Single metric#
TMkit can evaluate qualities of proteins using a QC metric. We currently compile 7 metrics shown as follows.
Attribute |
Description |
|---|---|
desc |
biological description |
met |
determination method |
bio_name |
biological name |
head |
headline notation |
mthm |
number of helices |
rez |
resolution |
seq |
sequence information |
Reminder of data
Please make sure that the build-in example dataset has been downloaded before you walk through the tutorial.
Example usage#
First, let’s define 5 transmembrane proteins to be used, protein 1xqf chain A, protein 1eq8 chain A, protein 6e3y chain E, protein 3pux chain G and protein 3udc chain A, and put them in a Pandas dataframe as follows.
import pandas as pd
prots = [['1xqf', 'A'], ['1eq8', 'A'], ['6e3y', 'E'], ['3pux', 'G'], ['3udc', 'A'], ['3rko', 'A']]
df_prot = pd.DataFrame(prots, columns=['prot', 'chain'])
df_prot = df_prot.rename(columns={
0: 'prot',
1: 'chain',
})
print(df_prot)
We can download their PDB structures from PDBTM and save them in ./data/pdb/pdbtm/.
import tmkit as tmk
tmk.seq.retrieve_pdb_from_pdbtm(
prot_series=df_prot['prot'],
sv_fp='./data/pdb/pdbtm/',
)
===>No.1 protein name: 1xqf
======>successfully downloaded!
===>No.2 protein name: 1eq8
======>successfully downloaded!
===>No.3 protein name: 6e3y
======>successfully downloaded!
===>No.4 protein name: 3pux
======>successfully downloaded!
===>No.5 protein name: 3udc
======>successfully downloaded!
===>No.6 protein name: 3rko
======>successfully downloaded!
We can then download their XML files from PDBTM and save them in ./data/xml/.
import tmkit as tmk
tmk.seq.retrieve_xml_from_pdbtm(
prot_series=df_prot['prot'],
sv_fp='./data/xml/',
)
===>No.1 protein name: 1xqf
======>successfully downloaded!
===>No.2 protein name: 1eq8
======>successfully downloaded!
===>No.3 protein name: 6e3y
======>successfully downloaded!
===>No.4 protein name: 3pux
======>successfully downloaded!
===>No.5 protein name: 3udc
======>successfully downloaded!
===>No.6 protein name: 3rko
======>successfully downloaded!
Finally, we can just use the following one command generate the results using one of the above-mentioned 7 metrics. Each time you can just alter what’s put in parameter metric.
import tmkit as tmk
df = tmk.qc.obtain_single(
df_prot=df_prot,
pdb_cplx_fp='./data/pdb/pdbtm/',
fasta_fp='./data/fasta/',
xml_fp='./data/xml/',
sv_fp='./data/qc/',
metric='desc', # 'desc' 'met', 'bio_name', 'head', 'mthm', 'seq'
)
print(df)
Attributes#
Attribute |
Description |
|---|---|
|
Pandas dataframe storing protein names and chain names |
|
path where a protein complex file from PDBTM is placed |
|
path where a protein Fasta file is placed |
|
path where a protein XML file from PDBTM is placed |
|
path to save files |
|
a QC metric: |
See also
Please see here for better understanding the file-naming system.
Output#
=========>protein 1xqf chain A with rez 1.8
=========>protein 1eq8 chain A with rez nan
=========>protein 6e3y chain E with rez 3.3
=========>protein 3pux chain G with rez 2.3
=========>protein 3udc chain A with rez 3.35
=========>protein 3rko chain A with rez 3.0
prot chain rez
0 1xqf A 1.80
1 1eq8 A NaN
2 6e3y E 3.30
3 3pux G 2.30
4 3udc A 3.35
5 3rko A 3.00
=========>protein 1xqf chain A with met x-ray diffraction
=========>protein 1eq8 chain A with met unknown
=========>protein 6e3y chain E with met x-ray diffraction
=========>protein 3pux chain G with met x-ray diffraction
=========>protein 3udc chain A with met x-ray diffraction
=========>protein 3rko chain A with met x-ray diffraction
prot chain met
0 1xqf A x-ray diffraction
1 1eq8 A unknown
2 6e3y E x-ray diffraction
3 3pux G x-ray diffraction
4 3udc A x-ray diffraction
5 3rko A x-ray diffraction
=========>protein 1xqf chain A with bio_name the mechanism of ammonia transport based on the crystal structure of amtb of e. coli.
