UMAP of PSPA & KD seq

import pandas as pd
from katlas.core import *
from katlas.plot import *

kinase info + PSPA data

df = Data.get_kinase_info()

df.columns

Index(['kinase', 'ID_coral', 'uniprot', 'ID_HGNC', 'modi_group', 'group',
       'family', 'subfamily_coral', 'subfamily', 'in_pspa_st', 'in_pspa_tyr',
       'in_pspa', 'in_cddm', 'kd_ID', 'active_D1_D2', 'active_kd_ID',
       'pspa_ID', 'pseudo', 'pspa_category_small', 'pspa_category_big',
       'cddm_big', 'cddm_small', 'length', 'human_uniprot_sequence',
       'kinasecom_domain', 'nucleus', 'cytosol', 'cytoskeleton',
       'plasma membrane', 'mitochondrion', 'Golgi apparatus',
       'endoplasmic reticulum', 'vesicle', 'centrosome', 'aggresome',
       'main_location'],
      dtype='object')

Filter active kd ID and in PSPA

pspa_info = df[df.active_kd_ID.notna() & df.in_pspa]

cols = ['kinase','uniprot','modi_group','pspa_category_big','pspa_category_small','family','subfamily','kd_ID']

pspa_info = pspa_info[cols]

The step above when merge, automatically filter out those with _TYR ( they are very non-specific, we’ll remove them)

Umap plot of PSPA PSSM

pspa=Data.get_pspa_all_scale().reset_index()

# add kinase info
info= pspa_info.merge(pspa)

feat = info.iloc[:,-230:]

embed = reduce_feature(feat,'umap',5,min_dist=0.6)

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

if k in info.modi_group

  Cell In[21], line 1
    if k in info.modi_group
                           ^
SyntaxError: expected ':'

[k for k in group_color.keys() if k in info.modi_group.tolist()]

['CMGC', 'AGC', 'TK', 'TKL', 'CAMK', 'STE', 'CK1', 'NEK', 'Atypical', 'Other']

import seaborn as sns
from matplotlib import pyplot as plt

def plot_2d(X: pd.DataFrame, # a dataframe that has first column to be x, and second column to be y
            hue_title=None, # legend box title
            hue_order=None,
            figsize=(7,7),
            **kwargs, # arguments for sns.scatterplot
            ):
    """
    Make 2D plot from a dataframe that has first column to be x, and second column to be y.
    Legend box on the right.
    """
    fig, ax = plt.subplots(figsize=figsize)
    hue_data = kwargs.get('hue')
    if hue_data is not None and hue_order is not None: 
        hue_order = [k for k in hue_order if k in pd.Series(hue_data).unique()]
    sns.scatterplot(data=X, 
                    x=X.columns[0], y=X.columns[1], 
                    hue_order=hue_order,
                    alpha=0.7, ax=ax, **kwargs)
    # have legend box on the right
    if hue_data is not None: ax.legend(title=hue_title, loc='center left', bbox_to_anchor=(1.02, 0.5))

plot_2d(embed,hue=info.modi_group,hue_title='Group',
        palette=group_color,
        hue_order=group_color.keys())

plot_2d(embed,hue=info.modi_group,hue_title='Group',
        palette=group_color,
        hue_order=[k for k in group_color.keys() if k in info.modi_group.tolist()])

def get_hue_big(df,
                hue_col, # column of hue
                cnt_thr=10, # higher or equal to this threshold will be considered
               ):
    "Get part of hue according to its value counts; applied when the groups are too many."
    cnt = df[hue_col].value_counts()
    names = cnt[cnt>=cnt_thr].index
    return df[hue_col][df[hue_col].isin(names)]

hue_pspa = get_hue_big(info,'pspa_category_big',7)

plot_2d(embed,hue=hue_pspa,hue_title='PSPA category',palette='tab20')

hue_pspa = get_hue_big(info,'pspa_category_small',10)

plot_2d(embed,hue=hue_pspa,hue_title='PSPA category (detailed)',palette='tab20')

# plot_2d(embed,hue=pspa_info_pssm.subfamily,hue_title='Subfamily')

Func that wrap all

def plot_group_pspa_category(info_df, # info df that contain key column for merge with feat_df
                             feat_df, # firt column is key column
                             n_neighbors,
                             min_dist,
                            ):
    merged= info_df.merge(feat_df)

    # Get UMAP embedding
    feat_col = feat_df.columns[1:]
    print('feature columns:', len(feat_col))
    feat = merged[feat_col]
    print('row numbers:', len(feat))
    embed = reduce_feature(feat,'umap',n_neighbors,min_dist=min_dist)

    # Colored by group
    plot_2d(embed,hue=merged.modi_group,hue_title='Group',palette='tab20')

    # Colored by pspa category
    hue_pspa = get_hue_big(merged,'pspa_category_big',10)
    plot_2d(embed,hue=hue_pspa,hue_title='PSPA category',palette='tab20')

    # Colored by pspa category in details
    hue_pspa = get_hue_big(merged,'pspa_category_small',10)
    plot_2d(embed,hue=hue_pspa,hue_title='PSPA category (detailed)',palette='tab20')

plot_group_pspa_category(pspa_info,pspa,n_neighbors=5,min_dist=0.6)

feature columns: 230

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

Umap plot of T5 on uniprot kd

t5 = pd.read_parquet('out/uniprot_kd_t5.parquet').reset_index()

t5.head()

	kd_ID	T5_0	T5_1	T5_2	T5_3	T5_4	T5_5	T5_6	T5_7	T5_8	...	T5_1014	T5_1015	T5_1016	T5_1017	T5_1018	T5_1019	T5_1020	T5_1021	T5_1022	T5_1023
0	A0A075F7E9_LERK1_ORYSI_KD1	0.014122	0.068848	0.016098	-0.001535	-0.001333	0.021378	0.030289	-0.062408	0.028442	...	-0.029327	0.014893	-0.006218	-0.069824	0.044067	-0.009636	-0.007458	0.021240	0.005234	-0.034637
1	A0A078BQP2_GCY25_CAEEL_KD1	-0.001307	-0.030319	0.020981	0.026642	-0.012787	0.034088	-0.028961	-0.105713	-0.018692	...	-0.038696	-0.036804	-0.016571	-0.072998	0.060852	0.044586	0.002766	-0.014633	0.046051	0.004398
2	A0A078CGE6_M3KE1_BRANA_KD1	0.054504	0.093750	-0.007454	0.018860	-0.042267	-0.005390	-0.004925	-0.046814	-0.013489	...	-0.016037	0.000752	-0.038391	-0.041382	0.069580	-0.022369	-0.026276	-0.001382	0.006645	-0.034485
3	A0A0G2K344_PK3CA_RAT_KD1	0.030807	0.127075	0.007271	-0.018631	0.007950	0.019165	-0.024902	-0.068176	-0.020386	...	-0.018188	-0.014389	-0.023575	0.006779	0.052277	-0.052155	-0.023071	0.034332	0.002714	-0.007095
4	A0A0H2ZM62_HK06_STRP2_KD1	0.020813	-0.063660	-0.013458	0.045715	-0.035828	0.039062	0.024445	-0.089233	0.005287	...	-0.054688	0.024170	0.007973	-0.101318	0.066223	0.005753	-0.048492	0.015091	0.024933	0.019699

