coef_gathered - a number between 0.0 (no bias) and 1.0 (no MPNN) inputed via ../helper_scripts/make_pssm_input_dict.py can be adjusted per residue level; i.e only apply PSSM bias to specific residues; or chains
pssm_input_path="../inputs/PSSM_inputs"
folder_with_pdbs="../inputs/PDB_complexes/pdbs/"
output_dir="../outputs/example_pssm_outputs"
if [ ! -d $output_dir ]
then
mkdir -p $output_dir
fi
path_for_parsed_chains=$output_dir"/parsed_pdbs.jsonl"
path_for_assigned_chains=$output_dir"/assigned_pdbs.jsonl"
pssm=$output_dir"/pssm.jsonl"
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 "$chains_to_design"
python ../helper_scripts/make_pssm_input_dict.py --jsonl_input_path=$path_for_parsed_chains --PSSM_input_path=$pssm_input_path --output_path=$pssm
python ../protein_mpnn_run.py \
--jsonl_path $path_for_parsed_chains \
--chain_id_jsonl $path_for_assigned_chains \
--out_folder $output_dir \
--num_seq_per_target 2 \
--sampling_temp "0.1" \
--seed 37 \
--batch_size 1 \
--pssm_jsonl $pssm \
--pssm_multi 0.3 \
--pssm_bias_flag 1
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