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""" | |
Download all EXPASY amino acid scales. | |
Collaborative work done by Kannan Sankar, Mei Xiao and Eric Ma. | |
Extracts all amino acid properties from EXPASY, | |
and dumps them as a CSV file. | |
If you use the scales that are downloaded by this script, | |
please make sure that you cite ProtScale. | |
Reference is here: https://web.expasy.org/protscale/protscale-ref.html. | |
Credit must be due properly. | |
""" | |
import requests | |
from bs4 import BeautifulSoup | |
import pandas as pd | |
from tqdm.autonotebook import tqdm | |
import re | |
urls = requests.get("https://web.expasy.org/protscale/") | |
urls_list = urls.text.split("<PRE>")[1].split("</PRE>")[0] | |
soup = BeautifulSoup(urls.text, "html.parser") | |
wanted = [ | |
a.get("href") | |
for a in soup.body.find_all("a") | |
if (len(a) >= 1) | |
and ("/protscale/pscale" in a.get("href")) | |
and ("protscale_help" not in a.get("href")) | |
] | |
urls = ["https://web.expasy.org" + path for path in wanted] | |
property_names = [ | |
path.split("/")[-1].replace(".html", "").replace(".", "_").replace("-", "_").lower() | |
for path in wanted | |
] | |
def generate_mapping(url): | |
result = requests.get(url) | |
mappings = result.text.split("<PRE>")[1].split("</PRE>")[0].split("\n") | |
mappings = [m for m in mappings if ":" in m] | |
mappings = [re.split(":\s+", m) for m in mappings] | |
mappings = {aa.upper(): float(value) for aa, value in mappings} | |
return mappings | |
mappings = [] | |
for i, (url, name) in enumerate(zip(urls, property_names)): | |
print(name) | |
mapping = generate_mapping(url) | |
mapping = pd.Series(mapping, name=name) | |
mappings.append(mapping) | |
expasy_aa_feats = pd.DataFrame(mappings) | |
# aa_props.to_csv("amino_acid_properties.csv") | |
AMINO_ACIDS = [ | |
"ALA", | |
"ARG", | |
"ASN", | |
"ASP", | |
"CYS", | |
"GLN", | |
"GLU", | |
"GLY", | |
"HIS", | |
"ILE", | |
"LEU", | |
"LYS", | |
"MET", | |
"PHE", | |
"PRO", | |
"SER", | |
"THR", | |
"TRP", | |
"TYR", | |
"VAL", | |
] | |
# Taken from: https://www.anaspec.com/html/pK_n_pl_Values_of_AminoAcids.html | |
PKA_COOH_ALPHA = [ | |
2.35, | |
2.18, | |
2.18, | |
1.88, | |
1.71, | |
2.17, | |
2.19, | |
2.34, | |
1.78, | |
2.32, | |
2.36, | |
2.20, | |
2.28, | |
2.58, | |
1.99, | |
2.21, | |
2.15, | |
2.38, | |
2.20, | |
2.29, | |
] | |
PKA_NH3 = [ | |
9.87, | |
9.09, | |
9.09, | |
9.60, | |
10.78, | |
9.13, | |
9.67, | |
9.60, | |
8.97, | |
9.76, | |
9.60, | |
8.90, | |
9.21, | |
9.24, | |
10.60, | |
9.15, | |
9.12, | |
9.39, | |
9.11, | |
9.74, | |
] | |
PKA_RGROUP = [ | |
7.0, | |
13.2, | |
13.2, | |
3.65, | |
8.33, | |
7, | |
4.25, | |
7, | |
5.97, | |
7, | |
7, | |
10.28, | |
7, | |
7, | |
7, | |
7, | |
7, | |
7, | |
10.07, | |
7, | |
] | |
ISOELECTRIC_POINTS = [ | |
6.11, | |
10.76, | |
10.76, | |
2.98, | |
5.02, | |
5.65, | |
3.08, | |
6.06, | |
7.64, | |
6.04, | |
6.04, | |
9.47, | |
5.74, | |
5.91, | |
6.30, | |
5.68, | |
5.60, | |
5.88, | |
5.63, | |
6.02, | |
] | |
# Other features? | |
pka_cooh_alpha = pd.Series( | |
dict(zip(AMINO_ACIDS, PKA_COOH_ALPHA)), name="pka_cooh_alpha" | |
) | |
pka_nh3 = pd.Series(dict(zip(AMINO_ACIDS, PKA_NH3)), name="pka_nh3") | |
pka_rgroup = pd.Series(dict(zip(AMINO_ACIDS, PKA_RGROUP)), name="pka_rgroup") | |
isoelectric_points = pd.Series( | |
dict(zip(AMINO_ACIDS, ISOELECTRIC_POINTS)), name="isoelectric_points" | |
) | |
basic_aa_feats = pd.DataFrame([pka_cooh_alpha, pka_nh3, pka_rgroup, isoelectric_points]) | |
aa_feats = pd.concat([basic_aa_feats, expasy_aa_feats]) | |
aa_feats.to_csv("amino_acid_properties.csv") |
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