5 rows × 1025 columns

plot_group_pspa_category(pspa_info,t5,n_neighbors=10,min_dist=0.3)

feature columns: 1024

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

Umap plot of T5 on Kinasecome sequence

info = Data.get_kinase_info()

info.columns

Index(['kinase', 'ID_coral', 'uniprot', 'ID_HGNC', 'modi_group', 'group',
       'family', 'subfamily_coral', 'subfamily', 'in_pspa_st', 'in_pspa_tyr',
       'in_pspa', 'in_cddm', 'kd_ID', 'active_D1_D2', 'active_kd_ID',
       'pspa_ID', 'pseudo', 'pspa_category_small', 'pspa_category_big',
       'cddm_big', 'cddm_small', 'length', 'human_uniprot_sequence',
       'kinasecom_domain', 'nucleus', 'cytosol', 'cytoskeleton',
       'plasma membrane', 'mitochondrion', 'Golgi apparatus',
       'endoplasmic reticulum', 'vesicle', 'centrosome', 'aggresome',
       'main_location'],
      dtype='object')

kd_seq = info[['kd_ID','kinasecom_domain']]

pspa_info_seq = pspa_info[cols].merge(kd_seq)

from katlas.feature import *

kinasecom = get_t5(pspa_info_seq,'kinasecom_domain')

You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565

embed = reduce_feature(kinasecom,'umap',5,min_dist=0.6)
plot_2d(embed,hue=pspa_info_seq.modi_group,hue_title='Group',palette='tab20')

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

Onehot encode

align = pd.read_parquet('out/uniprot_kd_align.parquet')

align.shape

(5536, 3434)

active=(align[1525]=='D')& (align[1724]=='D')

active.value_counts()

True     4209
False    1327
Name: count, dtype: int64

align = align[active]

# check 'pseudo alignment' nbs to know how to get freq_max
freq_max = pd.read_csv('out/align_freq_max_aa.csv')

freq_max

	position	aa	max_value
0	1549	N	0.815390
1	2618	D	0.809429
2	1724	D	0.800759
3	1525	D	0.791004
4	1730	G	0.775470
...	...	...	...
214	193	E	0.101879
215	640	G	0.101879
216	922	L	0.101337
217	603	R	0.101156
218	2581	G	0.100614

219 rows × 3 columns

onehot_col = freq_max[freq_max.max_value>0.05].position.sort_values().tolist()

onehot = align[onehot_col]

from sklearn.preprocessing import OneHotEncoder

def get_onehot(df):
    df=df.copy()
    encoded_df = pd.DataFrame(index=df.index)
    
    encoder = OneHotEncoder(sparse_output=False, dtype=int, handle_unknown='ignore')
    
    for col in df.columns:
        reshaped = df[[col]]  # keep as DataFrame
        encoded = encoder.fit_transform(reshaped)
        aa_labels = encoder.categories_[0]
        new_col_names = [f"{col}_{aa}" for aa in aa_labels]
        encoded_subdf = pd.DataFrame(encoded,index=df.index)
        encoded_subdf.columns=new_col_names
        encoded_df = pd.concat([encoded_df, encoded_subdf], axis=1)
    return encoded_df

encoded_active=get_onehot(onehot)

Umap of onehot

feat_df = encoded_active.reset_index()

plot_group_pspa_category(pspa_info,feat_df,n_neighbors=15,min_dist=0.7)

feature columns: 4071
row numbers: 376

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

embed = reduce_feature(encoded_active,
                        'umap',
                        complexity=20,
                       min_dist=0.5
                       )

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/logomaker/../umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

hue_is_PSPA=embed.index.isin(pspa_info.kd_ID)

plot_2d(embed,
        hue_title='Known PSPA',
        # alpha=0.5,
        hue = hue_is_PSPA,
        hue_order=[True, False],
        s=5,
        palette={True: 'red', False: 'gray'})

PSPA kd plot

encoded_df = get_onehot(onehot_pspa)

encoded_df

	65_-	65_A	65_C	65_E	65_F	65_I	65_K	65_L	65_N	65_Q	...	3192_K	3192_L	3192_M	3192_Q	3192_R	3192_S	3192_T	3192_V	3192_W	3192_Y
kd_ID
Q2M2I8_AAK1_HUMAN_KD1	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P27037_AVR2A_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
Q13705_AVR2B_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P31749_AKT1_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P31751_AKT2_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
P17948_VGFR1_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	0
P35968_VGFR2_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P35916_VGFR3_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	1	0	0	0	0	0	0	0	0
P07947_YES_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
P43403_ZAP70_HUMAN_KD1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1

362 rows × 5121 columns

umap2d = reduce_feature(encoded_df,
                        'umap',
                        complexity=15,
                       min_dist=0.7
                       )

/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/sklearn/utils/deprecation.py:151: FutureWarning: 'force_all_finite' was renamed to 'ensure_all_finite' in 1.6 and will be removed in 1.8.
  warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/umap/umap_.py:1952: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.
  warn(

hue_pspa = pspa_active_kd_remove2kd.set_index('kd_ID')['pspa_category']
hue_group = pspa_active_kd_remove2kd.set_index('kd_ID')['group']

enrich_group = hue_pspa.value_counts()[hue_pspa.value_counts()>10].index

hue_pspa_partial = hue_pspa[hue_pspa.isin(enrich_group)]

group_color = pd.read_csv('raw/group_color.csv')

group_color = group_color.set_index('Label')['Color'].to_dict()

plot_umap(umap2d,box_title='Group',hue=hue_group, palette=group_color)

pspa_color = get_color_dict(hue_pspa_partial.unique())

def rgb_to_hex(rgb_dict):
    """
    Convert a dictionary with RGB values in [0, 1] to hex color codes.
    """
    hex_dict = {
        key: '#{:02X}{:02X}{:02X}'.format(
            int(r * 255), int(g * 255), int(b * 255)
        )
        for key, (r, g, b) in rgb_dict.items()
    }
    return hex_dict

pspa_color = rgb_to_hex(pspa_color)

pspa_color = pd.DataFrame.from_dict(pspa_color,orient='index').reset_index()

pspa_color.columns=['Label','Color']

