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This repository contains the workflow used to generate fasta datasets compatible with Peptide matcher or potentially other software capable of parsing the data. To download the datasets, go to
Code Snippets
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import re from pandas import read_csv from os.path import basename, splitext from Bio import SeqIO from Bio.Seq import Seq from sys import stderr from itertools import groupby parsed_files = snakemake.input['parsed'] uniprot_files = snakemake.input['uniprot'] fasta_file = str(snakemake.output) fnames = {} for fname in parsed_files: record_id = splitext(basename(fname))[0] fnames[record_id] = fname def compress(s): return ''.join(str(sum(1 for x in g)) + k for k, g in groupby(s)) def to_hex(decs): return ''.join('%02x' % x for x in decs) feature_types = { 'transmembrane region': 'T', 'signal peptide': 'S' } desc_re = re.compile(' ?([^=]+)=(.+?)( \{(.+?)\})?;') with open(fasta_file, 'w') as fasta: for uniprot_file in uniprot_files: uniprot = SeqIO.parse(uniprot_file, 'swiss') record = next(uniprot) assert record.id in fnames, "Record %s not in alphafold" % record.id data = read_csv(fnames[record.id], index_col = 0) TMs = {} for feature in record.features: if feature.type in feature_types and type(feature.location.end).__name__ == 'ExactPosition': for i in range(feature.location.start - 1, feature.location.end): TMs[i] = feature_types[feature.type] acc = [] sst = [] conf = [] tm = [] match = True for i in range(len(record.seq)): if i in data['res']: match = data['res'][i] == record.seq[i] if not match: print("Sequence mismatch in %s" % record.id, file = stderr) break acc.append(data['acc'][i]) sst.append(data['sst'][i]) conf.append(round(data['conf'][i])) else: acc.append(-1) sst.append('_') conf.append(-1) tm.append(TMs[i] if i in TMs else '-') descr = '' for key, val, *rest in desc_re.findall(record.description): if key == 'RecName: Full' or key == 'SubName: Full': descr = val break record.description = '%s confidence:%s secstruct:%s accessibility:%s' % (descr, to_hex(conf), compress(sst), to_hex(acc)) if match: record.description += ' transmembrane:%s' % compress(tm) else: record.seq = Seq(''.join(data['res'])) SeqIO.write(record, fasta, 'fasta') |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import pandas as pd from urllib import request import gzip from Bio.SeqUtils import IUPACData from Bio import SeqIO from io import StringIO hist_file = str(snakemake.input['history']) pdb_files = snakemake.params['pdb_files'] output_file = str(snakemake.output) accession = str(snakemake.wildcards['acc']) date_max = pd.to_datetime(snakemake.config['uniprot']['date_max']) host = str(snakemake.config['unisave']['host']) residues = { k.upper(): v for k, v in IUPACData.protein_letters_3to1.items() } residues['XAA'] = 'X' def parse_pdb(pdb_file): start = stop = -1 chain_seq = '' confs = [] with gzip.open(pdb_file, 'rt') as pdb_fh: for line in pdb_fh: if line.startswith('ATOM'): atom = line[13:17].strip() chain = line[20:22].strip() if atom == 'N' and chain == 'A': res = line[17:20] chain_seq += residues[res] elif start < 0 and line.startswith('DBREF'): values = line.split() chain = values[2] if chain == 'A': start = int(values[8]) - 1 stop = int(values[9]) return chain_seq, start, stop def get_ver(version): url = "{host}/{acc}?format={format}&versions={ver}".format(host = host, acc = accession, format = "txt", ver = version) with request.urlopen(url) as fp: txt = fp.read().decode('utf-8') return txt seq_dict = {} for pdb_file in pdb_files: pdb_seq, start, stop = parse_pdb(pdb_file) for i in range(stop - start): seq_dict[i + start] = pdb_seq[i] seq = ''.join(v for k, v in sorted(seq_dict.items())) data = pd.read_csv(hist_file, sep = "\t") data['Date'] = pd.to_datetime(data['Date']) versions = data[data['Date'] <= date_max].drop_duplicates('Sequence version') found = False for index, row in versions.iterrows(): txt = get_ver(row['Entry version']) swiss = SeqIO.parse(StringIO(txt), 'swiss') record = next(swiss) if record.seq == seq: found = True break assert found, "Matching sequence for pdb {acc} not found".format(acc = accession) with open(output_file, 'w') as fp: fp.write(txt) |