=========>protein 1eq8 chain A with bio_name three-dimensional structure of the pentameric helical bundle of the acetylcholine receptor m2 transmembrane segment
=========>protein 6e3y chain E with bio_name cryo-em structure of the active, gs-protein complexed, human cgrp receptor
=========>protein 3pux chain G with bio_name crystal structure of an outward-facing mbp-maltose transporter complex bound to adp-bef3
=========>protein 3udc chain A with bio_name crystal structure of a membrane protein
=========>protein 3rko chain A with bio_name crystal structure of the membrane domain of respiratory complex i from e. coli at 3.0 angstrom resolution
prot chain bio_name
0 1xqf A the mechanism of ammonia transport based on th...
1 1eq8 A three-dimensional structure of the pentameric ...
2 6e3y E cryo-em structure of the active, gs-protein co...
3 3pux G crystal structure of an outward-facing mbp-mal...
4 3udc A crystal structure of a membrane protein
5 3rko A crystal structure of the membrane domain of re...
=========>protein 1xqf chain A with head transport protein
=========>protein 1eq8 chain A with head signaling protein
=========>protein 6e3y chain E with head signaling protein
=========>protein 3pux chain G with head hydrolase/transport protein
=========>protein 3udc chain A with head membrane protein
=========>protein 3rko chain A with head oxidoreductase
prot chain head
0 1xqf A transport protein
1 1eq8 A signaling protein
2 6e3y E signaling protein
3 3pux G hydrolase/transport protein
4 3udc A membrane protein
5 3rko A oxidoreductase
=========>protein 1xqf chain A with desc TRANSPORT PROTEIN
=========>protein 1eq8 chain A with desc SIGNALING PROTEIN
=========>protein 6e3y chain E with desc SIGNALING PROTEIN
=========>protein 3pux chain G with desc HYDROLASE/TRANSPORT PROTEIN
=========>protein 3udc chain A with desc MEMBRANE PROTEIN
=========>protein 3rko chain A with desc OXIDOREDUCTASE
prot chain desc
0 1xqf A TRANSPORT PROTEIN
1 1eq8 A SIGNALING PROTEIN
2 6e3y E SIGNALING PROTEIN
3 3pux G HYDROLASE/TRANSPORT PROTEIN
4 3udc A MEMBRANE PROTEIN
5 3rko A OXIDOREDUCTASE
=========>protein 1xqf chain A with mthm 11.0
=========>protein 1eq8 chain A with mthm 1.0
=========>protein 6e3y chain E with mthm 1.0
=========>protein 3pux chain G with mthm 6.0
=========>protein 3udc chain A with mthm 3.0
=========>protein 3rko chain A with mthm 3.0
prot chain mthm
0 1xqf A 11.0
1 1eq8 A 1.0
2 6e3y E 1.0
3 3pux G 6.0
4 3udc A 3.0
5 3rko A 3.0
=========>File failed: 1xqf A
=========>File failed: 1eq8 A
=========>File failed: 6e3y E
=========>File failed: 3pux G
=========>File failed: 3udc A
=========>File failed: 3rko A
prot chain seq_aa seq_len
0 1xqf A AVADKADNAFMMICTALVLFMTIPGIALFYGGLIRGKNVLSMLTQV... 362.0
1 1eq8 A EKMSTAISVLLAQAVFLLLTSQR 23.0
2 6e3y E EANYGALLRELCLTQFQVDMEAVGETLWCDWGRTIRSYRELADCTW... 115.0
3 3pux G AMVQPQKARLFITHLLLLLFIAAIMFPLLMVVAISLRQGNFATGSL... 293.0
4 3udc A YDIKAVKFLLDVLKILIIAFIGIKFADFLIYRFYKLYSKSKIQLPQ... 267.0
5 3rko A AFAIFLIVAIGLCCLMLVGGWFLGGRARARLRLSAKFYLVAMFFVI... 95.0
You can use the information to do QC. For example, proteins that are determined by X-ray methods are reserved. You should do this as follows. Then, protein 1eq8 chain A will be eliminated.
df[df['met'] == 'x-ray diffraction']