# pspa_color.to_csv('raw/pspa_color.csv',index=False)

pspa_color=pd.read_csv('raw/pspa_color.csv')

pspa_color = pspa_color.set_index('Label')['Color'].to_dict()

plot_umap(umap2d,box_title='PSPA category',hue=hue_pspa_partial,palette=pspa_color)

Old

Merge with human PSPA

human_active = kd[kd.Organism=='Homo sapiens (Human)']

human_active[human_active.Uniprot.duplicated(keep=False)]

	kd_ID	Uniprot	Entry Name	Protein names	Gene Names	Gene Names (primary)	Organism	kd_note	kd_evidence	kd_start	...	Interacts with	Subunit structure	Function [CC]	Activity regulation	full_seq	D1	D2	D3	N1	active_D1_D2
732	O75582_KS6A5_HUMAN_KD1	O75582	KS6A5_HUMAN	Ribosomal protein S6 kinase alpha-5 (S6K-alpha...	RPS6KA5 MSK1	RPS6KA5	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	49	...	P67870; Q9UI47-2; Q9Y4C1; Q9NYL2; Q16539; Q9Y4C1	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGI...	1	1	1	1	1
733	O75582_KS6A5_HUMAN_KD2	O75582	KS6A5_HUMAN	Ribosomal protein S6 kinase alpha-5 (S6K-alpha...	RPS6KA5 MSK1	RPS6KA5	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	P67870; Q9UI47-2; Q9Y4C1; Q9NYL2; Q16539; Q9Y4C1	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGI...	1	1	1	1	1
734	O75676_KS6A4_HUMAN_KD1	O75676	KS6A4_HUMAN	Ribosomal protein S6 kinase alpha-4 (S6K-alpha...	RPS6KA4 MSK2	RPS6KA4	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	33	...	Q16539; O14901	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGK...	1	1	1	1	1
735	O75676_KS6A4_HUMAN_KD2	O75676	KS6A4_HUMAN	Ribosomal protein S6 kinase alpha-4 (S6K-alpha...	RPS6KA4 MSK2	RPS6KA4	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	411	...	Q16539; O14901	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGK...	1	1	1	1	1
1462	P51812_KS6A3_HUMAN_KD1	P51812	KS6A3_HUMAN	Ribosomal protein S6 kinase alpha-3 (S6K-alpha...	RPS6KA3 ISPK1 MAPKAPK1B RSK2	RPS6KA3	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	68	...	P46379-2; P67870; P09471; P08238; O14901; P284...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVS...	1	1	1	1	1
1463	P51812_KS6A3_HUMAN_KD2	P51812	KS6A3_HUMAN	Ribosomal protein S6 kinase alpha-3 (S6K-alpha...	RPS6KA3 ISPK1 MAPKAPK1B RSK2	RPS6KA3	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	422	...	P46379-2; P67870; P09471; P08238; O14901; P284...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVS...	1	1	1	1	1
1957	Q15349_KS6A2_HUMAN_KD1	Q15349	KS6A2_HUMAN	Ribosomal protein S6 kinase alpha-2 (S6K-alpha...	RPS6KA2 MAPKAPK1C RSK3	RPS6KA2	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	59	...	P05067; P15056; P67870; O14901; P28482; Q02156...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHH...	1	1	1	1	1
1958	Q15349_KS6A2_HUMAN_KD2	Q15349	KS6A2_HUMAN	Ribosomal protein S6 kinase alpha-2 (S6K-alpha...	RPS6KA2 MAPKAPK1C RSK3	RPS6KA2	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	415	...	P05067; P15056; P67870; O14901; P28482; Q02156...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHH...	1	1	1	1	1
1960	Q15418_KS6A1_HUMAN_KD1	Q15418	KS6A1_HUMAN	Ribosomal protein S6 kinase alpha-1 (S6K-alpha...	RPS6KA1 MAPKAPK1A RSK1	RPS6KA1	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	62	...	O43823; Q16543; P46527; P08238; P28482; P04271...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLKEPWPLMELVPLDPENGQTSGEEAGLQPSKDEGVLKEISI...	1	1	1	1	1
1961	Q15418_KS6A1_HUMAN_KD2	Q15418	KS6A1_HUMAN	Ribosomal protein S6 kinase alpha-1 (S6K-alpha...	RPS6KA1 MAPKAPK1A RSK1	RPS6KA1	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	418	...	O43823; Q16543; P46527; P08238; P28482; P04271...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLKEPWPLMELVPLDPENGQTSGEEAGLQPSKDEGVLKEISI...	1	1	1	1	1
1965	Q15772_SPEG_HUMAN_KD1	Q15772	SPEG_HUMAN	Striated muscle preferentially expressed prote...	SPEG APEG1 KIAA1297	SPEG	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	1601	...	Q99873; O75031; Q6FHY5; Q9NRD5; Q99873-3	SUBUNIT: Interacts with MTM1. Isoform 3 is fou...	FUNCTION: Isoform 3 may have a role in regulat...	NaN	MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFL...	1	1	1	1	1
1966	Q15772_SPEG_HUMAN_KD2	Q15772	SPEG_HUMAN	Striated muscle preferentially expressed prote...	SPEG APEG1 KIAA1297	SPEG	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	2966	...	Q99873; O75031; Q6FHY5; Q9NRD5; Q99873-3	SUBUNIT: Interacts with MTM1. Isoform 3 is fou...	FUNCTION: Isoform 3 may have a role in regulat...	NaN	MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFL...	1	1	1	1	1
2612	Q5VST9_OBSCN_HUMAN_KD1	Q5VST9	OBSCN_HUMAN	Obscurin (EC 2.7.11.1) (Obscurin-RhoGEF) (Obsc...	OBSCN KIAA1556 KIAA1639	OBSCN	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	6468	...	Q8WZ42; P16157; P16157-17	SUBUNIT: Interacts (via protein kinase domain ...	FUNCTION: Structural component of striated mus...	NaN	MDQPQFSGAPRFLTRPKAFVVSVGKDATLSCQIVGNPTPQVSWEKD...	1	1	1	1	1
2613	Q5VST9_OBSCN_HUMAN_KD2	Q5VST9	OBSCN_HUMAN	Obscurin (EC 2.7.11.1) (Obscurin-RhoGEF) (Obsc...	OBSCN KIAA1556 KIAA1639	OBSCN	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	7672	...	Q8WZ42; P16157; P16157-17	SUBUNIT: Interacts (via protein kinase domain ...	FUNCTION: Structural component of striated mus...	NaN	MDQPQFSGAPRFLTRPKAFVVSVGKDATLSCQIVGNPTPQVSWEKD...	1	1	1	1	1
4042	Q9UK32_KS6A6_HUMAN_KD1	Q9UK32	KS6A6_HUMAN	Ribosomal protein S6 kinase alpha-6 (S6K-alpha...	RPS6KA6 RSK4	RPS6KA6	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	73	...	Q7Z698	SUBUNIT: Forms a complex with MAPK3/ERK1 but n...	FUNCTION: Constitutively active serine/threoni...	ACTIVITY REGULATION: Constitutively activated ...	MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSC...	1	1	1	1	1
4043	Q9UK32_KS6A6_HUMAN_KD2	Q9UK32	KS6A6_HUMAN	Ribosomal protein S6 kinase alpha-6 (S6K-alpha...	RPS6KA6 RSK4	RPS6KA6	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	Q7Z698	SUBUNIT: Forms a complex with MAPK3/ERK1 but n...	FUNCTION: Constitutively active serine/threoni...	ACTIVITY REGULATION: Constitutively activated ...	MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSC...	1	1	1	1	1