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | import gzip from pandas import read_fwf from collections import defaultdict from Bio.SeqUtils import IUPACData pdb_files = snakemake.params['pdb_files'] dssp_files = snakemake.input['dssp_files'] output_file = str(snakemake.output) dssp_specs = { 'resnumber': (0, 5), 'resname': (5, 10), 'chain': (10, 12), 'aminoacid': (12, 14), 'secstruct': (14, 17), 'turns_helix_3': (17, 19), 'turns_helix_4': (19, 20), 'turns_helix_5': (20, 21), 'geometrical_bend': (21, 22), 'chirality': (22, 23), 'beta_bridge_label_1': (23, 24), 'beta_bridge_label_2': (24, 25), 'beta_bridge_partner_resnum_1': (25, 29), 'beta_bridge_partner_resnum_2': (29, 33), 'beta_sheet_label': (33, 34), 'accessibility': (34, 38), 'N_H___O_00': (38, 45), 'N_H___O_01': (45, 50), 'O___N_H_00': (50, 56), 'O___N_H_01': (57, 61), 'N_H___O_10': (61, 67), 'N_H___O_11': (68, 72), 'O___N_H_10': (72, 78), 'O___N_H_11': (79, 83), 'tco': (83, 91), 'kappa': (91, 97), 'alpha': (97, 103), 'phi': (103, 109), 'psi': (109, 115), 'x_ca': (115, 122), 'y_ca': (122, 129), 'z_ca': (129, 136) } maxasa_theoretical = { 'A': 129, 'C': 167, 'D': 193, 'E': 223, 'F': 240, 'G': 104, 'H': 224, 'I': 197, 'K': 236, 'L': 201, 'M': 224, 'N': 195, 'P': 159, 'Q': 225, 'R': 274, 'S': 155, 'T': 172, 'V': 174, 'W': 285, 'Y': 263 } maxasa_empirical = { 'A': 121, 'C': 148, 'D': 187, 'E': 214, 'F': 228, 'G': 97, 'H': 216, 'I': 195, 'K': 230, 'L': 191, 'M': 203, 'N': 187, 'P': 154, 'Q': 214, 'R': 265, 'S': 143, 'T': 163, 'V': 165, 'W': 264, 'Y': 255 } residues = { k.upper(): v for k, v in IUPACData.protein_letters_3to1.items() } residues['XAA'] = 'X' def parse_dssp(fname): colspecs = list(dssp_specs.values()) names = list(dssp_specs.keys()) with open(fname) as dssp: for line in dssp: if not line.rstrip().endswith('.'): break data = read_fwf(dssp, colspecs = colspecs, names = names) secstruct = [] dssp_seq = '' acc = [] for row in data.itertuples(): if row.chain == 'A': aminoacid = str(row.aminoacid) struct = str(row.secstruct) asa = int(row.accessibility) rsa = round(asa / maxasa_theoretical[aminoacid] * 100) secstruct.append(struct if struct != 'nan' else '-') dssp_seq += aminoacid acc.append(str(rsa)) return dssp_seq, secstruct, acc def parse_pdb(pdb_file): start = stop = -1 chain_seq = '' confs = [] with gzip.open(pdb_file, 'rt') as pdb_fh: for line in pdb_fh: if line.startswith('ATOM'): atom = line[13:17].strip() chain = line[20:22].strip() if atom == 'N' and chain == 'A': res = line[17:20] conf = float(line[60:66]) chain_seq += residues[res] confs.append(conf) elif start < 0 and line.startswith('DBREF'): values = line.split() chain = values[2] if chain == 'A': start = int(values[8]) - 1 stop = int(values[9]) return chain_seq, start, stop, confs with open(output_file, 'w') as output: num_wins = len(dssp_files) accs = {} ssts = {} confs = {} seq = {} count = defaultdict(int) for win in range(num_wins): dssp_seq, win_ssts, win_accs = parse_dssp(dssp_files[win]) pdb_seq, start, stop, win_confs = parse_pdb(pdb_files[win]) assert dssp_seq == pdb_seq, "Sequence mismatch between dssp and pdb" for i in range(stop - start): pos = i + start count[pos] += 1 if pos in seq: assert seq[pos] == dssp_seq[i], "Sequence mismatch between windows" else: seq[pos] = dssp_seq[i] if pos not in confs or win_confs[i] > confs[pos]: confs[pos] = win_confs[i] accs[pos] = win_accs[i] ssts[pos] = win_ssts[i] output.write('pos,res,count,acc,sst,conf\n') for pos, num in sorted(count.items()): output.write('%d,%s,%d,%s,%s,%.2f\n' % (pos, seq[pos], num, accs[pos], ssts[pos], confs[pos])) |
19 20 | shell: "wget -O {output} '{config[unisave][host]}/{wildcards.acc}?download=true&format=tsv'" |
27 28 | shell: "mkdir -p {output} && curl -sL {params.url:q} | tar xf - -C {output} --wildcards --no-anchored '*.pdb.gz'" |
85 86 | script: "scripts/dload_version.py" |
95 96 | shell: "gzip -cd {params.pdb_file} | dssp -i /dev/stdin -o {output}" |
106 107 | script: "scripts/parse.py" |
115 116 | script: "scripts/collect.py" |
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Created: 1yr ago
Updated: 1yr ago
Maitainers:
public
URL:
https://github.com/OKLAB2016/peptide-matcher-data
Name:
peptide-matcher-data
Version:
v4
Downloaded:
0
Copyright:
Public Domain
License:
None
Keywords:
- Future updates
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