now manual workaround for PDB ID mapping

scripts/results_processing/io_read_pdb.py

-import os
-import json
-from tqdm import tqdm
-from urllib import parse, request
-
-import numpy as np
-import pandas as pd
-
-def read_foldseek(result_path):
-    df = pd.read_csv(result_path, sep="\s+", header=None)
-    df.columns = ["query", "target", "seq. id.", "alignment length", "no. mismatches",
-                   "no. gap open", "query start", "query end", "target start", "target end",
-                   "e value", "bit score"]
-    df["query"] = df["query"].str.split("_").str[0]
-    df["corrected bit score"] = df["bit score"] / df["alignment length"]
-    if "pdb" in result_path:
-        df["target"] = df["target"].str.split(".").str[0]
-    else:
-        df["target"] = df["target"].str.split("-").str[:3].str.join("-")
-    return df
-
-def get_aligned_plddt(df, plddt, name_dict):
-    aligned_plddt = [0.] * len(df)
-    for e in tqdm(df.iterrows()):
-        index, row = e
-        query = row['query']
-        isoform = name_dict[query]
-        protein = plddt[isoform]
-        start = row['query start'] - 1
-        end = row['query end'] - 1
-        aligned_plddt[index] = np.mean(protein[start:end])
-    return aligned_plddt
-
-def get_from_uniprot(df, column, uniprot_from="ACC", uniprot_to="EGGNOG_ID", request_size=40000):
-    ids = df[column][~df[column].isnull()]
-    url = 'https://rest.uniprot.org/'
-
-    params = {
-    'from': uniprot_from,
-    'to': uniprot_to,
-    'format': 'tab',
-    'query': " ".join(ids.unique())
-    }
-
-    data = parse.urlencode(params)
-    data = data.encode('utf-8')
-    req = request.Request(url, data)
-    with request.urlopen(req) as f:
-        response = f.read()
-    return response
-
-def create_id_map(response, id_in, id_out):
-    map_from = []
-    map_to = []
-    for row in tqdm(response.decode("utf-8").split("\n")[:-1]):
-        col1, col2 = row.split("\t")
-        map_from.append(col1)
-        map_to.append(col2)
-
-    res = pd.DataFrame({id_in: map_from[1:], id_out: map_to[1:]})
-    res = pd.DataFrame(res.groupby(id_in)[id_out].apply(",".join))
-#     res.set_index(id_in, inplace=True)
-    return res
-
-def enrich_from_uniprot(df, column_from, column_to, uniprot_from="PDB_ID", uniprot_to="ACC"):
-    response = get_from_uniprot(df, column_from, uniprot_from=uniprot_from, uniprot_to=uniprot_to)
-    df_map = create_id_map(response, column_from, column_to)
-    return df.join(df_map, on=column_from)
-
-# FOLDSEEK scores
-fs_pdb = '/g/arendt/npapadop/repos/coffe/data/pdb_score.tsv'
-# per-residue pLDDT score
-plddt = np.load('/g/arendt/npapadop/repos/coffe/data/spongilla_plddt.npz')
-
-sequence_info = pd.read_csv("/g/arendt/npapadop/repos/coffe/data/sequence_info.csv")
-query_to_isoform = sequence_info[['query', 'isoform']].set_index('query').to_dict()['isoform']
-
-pdb = read_foldseek(fs_pdb)
-pdb["query"] = pdb["query"].values.astype(int)
-
-pdb['aligned_plddt'] = get_aligned_plddt(pdb, plddt, query_to_isoform)
-pdb = enrich_from_uniprot(pdb, "target", "uniprot", uniprot_from="PDB_ID", uniprot_to="ACC")
-pdb.to_parquet('/g/arendt/npapadop/repos/coffe/data/pdb_tmp.parquet')
\ No newline at end of file

scripts/results_processing/io_read_pdb.sh

-#!/bin/bash -ex
-#SBATCH -J coffe_read_pdb
-#SBATCH -t 5:00:00
-#SBATCH -c 1
-#SBATCH --mem=16G
-#SBATCH -o /g/arendt/npapadop/cluster/io_read_pdb.out
-#SBATCH -e /g/arendt/npapadop/cluster/io_read_pdb.err
-
-module load Anaconda3
-source ~/.bash_profile
-conda activate /g/arendt/npapadop/repos/condas/CoFFE
-
-python /g/arendt/npapadop/repos/coffe/scripts/results_processing/io_read_pdb.py
\ No newline at end of file