16 rows × 32 columns

Kinase with two KDs have duplicates in the Uniprot ID:

human_active[human_active.Uniprot.duplicated(keep=False)]

	kd_ID	Uniprot	Entry Name	Protein names	Gene Names	Gene Names (primary)	Organism	kd_note	kd_evidence	kd_start	...	Interacts with	Subunit structure	Function [CC]	Activity regulation	full_seq	D1	D2	D3	N1	active_D1_D2
732	O75582_KS6A5_HUMAN_KD1	O75582	KS6A5_HUMAN	Ribosomal protein S6 kinase alpha-5 (S6K-alpha...	RPS6KA5 MSK1	RPS6KA5	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	49	...	P67870; Q9UI47-2; Q9Y4C1; Q9NYL2; Q16539; Q9Y4C1	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGI...	1	1	1	1	1
733	O75582_KS6A5_HUMAN_KD2	O75582	KS6A5_HUMAN	Ribosomal protein S6 kinase alpha-5 (S6K-alpha...	RPS6KA5 MSK1	RPS6KA5	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	P67870; Q9UI47-2; Q9Y4C1; Q9NYL2; Q16539; Q9Y4C1	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGI...	1	1	1	1	1
734	O75676_KS6A4_HUMAN_KD1	O75676	KS6A4_HUMAN	Ribosomal protein S6 kinase alpha-4 (S6K-alpha...	RPS6KA4 MSK2	RPS6KA4	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	33	...	Q16539; O14901	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGK...	1	1	1	1	1
735	O75676_KS6A4_HUMAN_KD2	O75676	KS6A4_HUMAN	Ribosomal protein S6 kinase alpha-4 (S6K-alpha...	RPS6KA4 MSK2	RPS6KA4	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	411	...	Q16539; O14901	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGK...	1	1	1	1	1
1462	P51812_KS6A3_HUMAN_KD1	P51812	KS6A3_HUMAN	Ribosomal protein S6 kinase alpha-3 (S6K-alpha...	RPS6KA3 ISPK1 MAPKAPK1B RSK2	RPS6KA3	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	68	...	P46379-2; P67870; P09471; P08238; O14901; P284...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVS...	1	1	1	1	1
1463	P51812_KS6A3_HUMAN_KD2	P51812	KS6A3_HUMAN	Ribosomal protein S6 kinase alpha-3 (S6K-alpha...	RPS6KA3 ISPK1 MAPKAPK1B RSK2	RPS6KA3	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	422	...	P46379-2; P67870; P09471; P08238; O14901; P284...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVS...	1	1	1	1	1
1957	Q15349_KS6A2_HUMAN_KD1	Q15349	KS6A2_HUMAN	Ribosomal protein S6 kinase alpha-2 (S6K-alpha...	RPS6KA2 MAPKAPK1C RSK3	RPS6KA2	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	59	...	P05067; P15056; P67870; O14901; P28482; Q02156...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHH...	1	1	1	1	1
1958	Q15349_KS6A2_HUMAN_KD2	Q15349	KS6A2_HUMAN	Ribosomal protein S6 kinase alpha-2 (S6K-alpha...	RPS6KA2 MAPKAPK1C RSK3	RPS6KA2	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	415	...	P05067; P15056; P67870; O14901; P28482; Q02156...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHH...	1	1	1	1	1
1960	Q15418_KS6A1_HUMAN_KD1	Q15418	KS6A1_HUMAN	Ribosomal protein S6 kinase alpha-1 (S6K-alpha...	RPS6KA1 MAPKAPK1A RSK1	RPS6KA1	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	62	...	O43823; Q16543; P46527; P08238; P28482; P04271...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLKEPWPLMELVPLDPENGQTSGEEAGLQPSKDEGVLKEISI...	1	1	1	1	1
1961	Q15418_KS6A1_HUMAN_KD2	Q15418	KS6A1_HUMAN	Ribosomal protein S6 kinase alpha-1 (S6K-alpha...	RPS6KA1 MAPKAPK1A RSK1	RPS6KA1	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	418	...	O43823; Q16543; P46527; P08238; P28482; P04271...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLKEPWPLMELVPLDPENGQTSGEEAGLQPSKDEGVLKEISI...	1	1	1	1	1
1965	Q15772_SPEG_HUMAN_KD1	Q15772	SPEG_HUMAN	Striated muscle preferentially expressed prote...	SPEG APEG1 KIAA1297	SPEG	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	1601	...	Q99873; O75031; Q6FHY5; Q9NRD5; Q99873-3	SUBUNIT: Interacts with MTM1. Isoform 3 is fou...	FUNCTION: Isoform 3 may have a role in regulat...	NaN	MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFL...	1	1	1	1	1
1966	Q15772_SPEG_HUMAN_KD2	Q15772	SPEG_HUMAN	Striated muscle preferentially expressed prote...	SPEG APEG1 KIAA1297	SPEG	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	2966	...	Q99873; O75031; Q6FHY5; Q9NRD5; Q99873-3	SUBUNIT: Interacts with MTM1. Isoform 3 is fou...	FUNCTION: Isoform 3 may have a role in regulat...	NaN	MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFL...	1	1	1	1	1
2612	Q5VST9_OBSCN_HUMAN_KD1	Q5VST9	OBSCN_HUMAN	Obscurin (EC 2.7.11.1) (Obscurin-RhoGEF) (Obsc...	OBSCN KIAA1556 KIAA1639	OBSCN	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	6468	...	Q8WZ42; P16157; P16157-17	SUBUNIT: Interacts (via protein kinase domain ...	FUNCTION: Structural component of striated mus...	NaN	MDQPQFSGAPRFLTRPKAFVVSVGKDATLSCQIVGNPTPQVSWEKD...	1	1	1	1	1
2613	Q5VST9_OBSCN_HUMAN_KD2	Q5VST9	OBSCN_HUMAN	Obscurin (EC 2.7.11.1) (Obscurin-RhoGEF) (Obsc...	OBSCN KIAA1556 KIAA1639	OBSCN	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	7672	...	Q8WZ42; P16157; P16157-17	SUBUNIT: Interacts (via protein kinase domain ...	FUNCTION: Structural component of striated mus...	NaN	MDQPQFSGAPRFLTRPKAFVVSVGKDATLSCQIVGNPTPQVSWEKD...	1	1	1	1	1
4042	Q9UK32_KS6A6_HUMAN_KD1	Q9UK32	KS6A6_HUMAN	Ribosomal protein S6 kinase alpha-6 (S6K-alpha...	RPS6KA6 RSK4	RPS6KA6	Homo sapiens (Human)	Protein kinase 1	ECO:0000255|PROSITE-ProRule:PRU00159	73	...	Q7Z698	SUBUNIT: Forms a complex with MAPK3/ERK1 but n...	FUNCTION: Constitutively active serine/threoni...	ACTIVITY REGULATION: Constitutively activated ...	MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSC...	1	1	1	1	1
4043	Q9UK32_KS6A6_HUMAN_KD2	Q9UK32	KS6A6_HUMAN	Ribosomal protein S6 kinase alpha-6 (S6K-alpha...	RPS6KA6 RSK4	RPS6KA6	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	Q7Z698	SUBUNIT: Forms a complex with MAPK3/ERK1 but n...	FUNCTION: Constitutively active serine/threoni...	ACTIVITY REGULATION: Constitutively activated ...	MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSC...	1	1	1	1	1

