Phosphoproteomics scoring

Open In Colab

In this session, instead of scoring sequence one by one, we will score the whole phosphoproteomics dataset at once.

Setup

Run below to install:

!pip install python-katlas
import pandas as pd,numpy as np,seaborn as sns
from matplotlib import pyplot as plt
from katlas.core import *

Phosphoproteomics dataset

Three datasets are available:

  • PhosphoSitePlus human (2024 Jan)
  • Ochoa et al. human phosphoproteom
  • Combine Ochoa and PSP low throughput data
psp = Data.get_psp_human_site()
ochoa = Data.get_ochoa_site()
comb = Data.get_combine_site_psp_ochoa()
psp.head()
gene protein uniprot site gene_site SITE_GRP_ID species site_seq LT_LIT MS_LIT MS_CST CST_CAT# Ambiguous_Site
0 YWHAB 14-3-3 beta P31946 T2 YWHAB_T2 15718712 human ______MtMDksELV NaN 3.0 1.0 None 0
1 YWHAB 14-3-3 beta P31946 S6 YWHAB_S6 15718709 human __MtMDksELVQkAk NaN 8.0 NaN None 0
2 YWHAB 14-3-3 beta P31946 Y21 YWHAB_Y21 3426383 human LAEQAERyDDMAAAM NaN NaN 4.0 None 0
3 YWHAB 14-3-3 beta P31946 T32 YWHAB_T32 23077803 human AAAMkAVtEQGHELs NaN NaN 1.0 None 0
4 YWHAB 14-3-3 beta P31946 S39 YWHAB_S39 27442700 human tEQGHELsNEERNLL NaN 4.0 NaN None 0 
ochoa.head()
uniprot position residue is_disopred disopred_score log10_hotspot_pval_min isHotspot uniprot_position functional_score current_uniprot name gene Sequence is_valid site_seq gene_site
0 A0A075B6Q4 24 S True 0.91 6.839384 True A0A075B6Q4_24 0.149257 A0A075B6Q4 A0A075B6Q4_HUMAN None MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... True VDDEKGDSNDDYDSA A0A075B6Q4_S24
1 A0A075B6Q4 35 S True 0.87 9.192622 False A0A075B6Q4_35 0.136966 A0A075B6Q4 A0A075B6Q4_HUMAN None MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... True YDSAGLLSDEDCMSV A0A075B6Q4_S35
2 A0A075B6Q4 57 S False 0.28 0.818834 False A0A075B6Q4_57 0.125364 A0A075B6Q4 A0A075B6Q4_HUMAN None MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... True IADHLFWSEETKSRF A0A075B6Q4_S57
3 A0A075B6Q4 68 S False 0.03 0.375986 False A0A075B6Q4_68 0.119811 A0A075B6Q4 A0A075B6Q4_HUMAN None MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... True KSRFTEYSMTSSVMR A0A075B6Q4_S68
4 A0A075B6Q4 71 S False 0.05 0.000000 False A0A075B6Q4_71 0.095193 A0A075B6Q4 A0A075B6Q4_HUMAN None MDIQKSENEDDSEWEDVDDEKGDSNDDYDSAGLLSDEDCMSVPGKT... True FTEYSMTSSVMRRNE A0A075B6Q4_S71 
comb.head()
uniprot gene site site_seq source AM_pathogenicity CDDM_upper CDDM_max_score
0 A0A024R4G9 C19orf48 S20 ITGSRLLSMVPGPAR psp NaN PRKX,AKT1,PKG1,P90RSK,HIPK4,AKT3,HIPK1,PKACB,H... 2.407041
1 A0A075B6Q4 None S24 VDDEKGDSNDDYDSA ochoa NaN CK2A2,CK2A1,GRK7,GRK5,CK1G1,CK1A,IKKA,CK1G2,CA... 2.295654
2 A0A075B6Q4 None S35 YDSAGLLSDEDCMSV ochoa NaN CK2A2,CK2A1,IKKA,ATM,IKKB,CAMK1D,MARK2,GRK7,IK... 2.488683
3 A0A075B6Q4 None S57 IADHLFWSEETKSRF ochoa NaN GRK7,CK2A1,CK2A2,PKN2,GRK1,GRK5,MARK1,MARK2,UL... 1.851894
4 A0A075B6Q4 None S68 KSRFTEYSMTSSVMR ochoa NaN AKT1,P90RSK,AKT3,SGK1,AKT2,NDR2,RSK2,P70S6K,RS... 2.026384