16 rows × 32 columns

info=Data.get_kinase_info()

info.to_csv('test.csv',index=False)

kinase_map = info[['kinase','uniprot']]

Remove _b pseudo to get unique uniprot

kinase_map = kinase_map[~kinase_map.kinase.str.contains('_b')]

kinase_map = kinase_map.set_index('kinase')['uniprot']

kinase_map[kinase_map.duplicated(keep=False)]

kinase
TEX14    Q8IWB6
TEX14    Q8IWB6
Name: uniprot, dtype: object

kinase_map = kinase_map.drop_duplicates()

# kinase_map.to_csv('out/kinase_map.csv')

kinase_map

kinase
AAK1      Q2M2I8
ABL1      P00519
ABL2      P42684
TNK2      Q07912
ACVR2A    P27037
           ...  
YSK1      O00506
ZAK       Q9NYL2
ZAP70     P43403
EEF2K     O00418
FAM20C    Q8IXL6
Name: uniprot, Length: 509, dtype: object

Add uniprot ID to PSPA

pspa = Data.get_pspa_all_norm()

pspa.index.duplicated().sum()

0

a = pd.DataFrame(pspa.index)

a['name']=a.kinase.str.split('_').str[0]

a.groupby('name').agg({'kinase': lambda x: ','.join(x.unique())}).to_csv('raw/pspa_name.csv')

idx = pspa.index.str.split('_').str[0]

idx.map(kinase_map).isna().sum()

0

idx

Index(['AAK1', 'ACVR2A', 'ACVR2B', 'AKT1', 'AKT2', 'AKT3', 'ALK2', 'ALK4',
       'ALPHAK3', 'AMPKA1',
       ...
       'NTRK3', 'TXK', 'TYK2', 'TYRO3', 'FLT1', 'KDR', 'FLT4', 'WEE1', 'YES1',
       'ZAP70'],
      dtype='object', name='kinase', length=396)

pspa = pspa.reset_index()

uniprot = pd.DataFrame(idx.map(kinase_map))

uniprot.columns=['uniprot']

pspa = pd.concat([uniprot,pspa],axis=1)

# pspa.to_csv('out/pspa_uniprot.csv',index=False)

PSPA with duplicated uniprot:

pspa[pspa.uniprot.duplicated(keep=False)].sort_values('uniprot')

	uniprot	kinase	-5P	-5G	-5A	-5C	-5S	-5T	-5V	-5I	...	5H	5K	5R	5Q	5N	5D	5E	5s	5t	5y
22	Q13873	BMPR2	0.0558	0.0621	0.0638	0.0716	0.0571	0.0571	0.0597	0.0571	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
309	Q13873	BMPR2_TYR	0.0580	0.0617	0.0603	0.0661	0.0613	0.0613	0.0566	0.0580	...	0.0660	0.0665	0.0766	0.0607	0.0619	0.0648	0.0649	0.0702	0.0702	0.0653
212	Q15118	PDHK1	0.0451	0.0697	0.0594	0.0625	0.0594	0.0594	0.0573	0.0590	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
369	Q15118	PDHK1_TYR	0.0590	0.0608	0.0528	0.0620	0.0608	0.0608	0.0439	0.0528	...	0.0676	0.0490	0.0539	0.0620	0.0647	0.0949	0.0797	0.0633	0.0633	0.0820
213	Q16654	PDHK4	0.0452	0.0645	0.0665	0.0672	0.0622	0.0622	0.0515	0.0619	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
371	Q16654	PDHK4_TYR	0.0697	0.0690	0.0545	0.0772	0.0642	0.0642	0.0426	0.0483	...	0.0642	0.0486	0.0498	0.0755	0.0661	0.0836	0.0830	0.0686	0.0686	0.0892
221	Q9BXM7	PINK1	0.0516	0.0474	0.0612	0.0570	0.0523	0.0523	0.0474	0.0507	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
372	Q9BXM7	PINK1_TYR	0.0569	0.0585	0.0572	0.0643	0.0579	0.0579	0.0546	0.0555	...	0.0666	0.0818	0.1049	0.0636	0.0635	0.0525	0.0508	0.0516	0.0516	0.0611

8 rows × 238 columns

pspa_unique_uniprot = pspa[~pspa.uniprot.duplicated()]

Remove other _TYR

Also remove other TYR due to their overall low specificity

pspa_no_TYR = pspa_unique_uniprot[~pspa_unique_uniprot.kinase.str.contains('_TYR')]

pspa_no_TYR = pspa_no_TYR.dropna(axis=1)

# pspa_no_TYR.to_csv('out/pspa_uniprot_unique_no_TYR.csv',index=False)

active_kd = pd.read_excel('out/uniprot_kd_active_D1_D2.xlsx')

human_active[human_active.Uniprot.duplicated()]

	kd_ID	Uniprot	Entry Name	Protein names	Gene Names	Gene Names (primary)	Organism	kd_note	kd_evidence	kd_start	...	Interacts with	Subunit structure	Function [CC]	Activity regulation	full_seq	D1	D2	D3	N1	active_D1_D2
989	O75582_KS6A5_HUMAN_KD2	O75582	KS6A5_HUMAN	Ribosomal protein S6 kinase alpha-5 (S6K-alpha...	RPS6KA5 MSK1	RPS6KA5	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	P67870; Q9UI47-2; Q9Y4C1; Q9NYL2; Q16539; Q9Y4C1	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGI...	1	1	1	1	1
991	O75676_KS6A4_HUMAN_KD2	O75676	KS6A4_HUMAN	Ribosomal protein S6 kinase alpha-4 (S6K-alpha...	RPS6KA4 MSK2	RPS6KA4	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	411	...	Q16539; O14901	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Activated by phosphorylat...	MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGK...	1	1	1	1	1
1919	P51812_KS6A3_HUMAN_KD2	P51812	KS6A3_HUMAN	Ribosomal protein S6 kinase alpha-3 (S6K-alpha...	RPS6KA3 ISPK1 MAPKAPK1B RSK2	RPS6KA3	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	422	...	P46379-2; P67870; P09471; P08238; O14901; P284...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVS...	1	1	1	1	1
2561	Q15349_KS6A2_HUMAN_KD2	Q15349	KS6A2_HUMAN	Ribosomal protein S6 kinase alpha-2 (S6K-alpha...	RPS6KA2 MAPKAPK1C RSK3	RPS6KA2	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	415	...	P05067; P15056; P67870; O14901; P28482; Q02156...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHH...	1	1	1	1	1
2564	Q15418_KS6A1_HUMAN_KD2	Q15418	KS6A1_HUMAN	Ribosomal protein S6 kinase alpha-1 (S6K-alpha...	RPS6KA1 MAPKAPK1A RSK1	RPS6KA1	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	418	...	O43823; Q16543; P46527; P08238; P28482; P04271...	SUBUNIT: Forms a complex with either MAPK1/ERK...	FUNCTION: Serine/threonine-protein kinase that...	ACTIVITY REGULATION: Upon extracellular signal...	MPLAQLKEPWPLMELVPLDPENGQTSGEEAGLQPSKDEGVLKEISI...	1	1	1	1	1
2569	Q15772_SPEG_HUMAN_KD2	Q15772	SPEG_HUMAN	Striated muscle preferentially expressed prote...	SPEG APEG1 KIAA1297	SPEG	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	2966	...	Q99873; O75031; Q6FHY5; Q9NRD5; Q99873-3	SUBUNIT: Interacts with MTM1. Isoform 3 is fou...	FUNCTION: Isoform 3 may have a role in regulat...	NaN	MQKARGTRGEDAGTRAPPSPGVPPKRAKVGAGGGAPVAVAGAPVFL...	1	1	1	1	1
3481	Q5VST9_OBSCN_HUMAN_KD2	Q5VST9	OBSCN_HUMAN	Obscurin (EC 2.7.11.1) (Obscurin-RhoGEF) (Obsc...	OBSCN KIAA1556 KIAA1639	OBSCN	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	7672	...	Q8WZ42; P16157; P16157-17	SUBUNIT: Interacts (via protein kinase domain ...	FUNCTION: Structural component of striated mus...	NaN	MDQPQFSGAPRFLTRPKAFVVSVGKDATLSCQIVGNPTPQVSWEKD...	1	1	1	1	1
5339	Q9UK32_KS6A6_HUMAN_KD2	Q9UK32	KS6A6_HUMAN	Ribosomal protein S6 kinase alpha-6 (S6K-alpha...	RPS6KA6 RSK4	RPS6KA6	Homo sapiens (Human)	Protein kinase 2	ECO:0000255|PROSITE-ProRule:PRU00159	426	...	Q7Z698	SUBUNIT: Forms a complex with MAPK3/ERK1 but n...	FUNCTION: Constitutively active serine/threoni...	ACTIVITY REGULATION: Constitutively activated ...	MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSC...	1	1	1	1	1

8 rows × 32 columns

pspa_no_TYR.uniprot.isin(human_active.Uniprot).value_counts()

uniprot
True     368
False     13
Name: count, dtype: int64

PSPA not in human uniprot active kd:

pspa_no_TYR[~pspa_no_TYR.uniprot.isin(human_active.Uniprot)]