Scoring on all-capital sequence

comb.head()
uniprot gene site site_seq source AM_pathogenicity CDDM_upper CDDM_max_score
0 A0A024R4G9 C19orf48 S20 ITGSRLLSMVPGPAR psp NaN PRKX,AKT1,PKG1,P90RSK,HIPK4,AKT3,HIPK1,PKACB,H... 2.407041
1 A0A075B6Q4 None S24 VDDEKGDSNDDYDSA ochoa NaN CK2A2,CK2A1,GRK7,GRK5,CK1G1,CK1A,IKKA,CK1G2,CA... 2.295654
2 A0A075B6Q4 None S35 YDSAGLLSDEDCMSV ochoa NaN CK2A2,CK2A1,IKKA,ATM,IKKB,CAMK1D,MARK2,GRK7,IK... 2.488683
3 A0A075B6Q4 None S57 IADHLFWSEETKSRF ochoa NaN GRK7,CK2A1,CK2A2,PKN2,GRK1,GRK5,MARK1,MARK2,UL... 1.851894
4 A0A075B6Q4 None S68 KSRFTEYSMTSSVMR ochoa NaN AKT1,P90RSK,AKT3,SGK1,AKT2,NDR2,RSK2,P70S6K,RS... 2.026384

It looks that the sequence are in all-capital format, so we will use param_CDDM_upper

cddm = predict_kinase_df(comb,'site_seq',**Params('CDDM_upper'))
input dataframe has a length 121419
Preprocessing
Finish preprocessing
Merging reference
Finish merging
# PSPA involves more calculation, will take longer
pspa = predict_kinase_df(comb,'site_seq',**Params('PSPA'))
input dataframe has a length 121419
Preprocessing
Finish preprocessing
Merging reference
Finish merging
100%|██████████| 396/396 [00:44<00:00,  8.86it/s]

We can also split S/T and Y sites for scoring.

comb_y = comb[comb.site_seq.str[7]=='Y']
comb_st = comb[comb.site_seq.str[7]!='Y']
pspa_y = predict_kinase_df(comb_y,'site_seq',**Params('PSPA_y'))
pspa_st = predict_kinase_df(comb_st,'site_seq',**Params('PSPA_st'))
input dataframe has a length 8051
Preprocessing
Finish preprocessing
Merging reference
Finish merging
100%|██████████| 93/93 [00:00<00:00, 227.27it/s]
input dataframe has a length 113368
Preprocessing
Finish preprocessing
Merging reference
Finish merging
100%|██████████| 303/303 [00:15<00:00, 19.77it/s]

Release RAM for the following tasks

del comb_y, comb_st,pspa_y,pspa_st

Scoring on phosphorylated sequence

psp.head()
gene protein uniprot site gene_site SITE_GRP_ID species site_seq LT_LIT MS_LIT MS_CST CST_CAT# Ambiguous_Site
0 YWHAB 14-3-3 beta P31946 T2 YWHAB_T2 15718712 human ______MtMDksELV NaN 3.0 1.0 None 0
1 YWHAB 14-3-3 beta P31946 S6 YWHAB_S6 15718709 human __MtMDksELVQkAk NaN 8.0 NaN None 0
2 YWHAB 14-3-3 beta P31946 Y21 YWHAB_Y21 3426383 human LAEQAERyDDMAAAM NaN NaN 4.0 None 0
3 YWHAB 14-3-3 beta P31946 T32 YWHAB_T32 23077803 human AAAMkAVtEQGHELs NaN NaN 1.0 None 0
4 YWHAB 14-3-3 beta P31946 S39 YWHAB_S39 27442700 human tEQGHELsNEERNLL NaN 4.0 NaN None 0

It looks that the sequence are in phosphorylated status, so we will use param_CDDM

Directly run psp in predict_kinase_df will gives an error, as some of the site_sequence do not have S/T/Y at the center. We need to clean them before process.

psp.site_seq.str[7].value_counts()
site_seq
s    141851
t     58761
y     39367
h        14
k         4
r         3
g         3
p         2
n         1
f         1
l         1
a         1
i         1
d         1
Name: count, dtype: int64
psp = psp[psp.site_seq.str[7].isin(['s','t','y'])]
cddm = predict_kinase_df(psp,'site_seq',**Params('CDDM'))
input dataframe has a length 239979
Preprocessing
Finish preprocessing
Merging reference
Finish merging
# PSPA calculation will take longer
pspa = predict_kinase_df(psp,'site_seq',**Params('PSPA'))
input dataframe has a length 239979
Preprocessing
Finish preprocessing
Merging reference
Finish merging
100%|██████████| 396/396 [01:34<00:00,  4.19it/s]