	uniprot	kinase	-5P	-5G	-5A	-5C	-5S	-5T	-5V	-5I	...	4E	4s	4t	4y	0s	0t	0y	0S	0T	0Y
8	Q96QP1	ALPHAK3	0.0571	0.0478	0.0253	0.0384	0.0571	0.0571	0.0586	0.0602	...	0.0747	0.1142	0.1142	0.1744	0.1319	1.0000	0.0	0.1319	1.0000	0.0
18	O14874	BCKDK	0.0482	0.0672	0.0598	0.0694	0.0566	0.0566	0.0517	0.0467	...	0.0563	0.0401	0.0401	0.0859	1.0000	0.2348	0.0	1.0000	0.2348	0.0
26	O43683	BUB1	0.0899	0.0222	0.0249	0.0470	0.0286	0.0286	0.0319	0.0659	...	0.0250	0.0261	0.0261	0.0273	0.5413	1.0000	0.0	0.5413	1.0000	0.0
59	Q96QT4	CHAK1	0.0649	0.0823	0.0686	0.0995	0.0608	0.0608	0.0431	0.0313	...	0.0330	0.0400	0.0400	0.0318	1.0000	0.7003	0.0	1.0000	0.7003	0.0
60	Q9BX84	CHAK2	0.0532	0.0844	0.0761	0.0626	0.0588	0.0588	0.0439	0.0406	...	0.0584	0.0610	0.0610	0.0538	1.0000	0.5794	0.0	1.0000	0.5794	0.0
93	O00418	EEF2K	0.0603	0.0627	0.0635	0.0586	0.0602	0.0602	0.0584	0.0597	...	0.0379	0.0620	0.0620	0.0434	0.2741	1.0000	0.0	0.2741	1.0000	0.0
98	Q8IXL6	FAM20C	0.0496	0.0620	0.0669	0.0649	0.0564	0.0564	0.0564	0.0442	...	0.1123	0.1801	0.1801	0.1610	1.0000	0.1276	0.0	1.0000	0.1276	0.0
111	Q8TF76	HASPIN	0.0775	0.0522	0.0492	0.0495	0.0522	0.0522	0.0578	0.1577	...	0.0237	0.0381	0.0381	0.0347	0.3998	1.0000	0.0	0.3998	1.0000	0.0
212	Q15118	PDHK1	0.0451	0.0697	0.0594	0.0625	0.0594	0.0594	0.0573	0.0590	...	0.0535	0.0548	0.0548	0.0575	1.0000	0.4886	0.0	1.0000	0.4886	0.0
213	Q16654	PDHK4	0.0452	0.0645	0.0665	0.0672	0.0622	0.0622	0.0515	0.0619	...	0.0608	0.0848	0.0848	0.0628	1.0000	0.4640	0.0	1.0000	0.4640	0.0
270	Q96SB4	SRPK1	0.0594	0.0753	0.0889	0.0814	0.0525	0.0525	0.0517	0.0468	...	0.0521	0.0701	0.0701	0.0622	1.0000	0.2897	0.0	1.0000	0.2897	0.0
271	P78362	SRPK2	0.0446	0.0660	0.0596	0.0694	0.0491	0.0491	0.0452	0.0349	...	0.0440	0.0562	0.0562	0.0559	1.0000	0.1949	0.0	1.0000	0.1949	0.0
272	Q9UPE1	SRPK3	0.0435	0.0618	0.0556	0.0622	0.0541	0.0541	0.0527	0.0541	...	0.0394	0.0514	0.0514	0.0544	1.0000	0.4323	0.0	1.0000	0.4323	0.0

13 rows × 215 columns

pspa_active_kd = pspa_no_TYR[pspa_no_TYR.uniprot.isin(human_active.Uniprot)]

# some category test _TYR and have double annotation, split by _
info['pspa_category'] = info.pspa_category_big.str.split('_').str[0]

info['pspa_category_detail']= info.pspa_category_small.str.split('_').str[0]

info = info[info.pseudo=='0']

category = info[['uniprot','kinase','group','pspa_category','pspa_category_detail']]

head = pd.DataFrame(pspa_active_kd.uniprot).merge(category)

pspa_active_kd = pspa_active_kd.reset_index(drop=True)

head

	uniprot	kinase	group	pspa_category	pspa_category_detail
0	Q2M2I8	AAK1	Other	NAK	NAK
1	P27037	ACVR2A	TKL	acidophilic	TGFBR
2	Q13705	ACVR2B	TKL	acidophilic	TGFBR
3	P31749	AKT1	AGC	basophilic	AKT/ROCK
4	P31751	AKT2	AGC	basophilic	AKT/ROCK
...	...	...	...	...	...
363	P17948	FLT1	TK	FGF and VEGF receptors	FGF and VEGF receptors
364	P35968	KDR	TK	FGF and VEGF receptors	FGF and VEGF receptors
365	P35916	FLT4	TK	FGF and VEGF receptors	FGF and VEGF receptors
366	P07947	YES1	TK	SRC	SRC
367	P43403	ZAP70	TK	SYK and FAK	SYK and FAK

368 rows × 5 columns

pspa_active_kd = pd.concat([head,pspa_active_kd.iloc[:,2:]],axis=1)

# pspa_active_kd.to_csv('out/pspa_uniprot_unique_no_TYR_category.csv',index=False)

pspa_active_kd

	uniprot	kinase	group	pspa_category	pspa_category_detail	-5P	-5G	-5A	-5C	-5S	...	4E	4s	4t	4y	0s	0t	0y	0S	0T	0Y
0	Q2M2I8	AAK1	Other	NAK	NAK	0.0720	0.0245	0.0284	0.0456	0.0425	...	0.0457	0.0251	0.0251	0.0270	0.1013	1.0000	0.0	0.1013	1.0000	0.0
1	P27037	ACVR2A	TKL	acidophilic	TGFBR	0.0415	0.0481	0.0584	0.0489	0.0578	...	0.0640	0.0703	0.0703	0.0589	0.9833	1.0000	0.0	0.9833	1.0000	0.0
2	Q13705	ACVR2B	TKL	acidophilic	TGFBR	0.0533	0.0517	0.0566	0.0772	0.0533	...	0.0697	0.0761	0.0761	0.0637	0.9593	1.0000	0.0	0.9593	1.0000	0.0
3	P31749	AKT1	AGC	basophilic	AKT/ROCK	0.0603	0.0594	0.0552	0.0605	0.0516	...	0.0312	0.0393	0.0393	0.0263	1.0000	0.6440	0.0	1.0000	0.6440	0.0
4	P31751	AKT2	AGC	basophilic	AKT/ROCK	0.0602	0.0617	0.0643	0.0582	0.0534	...	0.0350	0.0548	0.0548	0.0417	1.0000	0.6077	0.0	1.0000	0.6077	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
363	P17948	FLT1	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0642	0.0688	0.0597	0.0618	0.0614	...	0.0510	0.0677	0.0677	0.0380	0.0000	0.0000	1.0	0.0000	0.0000	1.0
364	P35968	KDR	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0634	0.0672	0.0556	0.0517	0.0541	...	0.0338	0.0300	0.0300	0.0292	0.0000	0.0000	1.0	0.0000	0.0000	1.0
365	P35916	FLT4	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0457	0.0531	0.0488	0.0553	0.0512	...	0.0497	0.0445	0.0445	0.0500	0.0000	0.0000	1.0	0.0000	0.0000	1.0
366	P07947	YES1	TK	SRC	SRC	0.0677	0.0571	0.0537	0.0530	0.0527	...	0.0492	0.0371	0.0371	0.0467	0.0000	0.0000	1.0	0.0000	0.0000	1.0
367	P43403	ZAP70	TK	SYK and FAK	SYK and FAK	0.0602	0.0880	0.0623	0.0496	0.0471	...	0.0558	0.0440	0.0440	0.0318	0.0000	0.0000	1.0	0.0000	0.0000	1.0

368 rows × 218 columns

id_map = human_active[['Uniprot','kd_ID']]
id_map.columns=['uniprot','kd_ID']

active_id = id_map[id_map.uniprot.isin(pspa_active_kd.uniprot)]

Uniprot in pspa with two kinase domains

pspa_2kd = active_id[active_id.uniprot.duplicated(keep=False)].sort_values('uniprot')

pspa_2kd

	uniprot	kd_ID
988	O75582	O75582_KS6A5_HUMAN_KD1
989	O75582	O75582_KS6A5_HUMAN_KD2
990	O75676	O75676_KS6A4_HUMAN_KD1
991	O75676	O75676_KS6A4_HUMAN_KD2
1918	P51812	P51812_KS6A3_HUMAN_KD1
1919	P51812	P51812_KS6A3_HUMAN_KD2
2560	Q15349	Q15349_KS6A2_HUMAN_KD1
2561	Q15349	Q15349_KS6A2_HUMAN_KD2
2563	Q15418	Q15418_KS6A1_HUMAN_KD1
2564	Q15418	Q15418_KS6A1_HUMAN_KD2
5338	Q9UK32	Q9UK32_KS6A6_HUMAN_KD1
5339	Q9UK32	Q9UK32_KS6A6_HUMAN_KD2

pspa_2kd.uniprot.unique()

array(['O75582', 'O75676', 'P51812', 'Q15349', 'Q15418', 'Q9UK32'],
      dtype=object)

pspa_active_kd_remove2kd = pspa_active_kd[~pspa_active_kd.uniprot.isin(pspa_2kd.uniprot.unique())]

pspa_active_kd_remove2kd = pspa_active_kd_remove2kd.reset_index(drop=True)

head = pd.DataFrame(pspa_active_kd_remove2kd.uniprot).merge(id_map)

head

	uniprot	kd_ID
0	Q2M2I8	Q2M2I8_AAK1_HUMAN_KD1
1	P27037	P27037_AVR2A_HUMAN_KD1
2	Q13705	Q13705_AVR2B_HUMAN_KD1
3	P31749	P31749_AKT1_HUMAN_KD1
4	P31751	P31751_AKT2_HUMAN_KD1
...	...	...
357	P17948	P17948_VGFR1_HUMAN_KD1
358	P35968	P35968_VGFR2_HUMAN_KD1
359	P35916	P35916_VGFR3_HUMAN_KD1
360	P07947	P07947_YES_HUMAN_KD1
361	P43403	P43403_ZAP70_HUMAN_KD1

362 rows × 2 columns

pspa_active_kd_remove2kd = pd.concat([head,pspa_active_kd_remove2kd.iloc[:,1:]],axis=1)

# pspa_active_kd_remove2kd.to_csv('out/pspa_uniprot_unique_no_TYR_category_remove2kd.csv')

pspa_active_kd_remove2kd

	uniprot	kd_ID	kinase	group	pspa_category	pspa_category_detail	-5P	-5G	-5A	-5C	...	4E	4s	4t	4y	0s	0t	0y	0S	0T	0Y
0	Q2M2I8	Q2M2I8_AAK1_HUMAN_KD1	AAK1	Other	NAK	NAK	0.0720	0.0245	0.0284	0.0456	...	0.0457	0.0251	0.0251	0.0270	0.1013	1.0000	0.0	0.1013	1.0000	0.0
1	P27037	P27037_AVR2A_HUMAN_KD1	ACVR2A	TKL	acidophilic	TGFBR	0.0415	0.0481	0.0584	0.0489	...	0.0640	0.0703	0.0703	0.0589	0.9833	1.0000	0.0	0.9833	1.0000	0.0
2	Q13705	Q13705_AVR2B_HUMAN_KD1	ACVR2B	TKL	acidophilic	TGFBR	0.0533	0.0517	0.0566	0.0772	...	0.0697	0.0761	0.0761	0.0637	0.9593	1.0000	0.0	0.9593	1.0000	0.0
3	P31749	P31749_AKT1_HUMAN_KD1	AKT1	AGC	basophilic	AKT/ROCK	0.0603	0.0594	0.0552	0.0605	...	0.0312	0.0393	0.0393	0.0263	1.0000	0.6440	0.0	1.0000	0.6440	0.0
4	P31751	P31751_AKT2_HUMAN_KD1	AKT2	AGC	basophilic	AKT/ROCK	0.0602	0.0617	0.0643	0.0582	...	0.0350	0.0548	0.0548	0.0417	1.0000	0.6077	0.0	1.0000	0.6077	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
357	P17948	P17948_VGFR1_HUMAN_KD1	FLT1	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0642	0.0688	0.0597	0.0618	...	0.0510	0.0677	0.0677	0.0380	0.0000	0.0000	1.0	0.0000	0.0000	1.0
358	P35968	P35968_VGFR2_HUMAN_KD1	KDR	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0634	0.0672	0.0556	0.0517	...	0.0338	0.0300	0.0300	0.0292	0.0000	0.0000	1.0	0.0000	0.0000	1.0
359	P35916	P35916_VGFR3_HUMAN_KD1	FLT4	TK	FGF and VEGF receptors	FGF and VEGF receptors	0.0457	0.0531	0.0488	0.0553	...	0.0497	0.0445	0.0445	0.0500	0.0000	0.0000	1.0	0.0000	0.0000	1.0
360	P07947	P07947_YES_HUMAN_KD1	YES1	TK	SRC	SRC	0.0677	0.0571	0.0537	0.0530	...	0.0492	0.0371	0.0371	0.0467	0.0000	0.0000	1.0	0.0000	0.0000	1.0
361	P43403	P43403_ZAP70_HUMAN_KD1	ZAP70	TK	SYK and FAK	SYK and FAK	0.0602	0.0880	0.0623	0.0496	...	0.0558	0.0440	0.0440	0.0318	0.0000	0.0000	1.0	0.0000	0.0000	1.0

362 rows × 219 columns